366 results on '"Dean, Sarah"'
Search Results
2. Harm Mitigation in Recommender Systems under User Preference Dynamics
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Chee, Jerry, Kalyanaraman, Shankar, Ernala, Sindhu Kiranmai, Weinsberg, Udi, Dean, Sarah, and Ioannidis, Stratis
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Computer Science - Information Retrieval ,Computer Science - Computers and Society ,Computer Science - Machine Learning - Abstract
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish conditions under which the user profile dynamics have a stationary point, and propose algorithms for finding an optimal recommendation policy at stationarity. We experiment on a semi-synthetic movie recommendation setting initialized with real data and observe that our policies outperform baselines at simultaneously maximizing CTR and mitigating harm., Comment: Recommender Systems; Harm Mitigation; Amplification; User Preference Modeling
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- 2024
3. Random Features Approximation for Control-Affine Systems
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Kazemian, Kimia, Sattar, Yahya, and Dean, Sarah
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Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
Modern data-driven control applications call for flexible nonlinear models that are amenable to principled controller synthesis and realtime feedback. Many nonlinear dynamical systems of interest are control affine. We propose two novel classes of nonlinear feature representations which capture control affine structure while allowing for arbitrary complexity in the state dependence. Our methods make use of random features (RF) approximations, inheriting the expressiveness of kernel methods at a lower computational cost. We formalize the representational capabilities of our methods by showing their relationship to the Affine Dot Product (ADP) kernel proposed by Casta\~neda et al. (2021) and a novel Affine Dense (AD) kernel that we introduce. We further illustrate the utility by presenting a case study of data-driven optimization-based control using control certificate functions (CCF). Simulation experiments on a double pendulum empirically demonstrate the advantages of our methods., Comment: 25 pages, 3 figures
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- 2024
4. Learning from Streaming Data when Users Choose
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Su, Jinyan and Dean, Sarah
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
In digital markets comprised of many competing services, each user chooses between multiple service providers according to their preferences, and the chosen service makes use of the user data to incrementally improve its model. The service providers' models influence which service the user will choose at the next time step, and the user's choice, in return, influences the model update, leading to a feedback loop. In this paper, we formalize the above dynamics and develop a simple and efficient decentralized algorithm to locally minimize the overall user loss. Theoretically, we show that our algorithm asymptotically converges to stationary points of of the overall loss almost surely. We also experimentally demonstrate the utility of our algorithm with real world data., Comment: Accepted by ICML24
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- 2024
5. To Ask or Not To Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding
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Banerjee, Rohan, Jenamani, Rajat Kumar, Vasudev, Sidharth, Nanavati, Amal, Dean, Sarah, and Bhattacharjee, Tapomayukh
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Computer Science - Robotics - Abstract
Robot-assisted bite acquisition involves picking up food items that vary in their shape, compliance, size, and texture. A fully autonomous strategy for bite acquisition is unlikely to efficiently generalize to this wide variety of food items. We propose to leverage the presence of the care recipient to provide feedback when the system encounters novel food items. However, repeatedly asking for help imposes cognitive workload on the user. In this work, we formulate human-in-the-loop bite acquisition within a contextual bandit framework and propose a novel method, LinUCB-QG, that selectively asks for help. This method leverages a predictive model of cognitive workload in response to different types and timings of queries, learned using data from 89 participants collected in an online user study. We demonstrate that this method enhances the balance between task performance and cognitive workload compared to autonomous and querying baselines, through experiments in a food dataset-based simulator and a user study with 18 participants without mobility limitations., Comment: The second and third authors contributed equally. The last two authors advised equally
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- 2024
6. Metasurface-based Toroidal Lenslet Array Design for Addressing Laser Guide Star Elongation
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Munro, Josephine, Dean, Sarah E., Li, Neuton, Vaughn, Israel J., Kruse, Andrew W., Travouillon, Tony, Neshev, Dragomir N., Sharp, Robert, and Sukhorukov, Andrey A.
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Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The Giant Magellan Telescope will use laser tomography adaptive optics to correct for atmospheric turbulence using artificial guide stars created in the sodium layer of the atmosphere (altitude ~95km). The sodium layer has appreciable thickness (~11km) and this results in the laser guide star being an elongated cylinder shape. Wavefront sensing with a Shack-Hartmann is challenging, as subapertures located further away from the laser launch position image an increasingly elongated perspective of the laser guide star. Large detectors can be used to adequately pack and sample the images on the detector, however, this increases readout noise and limits the design space available for the wavefront sensor. To tackle this challenge, we propose an original solution based on nano-engineered meta-optics tailored to produce a spatially varying anamorphic image scale compression. We present meta-lenslet array designs that can deliver ~100% of the full anamorphic image size reduction required for focal lengths down to 8mm, and greater than 50% image size reduction for focal lengths down to 2mm. This will allow greatly improved sampling of the available information across the whole wavefront sensor, while still being a viable design within the limits of current-generation fabrication facilities.
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- 2024
7. Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
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Dean, Sarah, Dong, Evan, Jagadeesan, Meena, and Leqi, Liu
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Computer Science - Machine Learning ,Computer Science - Computers and Society ,Computer Science - Computer Science and Game Theory ,Computer Science - Information Retrieval - Abstract
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In this position paper, we argue for the development of formal interaction models which mathematically specify how AI and users shape one another. Formal interaction models can be leveraged to (1) specify interactions for implementation, (2) monitor interactions through empirical analysis, (3) anticipate societal impacts via counterfactual analysis, and (4) control societal impacts via interventions. The design space of formal interaction models is vast, and model design requires careful consideration of factors such as style, granularity, mathematical complexity, and measurability. Using content recommender systems as a case study, we critically examine the nascent literature of formal interaction models with respect to these use-cases and design axes. More broadly, we call for the community to leverage formal interaction models when designing, evaluating, or auditing any AI system which interacts with users.
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- 2024
8. Strategic Usage in a Multi-Learner Setting
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Shekhtman, Eliot and Dean, Sarah
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Computer Science - Machine Learning ,Computer Science - Computer Science and Game Theory ,91A10 - Abstract
Real-world systems often involve some pool of users choosing between a set of services. With the increase in popularity of online learning algorithms, these services can now self-optimize, leveraging data collected on users to maximize some reward such as service quality. On the flipside, users may strategically choose which services to use in order to pursue their own reward functions, in the process wielding power over which services can see and use their data. Extensive prior research has been conducted on the effects of strategic users in single-service settings, with strategic behavior manifesting in the manipulation of observable features to achieve a desired classification; however, this can often be costly or unattainable for users and fails to capture the full behavior of multi-service dynamic systems. As such, we analyze a setting in which strategic users choose among several available services in order to pursue positive classifications, while services seek to minimize loss functions on their observations. We focus our analysis on realizable settings, and show that naive retraining can still lead to oscillation even if all users are observed at different times; however, if this retraining uses memory of past observations, convergent behavior can be guaranteed for certain loss function classes. We provide results obtained from synthetic and real-world data to empirically validate our theoretical findings., Comment: 18 pages, 9 figures
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- 2024
9. “So that’s why I found PrEP to be safest way to protect yourself”: exploring IPV experiences and impact on HIV prevention among pregnant and postpartum women in Cape Town, South Africa
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Miller, Amanda P, Dean, Sarah Schoetz, Court, Lara, Mvududu, Rufaro, Mashele, Nyiko, Wara, Nafisa J, Myer, Landon, Shoptaw, Steven, and Davey, Dvora L Joseph
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Public Health ,Health Sciences ,Violence Research ,Clinical Research ,Mental Health ,Alcoholism ,Alcohol Use and Health ,Prevention ,Violence Against Women ,Substance Misuse ,Pediatric ,HIV/AIDS ,Behavioral and Social Science ,Prevention of disease and conditions ,and promotion of well-being ,3.1 Primary prevention interventions to modify behaviours or promote wellbeing ,Infection ,Reproductive health and childbirth ,Gender Equality ,Peace ,Justice and Strong Institutions ,Good Health and Well Being ,Female ,Humans ,Male ,Pregnancy ,South Africa ,Pregnant Women ,Intimate Partner Violence ,HIV Infections ,Postpartum Period ,Intimate partner violence ,HIV ,Alcohol use ,PrEP ,Public Health and Health Services ,Epidemiology ,Health services and systems ,Public health - Abstract
Intimate partner violence (IPV) occurs at alarmingly high rates towards pregnant women in South Africa. Experiences of emotional, physical, and sexual IPV in pregnancy can adversely impact the health and safety of mother and fetus. Furthermore, IPV is associated with increased risk of HIV, exacerbating the public health impact of violence among pregnant women in this HIV endemic setting. In-depth understanding of cultural and contextual drivers of experiences of IPV is a critical precursor to development of interventions effectively addressing this issue among pregnant women in South Africa. The present study examines factors contributing to IPV among pregnant women to identify potential points of intervention. We conducted twenty in-depth interviews with postpartum women who used oral pre-exposure prophylaxis (PrEP) in pregnancy and reported recent experiences of IPV and/or ongoing alcohol use in a township near Cape Town, South Africa that experiences a heavy burden of both HIV and IPV. Interpretive thematic analysis was used. Several patterns of IPV during pregnancy were identified and violence was frequently described as co-occurring with male partner alcohol use. A majority of women referenced oral PrEP as their preferred method for HIV prevention, highlighting the agency and discretion it provided as beneficial attributes for women experiencing IPV. Fear of judgement from peers for remaining with an abusive partner and a lack of clear community messaging around IPV were identified as barriers to disclosure and support-seeking. Addressing the lack of social support received by women experiencing IPV during pregnancy in South Africa is essential to comprehensive IPV programming.
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- 2024
10. Pregnancy outcomes following self-reported and objective-measured exposure to oral preexposure prophylaxis in South Africa.
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Davey, Dvora, Nyemba, Dorothy, Mvududu, Rufaro, Mashele, Nyiko, Johnson, Leigh, Bekker, Linda-Gail, Dean, Sarah, Bheemraj, Kalisha, Coates, Thomas, and Myer, Landon
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Female ,Humans ,Infant ,Newborn ,Pregnancy ,Pregnancy Outcome ,Anti-HIV Agents ,HIV Infections ,Premature Birth ,South Africa ,Birth Weight ,Self Report ,Emtricitabine ,Abortion ,Spontaneous ,Pre-Exposure Prophylaxis - Abstract
OBJECTIVE: To compare pregnancy outcomes using self-reported and objective levels of intracellular tenofovir diphosphate (TFV-DP) in pregnant women using preexposure prophylaxis (PrEP). DESIGN: We enrolled pregnant women >15 years without HIV at first antenatal care visit in an observational cohort study to compare pregnancy outcomes by PrEP use. METHODS: Exposure defined as: any PrEP use [tenofovir disoproxil and emtricitabine (TDF/FTC]) prescription + reported taking PrEP], or objectively-measured TFV-DP in dried blood spots in PrEP-using pregnant women. The primary outcome was a composite of pregnancy loss, preterm birth (
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- 2024
11. Initializing Services in Interactive ML Systems for Diverse Users
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Bose, Avinandan, Curmei, Mihaela, Jiang, Daniel L., Morgenstern, Jamie, Dean, Sarah, Ratliff, Lillian J., and Fazel, Maryam
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
This paper studies ML systems that interactively learn from users across multiple subpopulations with heterogeneous data distributions. The primary objective is to provide specialized services for different user groups while also predicting user preferences. Once the users select a service based on how well the service anticipated their preference, the services subsequently adapt and refine themselves based on the user data they accumulate, resulting in an iterative, alternating minimization process between users and services (learning dynamics). Employing such tailored approaches has two main challenges: (i) Unknown user preferences: Typically, data on user preferences are unavailable without interaction, and uniform data collection across a large and diverse user base can be prohibitively expensive. (ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima. The final outcome of the aforementioned learning dynamics is thus strongly influenced by the initial set of services offered to users, and is not guaranteed to be close to the globally optimal outcome. In this work, we propose a randomized algorithm to adaptively select very few users to collect preference data from, while simultaneously initializing a set of services. We prove that under mild assumptions on the loss functions, the expected total loss achieved by the algorithm right after initialization is within a factor of the globally optimal total loss with complete user preference data, and this factor scales only logarithmically in the number of services. Our theory is complemented by experiments on real as well as semi-synthetic datasets.
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- 2023
12. Ranking with Long-Term Constraints
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Brantley, Kianté, Fang, Zhichong, Dean, Sarah, and Joachims, Thorsten
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Computer Science - Information Retrieval - Abstract
The feedback that users provide through their choices (e.g., clicks, purchases) is one of the most common types of data readily available for training search and recommendation algorithms. However, myopically training systems based on choice data may only improve short-term engagement, but not the long-term sustainability of the platform and the long-term benefits to its users, content providers, and other stakeholders. In this paper, we thus develop a new framework in which decision makers (e.g., platform operators, regulators, users) can express long-term goals for the behavior of the platform (e.g., fairness, revenue distribution, legal requirements). These goals take the form of exposure or impact targets that go well beyond individual sessions, and we provide new control-based algorithms to achieve these goals. In particular, the controllers are designed to achieve the stated long-term goals with minimum impact on short-term engagement. Beyond the principled theoretical derivation of the controllers, we evaluate the algorithms on both synthetic and real-world data. While all controllers perform well, we find that they provide interesting trade-offs in efficiency, robustness, and the ability to plan ahead.
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- 2023
13. Pelvic floor muscle training for female urinary incontinence: development of a programme theory from a longitudinal qualitative case study
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Bugge, Carol, Hay-Smith, Jean, Hagen, Suzanne, Grant, Aileen, Taylor, Anne, and Dean, Sarah
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- 2024
- Full Text
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14. Integration of HIV pre-exposure prophylaxis (PrEP) services for pregnant and breastfeeding women in eight primary care clinics: results of an implementation science study
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Nelson, Aurelie, Bheemraj, Kalisha, Dean, Sarah Schoetz, de Voux, Alex, Hlatshwayo, Lerato, Mvududu, Rufaro, Berkowitz, Natacha, Neumuller, Caroline, Jacobs, Shahida, Fourie, Stephanie, Coates, Thomas, Gail-Bekker, Linda, Myer, Landon, and Davey, Dvora Joseph
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- 2024
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15. Understanding how therapeutic exercise prescription changes outcomes important to patients with persistent non-specific low back pain: a realist review protocol
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Wood, Lianne, Booth, Vicky, Dean, Sarah, Foster, Nadine E., Hayden, Jill A., and Booth, Andrew
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- 2024
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16. Decision-aid or Controller? Steering Human Decision Makers with Algorithms
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Xu, Ruqing and Dean, Sarah
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Computer Science - Artificial Intelligence ,Computer Science - Computers and Society ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning - Abstract
Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We first consider fixed human decision functions which map observable features and the algorithm's recommendations to final decisions. We characterize the conditions under which perfect control over final decisions is attainable. Under fairly general assumptions, the parameters of the human decision function can be identified from past interactions between the algorithm and the human decision maker, even when the algorithm was constrained to make truthful recommendations. We then consider a decision maker who is aware of the algorithm's manipulation and responds strategically. By posing the setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we show that all equilibria are partition equilibria where only coarse information is shared: the algorithm recommends an interval containing the ideal decision. We discuss the potential applications of such algorithms and their social implications.
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- 2023
17. Cross-Dataset Propensity Estimation for Debiasing Recommender Systems
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Li, Fengyu and Dean, Sarah
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Computer Science - Information Retrieval ,Computer Science - Machine Learning ,Statistics - Methodology - Abstract
Datasets for training recommender systems are often subject to distribution shift induced by users' and recommenders' selection biases. In this paper, we study the impact of selection bias on datasets with different quantization. We then leverage two differently quantized datasets from different source distributions to mitigate distribution shift by applying the inverse probability scoring method from causal inference. Empirically, our approach gains significant performance improvement over single-dataset methods and alternative ways of combining two datasets., Comment: In Workshop on Distribution Shifts, 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
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- 2022
18. Perception-Based Sampled-Data Optimization of Dynamical Systems
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Cothren, Liliaokeawawa, Bianchin, Gianluca, Dean, Sarah, and Dall'Anese, Emiliano
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Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
Motivated by perception-based control problems in autonomous systems, this paper addresses the problem of developing feedback controllers to regulate the inputs and the states of a dynamical system to optimal solutions of an optimization problem when one has no access to exact measurements of the system states. In particular, we consider the case where the states need to be estimated from high-dimensional sensory data received only at discrete time intervals. We develop a sampled-data feedback controller that is based on adaptations of a projected gradient descent method, and that includes neural networks as integral components to estimate the state of the system from perceptual information. We derive sufficient conditions to guarantee (local) input-to-state stability of the control loop. Moreover, we show that the interconnected system tracks the solution trajectory of the underlying optimization problem up to an error that depends on the approximation errors of the neural network and on the time-variability of the optimization problem; the latter originates from time-varying safety and performance objectives, input constraints, and unknown disturbances. As a representative application, we illustrate our results with numerical simulations for vision-based autonomous driving., Comment: This is an extended version of the paper accepted to IFAC World Congress 2023 for publication, containing proofs, and recently updated to address a typo in Assumption 3
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- 2022
19. Online Convex Optimization with Unbounded Memory
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Kumar, Raunak, Dean, Sarah, and Kleinberg, Robert
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Computer Science - Machine Learning ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
Online convex optimization (OCO) is a widely used framework in online learning. In each round, the learner chooses a decision in a convex set and an adversary chooses a convex loss function, and then the learner suffers the loss associated with their current decision. However, in many applications the learner's loss depends not only on the current decision but on the entire history of decisions until that point. The OCO framework and its existing generalizations do not capture this, and they can only be applied to many settings of interest after a long series of approximation arguments. They also leave open the question of whether the dependence on memory is tight because there are no non-trivial lower bounds. In this work we introduce a generalization of the OCO framework, "Online Convex Optimization with Unbounded Memory", that captures long-term dependence on past decisions. We introduce the notion of $p$-effective memory capacity, $H_p$, that quantifies the maximum influence of past decisions on present losses. We prove an $O(\sqrt{H_p T})$ upper bound on the policy regret and a matching (worst-case) lower bound. As a special case, we prove the first non-trivial lower bound for OCO with finite memory \citep{anavaHM2015online}, which could be of independent interest, and also improve existing upper bounds. We demonstrate the broad applicability of our framework by using it to derive regret bounds, and to improve and simplify existing regret bound derivations, for a variety of online learning problems including online linear control and an online variant of performative prediction., Comment: Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
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- 2022
20. A Qualitative Exploration of Patient and Staff Experiences of the Receipt and Delivery of Specialist Weight Management Services in the UK
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Watkins, Ross, Swancutt, Dawn, Alexander, Mia, Moghadam, Shokraneh, Perry, Steve, Dean, Sarah, Sheaff, Rod, Pinkney, Jonathan, Tarrant, Mark, and Lloyd, Jenny
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- 2023
- Full Text
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21. Modeling Content Creator Incentives on Algorithm-Curated Platforms
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Hron, Jiri, Krauth, Karl, Jordan, Michael I., Kilbertus, Niki, and Dean, Sarah
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Computer Science - Computer Science and Game Theory ,Computer Science - Computers and Society ,Computer Science - Information Retrieval ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by algorithms, including modern factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices, e.g., non-negative vs. unconstrained factorization, significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models, like exposure games, for an (ex-ante) pre-deployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups., Comment: presented at ICLR 2023 (top 5%)
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- 2022
22. Emergent specialization from participation dynamics and multi-learner retraining
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Dean, Sarah, Curmei, Mihaela, Ratliff, Lillian J., Morgenstern, Jamie, and Fazel, Maryam
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Computer Science - Machine Learning ,Computer Science - Computer Science and Game Theory ,Statistics - Machine Learning - Abstract
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in turn affects the way users may interact with the system. For example, people may choose to use a service only for tasks that already works well, or they may choose to switch to a different service. These adaptations influence the ability of a system to learn about a population of users and tasks in order to improve its performance broadly. In this work, we analyze a class of such dynamics -- where users allocate their participation amongst services to reduce the individual risk they experience, and services update their model parameters to reduce the service's risk on their current user population. We refer to these dynamics as \emph{risk-reducing}, which cover a broad class of common model updates including gradient descent and multiplicative weights. For this general class of dynamics, we show that asymptotically stable equilibria are always segmented, with sub-populations allocated to a single learner. Under mild assumptions, the utilitarian social optimum is a stable equilibrium. In contrast to previous work, which shows that repeated risk minimization can result in (Hashimoto et al., 2018; Miller et al., 2021), we find that repeated myopic updates with multiple learners lead to better outcomes. We illustrate the phenomena via a simulated example initialized from real data., Comment: AISTATS 2024
- Published
- 2022
23. Preference Dynamics Under Personalized Recommendations
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Dean, Sarah and Morgenstern, Jamie
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Computer Science - Machine Learning ,Computer Science - Computer Science and Game Theory ,Computer Science - Information Retrieval ,Computer Science - Social and Information Networks ,Electrical Engineering and Systems Science - Systems and Control ,Statistics - Machine Learning - Abstract
Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that individuals' preferences are directly shaped by what content they see -- radicalization, rabbit holes, polarization, and boredom are all example phenomena of preferences affected by content. Polarization in particular can occur even in ecosystems with "mass media," where no personalization takes place, as recently explored in a natural model of preference dynamics by~\citet{hkazla2019geometric} and~\citet{gaitonde2021polarization}. If all users' preferences are drawn towards content they already like, or are repelled from content they already dislike, uniform consumption of media leads to a population of heterogeneous preferences converging towards only two poles. In this work, we explore whether some phenomenon akin to polarization occurs when users receive \emph{personalized} content recommendations. We use a similar model of preference dynamics, where an individual's preferences move towards content the consume and enjoy, and away from content they consume and dislike. We show that standard user reward maximization is an almost trivial goal in such an environment (a large class of simple algorithms will achieve only constant regret). A more interesting objective, then, is to understand under what conditions a recommendation algorithm can ensure stationarity of user's preferences. We show how to design a content recommendations which can achieve approximate stationarity, under mild conditions on the set of available content, when a user's preferences are known, and how one can learn enough about a user's preferences to implement such a strategy even when user preferences are initially unknown., Comment: EC 2022
- Published
- 2022
24. Reward Reports for Reinforcement Learning
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Gilbert, Thomas Krendl, Lambert, Nathan, Dean, Sarah, Zick, Tom, and Snoswell, Aaron
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Computer Science - Machine Learning ,Computer Science - Computers and Society - Abstract
Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation about these complexities. However, these developments have been grounded in a static ML paradigm, leaving the role of feedback and post-deployment performance unexamined. Meanwhile, recent work in reinforcement learning has shown that the effects of feedback and optimization objectives on system behavior can be wide-ranging and unpredictable. In this paper we sketch a framework for documenting deployed and iteratively updated learning systems, which we call Reward Reports. Taking inspiration from various contributions to the technical literature on reinforcement learning, we outline Reward Reports as living documents that track updates to design choices and assumptions behind what a particular automated system is optimizing for. They are intended to track dynamic phenomena arising from system deployment, rather than merely static properties of models or data. After presenting the elements of a Reward Report, we discuss a concrete example: Meta's BlenderBot 3 chatbot. Several others for game-playing (DeepMind's MuZero), content recommendation (MovieLens), and traffic control (Project Flow) are included in the appendix.
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- 2022
25. Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
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Gilbert, Thomas Krendl, Dean, Sarah, Zick, Tom, and Lambert, Nathan
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Computer Science - Machine Learning ,Computer Science - Computers and Society - Abstract
In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence. This places RL practitioners in a position to design systems that have never existed before and lack prior documentation in law and policy. Public agencies could intervene on complex dynamics that were previously too opaque to deliberate about, and long-held policy ambitions would finally be made tractable. In this whitepaper we illustrate this potential and how it might be technically enacted in the domains of energy infrastructure, social media recommender systems, and transportation. Alongside these unprecedented interventions come new forms of risk that exacerbate the harms already generated by standard machine learning tools. We correspondingly present a new typology of risks arising from RL design choices, falling under four categories: scoping the horizon, defining rewards, pruning information, and training multiple agents. Rather than allowing RL systems to unilaterally reshape human domains, policymakers need new mechanisms for the rule of reason, foreseeability, and interoperability that match the risks these systems pose. We argue that criteria for these choices may be drawn from emerging subfields within antitrust, tort, and administrative law. It will then be possible for courts, federal and state agencies, and non-governmental organizations to play more active roles in RL specification and evaluation. Building on the "model cards" and "datasheets" frameworks proposed by Mitchell et al. and Gebru et al., we argue the need for Reward Reports for AI systems. Reward Reports are living documents for proposed RL deployments that demarcate design choices., Comment: 60 pages
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- 2022
26. Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts
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Krauth, Stefanie J., Steell, Lewis, Ahmed, Sayem, McIntosh, Emma, Dibben, Grace O., Hanlon, Peter, Lewsey, Jim, Nicholl, Barbara I., McAllister, David A., Smith, Susan M., Evans, Rachael, Ahmed, Zahira, Dean, Sarah, Greaves, Colin, Barber, Shaun, Doherty, Patrick, Gardiner, Nikki, Ibbotson, Tracy, Jolly, Kate, Ormandy, Paula, Simpson, Sharon A., Taylor, Rod S., Singh, Sally J., Mair, Frances S., and Jani, Bhautesh Dinesh
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- 2024
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27. Evidence for exercise-based interventions across 45 different long-term conditions: an overview of systematic reviews
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Begum, Samina, DeBarros, Clara, Davies, Firoza, Sterniczuk, Kamil, Kumar, Rashmi, Longley, Rebecca, Freeman, Andrew, Lalseta, Jagruti, Ashby, Paul, Van Grieken, Marc, Grace Elder, Dorothy, Dibben, Grace O., Gardiner, Lucy, Young, Hannah M.L., Wells, Valerie, Evans, Rachael A., Ahmed, Zahira, Barber, Shaun, Dean, Sarah, Doherty, Patrick, Gardiner, Nikki, Greaves, Colin, Ibbotson, Tracy, Jani, Bhautesh D., Jolly, Kate, Mair, Frances S., McIntosh, Emma, Ormandy, Paula, Simpson, Sharon A., Ahmed, Sayem, Krauth, Stefanie J., Steell, Lewis, Singh, Sally J., and Taylor, Rod S.
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- 2024
- Full Text
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28. Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
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Curmei, Mihaela, Dean, Sarah, and Recht, Benjamin
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Computer Science - Information Retrieval ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications. This framework allows us to compute an upper bound on the likelihood of recommendation with minimal assumptions about user behavior. Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy. We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly., Comment: to appear ICML 2021
- Published
- 2021
29. Axes for Sociotechnical Inquiry in AI Research
- Author
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Dean, Sarah, Gilbert, Thomas Krendl, Lambert, Nathan, and Zick, Tom
- Subjects
Computer Science - Computers and Society ,Computer Science - Artificial Intelligence - Abstract
The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for external investigation, comparison, and cross-pollination are lacking. In this paper, we propose four directions for inquiry into new and evolving areas of technological development: value--what progress and direction does a field promote, optimization--how the defined system within a problem formulation relates to broader dynamics, consensus--how agreement is achieved and who is included in building it, and failure--what methods are pursued when the problem specification is found wanting. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology., Comment: 9 pages, 1 figure
- Published
- 2021
- Full Text
- View/download PDF
30. Inflammation, endothelial injury, and the acute respiratory distress syndrome after out-of-hospital cardiac arrest
- Author
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Katsandres, Sarah C., Hall, Jane, Danielson, Kyle, Sakr, Sana, Dean, Sarah G., Carlbom, David J., Wurfel, Mark M., Bhatraju, Pavan K., Hippensteel, Joseph A., Schmidt, Eric P., Oshima, Kaori, Counts, Catherine R., Sayre, Michael R., Henning, Daniel J., and Johnson, Nicholas J.
- Published
- 2024
- Full Text
- View/download PDF
31. AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks
- Author
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Andrus, McKane, Dean, Sarah, Gilbert, Thomas Krendl, Lambert, Nathan, and Zick, Tom
- Subjects
Computer Science - Computers and Society ,Computer Science - Artificial Intelligence - Abstract
Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the graduate level remain largely unexplored. This may be due to the conflicting ways through which distinct Artificial Intelligence (AI) research tracks conceive of their interface with social contexts. In this paper we track the historical emergence of sociotechnical inquiry in three distinct subfields of AI research: AI Safety, Fair Machine Learning (Fair ML) and Human-in-the-Loop (HIL) Autonomy. We show that for each subfield, perceptions of PIT stem from the particular dangers faced by past integration of technical systems within a normative social order. We further interrogate how these histories dictate the response of each subfield to conceptual traps, as defined in the Science and Technology Studies literature. Finally, through a comparative analysis of these currently siloed fields, we present a roadmap for a unified approach to sociotechnical graduate pedagogy in AI., Comment: 8 Pages
- Published
- 2021
32. Patient and clinician perceptions of asthma education and management in resistant asthma : a qualitative study
- Author
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Dean, Sarah G
- Published
- 2008
33. Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty
- Author
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Taylor, Andrew J., Dorobantu, Victor D., Dean, Sarah, Recht, Benjamin, Yue, Yisong, and Ames, Aaron D.
- Subjects
Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Robotics - Abstract
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations., Comment: 8 pages, 2 figures, submitted to Conference on Decision & Control (CDC) 2021
- Published
- 2020
34. Do Offline Metrics Predict Online Performance in Recommender Systems?
- Author
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Krauth, Karl, Dean, Sarah, Zhao, Alex, Guo, Wenshuo, Curmei, Mihaela, Recht, Benjamin, and Jordan, Michael I.
- Subjects
Computer Science - Information Retrieval ,Computer Science - Machine Learning - Abstract
Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences change. However, experimenting in production systems with real user dynamics is often infeasible, and existing simulation-based approaches have limited scale. As a result, many state-of-the-art algorithms are designed to solve supervised learning problems, and progress is judged only by offline metrics. In this work we investigate the extent to which offline metrics predict online performance by evaluating eleven recommenders across six controlled simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. However, improvements in offline metrics lead to diminishing returns in online performance. Furthermore, we observe that the ranking of recommenders varies depending on the amount of initial offline data available. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm. We provide the environments and recommenders described in this paper as Reclab: an extensible ready-to-use simulation framework at https://github.com/berkeley-reclab/RecLab.
- Published
- 2020
35. Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
- Author
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Dean, Sarah, Taylor, Andrew J., Cosner, Ryan K., Recht, Benjamin, and Ames, Aaron D.
- Subjects
Electrical Engineering and Systems Science - Systems and Control ,Computer Science - Machine Learning ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to error in state estimates that degrades these guarantees. In this paper, we seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety in the presence of measurement model uncertainty. We define the notion of a Measurement-Robust Control Barrier Function (MR-CBF) as a tool for determining safe control inputs when facing measurement model uncertainty. Furthermore, MR-CBFs are used to inform sampling methodologies for learning-based perception systems and quantify tolerable error in the resulting learned models. We demonstrate the efficacy of MR-CBFs in achieving safety with measurement model uncertainty on a simulated Segway system.
- Published
- 2020
36. Certainty Equivalent Perception-Based Control
- Author
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Dean, Sarah and Recht, Benjamin
- Subjects
Computer Science - Machine Learning ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
In order to certify performance and safety, feedback control requires precise characterization of sensor errors. In this paper, we provide guarantees on such feedback systems when sensors are characterized by solving a supervised learning problem. We show a uniform error bound on nonparametric kernel regression under a dynamically-achievable dense sampling scheme. This allows for a finite-time convergence rate on the sub-optimality of using the regressor in closed-loop for waypoint tracking. We demonstrate our results in simulation with simplified unmanned aerial vehicle and autonomous driving examples., Comment: to appear at L4DC 2021
- Published
- 2020
37. Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
- Author
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Rolf, Esther, Simchowitz, Max, Dean, Sarah, Liu, Lydia T., Björkegren, Daniel, Hardt, Moritz, and Blumenstock, Joshua
- Subjects
Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts -- online content recommendation and sustainable abalone fisheries -- to underscore the applicability of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.
- Published
- 2020
38. Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information
- Author
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Dean, Sarah, Rich, Sarah, and Recht, Benjamin
- Subjects
Computer Science - Machine Learning ,Computer Science - Information Retrieval ,Statistics - Machine Learning - Abstract
Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-$N$ linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset., Comment: appeared at FAccT '20
- Published
- 2019
- Full Text
- View/download PDF
39. Robust Guarantees for Perception-Based Control
- Author
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Dean, Sarah, Matni, Nikolai, Recht, Benjamin, and Ye, Vickie
- Subjects
Mathematics - Optimization and Control ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Motivated by vision-based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as vehicle position, is extracted from complex and nonlinear data, such as a camera image. Our approach is to use a learned perception map that predicts some linear function of the state and to design a corresponding safe set and robust controller for the closed loop system with this sensing scheme. We show that under suitable smoothness assumptions on both the perception map and the generative model relating state to complex and nonlinear data, parameters of the safe set can be learned via appropriately dense sampling of the state space. We then prove that the resulting perception-control loop has favorable generalization properties. We illustrate the usefulness of our approach on a synthetic example and on the self-driving car simulation platform CARLA., Comment: This revision includes reframing the local generalization problem, with relaxed the assumptions so that the robust problem depends on a local slope bound rather than a Lipschitz constant, and provide a method for learning the slope bound from data. We also include additional experiments with a CNN perception module
- Published
- 2019
40. High-throughput fluorescence microscopy using multi-frame motion deblurring.
- Author
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Phillips, Zachary, Dean, Sarah, Recht, Benjamin, and Waller, Laura
- Abstract
We demonstrate multi-frame motion deblurring for gigapixel wide-field fluorescence microscopy using fast slide scanning with coded illumination. Our method illuminates the sample with multiple pulses within each exposure, in order to introduce structured motion blur. By deconvolving this known motion sequence from the set of acquired measurements, we recover the object with up to 10× higher SNR than when illuminated with a single pulse (strobed illumination), while performing acquisition at 5× higher frame-rate than a comparable stop-and-stare method. Our coded illumination sequence is optimized to maximize the reconstruction SNR. We also derive a framework for determining when coded illumination is SNR-optimal in terms of system parameters such as source illuminance, noise, and motion stage specifications. This helps system designers to choose the ideal technique for high-throughput microscopy of very large samples.
- Published
- 2020
41. Perception-Based Sampled-Data Optimization of Dynamical Systems
- Author
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Cothren, Liliaokeawawa, Bianchin, Gianluca, Dean, Sarah, and Dall'Anese, Emiliano
- Published
- 2023
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42. Deep learning to predict rapid progression of Alzheimer’s disease from pooled clinical trials: A retrospective study
- Author
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Ma, Xiaotian, primary, Shyer, Madison, additional, Harris, Kristofer, additional, Wang, Dulin, additional, Hsu, Yu-Chun, additional, Farrell, Christine, additional, Goodwin, Nathan, additional, Anjum, Sahar, additional, Bukhbinder, Avram S., additional, Dean, Sarah, additional, Khan, Tanveer, additional, Hunter, David, additional, Schulz, Paul E., additional, Jiang, Xiaoqian, additional, and Kim, Yejin, additional
- Published
- 2024
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- View/download PDF
43. Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (PERFORM): protocol for a randomised feasibility trial
- Author
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Simpson, Sharon Anne, primary, Evans, Rachael A, additional, Gilbert, Hannah Rosemary, additional, Branson, Amy, additional, Barber, Shaun, additional, McIntosh, Emma, additional, Ahmed, Zahira, additional, Dean, Sarah Gerard, additional, Doherty, Patrick Joseph, additional, Gardiner, Nikki, additional, Greaves, Colin, additional, Daw, Paulina, additional, Ibbotson, Tracy, additional, Jani, Bhautesh, additional, Jolly, Kate, additional, Mair, Frances, additional, Ormandy, Paula, additional, Smith, Susan, additional, Singh, Sally J, additional, and Taylor, Rod, additional
- Published
- 2024
- Full Text
- View/download PDF
44. Evidence for exercise-based interventions across 45 different long-term conditions: an overview of systematic reviews
- Author
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Dibben, Grace O., primary, Gardiner, Lucy, additional, Young, Hannah M.L., additional, Wells, Valerie, additional, Evans, Rachael A., additional, Ahmed, Zahira, additional, Barber, Shaun, additional, Dean, Sarah, additional, Doherty, Patrick, additional, Gardiner, Nikki, additional, Greaves, Colin, additional, Ibbotson, Tracy, additional, Jani, Bhautesh D., additional, Jolly, Kate, additional, Mair, Frances S., additional, McIntosh, Emma, additional, Ormandy, Paula, additional, Simpson, Sharon A., additional, Ahmed, Sayem, additional, Krauth, Stefanie J., additional, Steell, Lewis, additional, Singh, Sally J., additional, Taylor, Rod S., additional, Begum, Samina, additional, DeBarros, Clara, additional, Davies, Firoza, additional, Sterniczuk, Kamil, additional, Kumar, Rashmi, additional, Longley, Rebecca, additional, Freeman, Andrew, additional, Lalseta, Jagruti, additional, Ashby, Paul, additional, Van Grieken, Marc, additional, and Grace Elder, Dorothy, additional
- Published
- 2024
- Full Text
- View/download PDF
45. Safely Learning to Control the Constrained Linear Quadratic Regulator
- Author
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Dean, Sarah, Tu, Stephen, Matni, Nikolai, and Recht, Benjamin
- Subjects
Mathematics - Optimization and Control ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of state and input constraints. This framework involves a novel method for synthesizing robust constraint-satisfying feedback controllers, leveraging newly developed tools from system level synthesis. We connect statistical results with cost sub-optimality bounds to give non-asymptotic guarantees on both estimation and controller performance.
- Published
- 2018
46. A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
- Author
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Dobbe, Roel, Dean, Sarah, Gilbert, Thomas, and Kohli, Nitin
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Systems and Control ,Mathematics - Dynamical Systems ,Statistics - Machine Learning - Abstract
Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical and emergent biases, which often arise as context-specific artifacts of implementation. This position paper interprets technical bias as an epistemological problem and emergent bias as a dynamical feedback phenomenon. In order to stimulate debate on how to change machine learning practice to effectively address these issues, we explore this broader view on bias, stress the need to reflect on epistemology, and point to value-sensitive design methodologies to revisit the design and implementation process of automated decision-making systems., Comment: Presented at the 2018 Workshop on Fairness, Accountability and Transparency in Machine Learning during ICML 2018, Stockholm, Sweden
- Published
- 2018
47. Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
- Author
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Dean, Sarah, Mania, Horia, Matni, Nikolai, Recht, Benjamin, and Tu, Stephen
- Subjects
Computer Science - Learning ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the estimation of linear systems and in robust controller synthesis, we present the first provably polynomial time algorithm that provides high probability guarantees of sub-linear regret on this problem. We further study the interplay between regret minimization and parameter estimation by proving a lower bound on the expected regret in terms of the exploration schedule used by any algorithm. Finally, we conduct a numerical study comparing our robust adaptive algorithm to other methods from the adaptive LQR literature, and demonstrate the flexibility of our proposed method by extending it to a demand forecasting problem subject to state constraints.
- Published
- 2018
48. Delayed Impact of Fair Machine Learning
- Author
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Liu, Lydia T., Dean, Sarah, Rolf, Esther, Simchowitz, Max, and Hardt, Moritz
- Subjects
Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs., Comment: 37 pages, 6 figures
- Published
- 2018
49. On the Sample Complexity of the Linear Quadratic Regulator
- Author
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Dean, Sarah, Mania, Horia, Matni, Nikolai, Recht, Benjamin, and Tu, Stephen
- Subjects
Mathematics - Optimization and Control ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown. We propose a multi-stage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then designs a controller using both the model and uncertainty estimate. Our technique uses contemporary tools from random matrix theory to bound the error in the estimation procedure. We also employ a recently developed approach to control synthesis called System Level Synthesis that enables robust control design by solving a convex optimization problem. We provide end-to-end bounds on the relative error in control cost that are nearly optimal in the number of parameters and that highlight salient properties of the system to be controlled such as closed-loop sensitivity and optimal control magnitude. We show experimentally that the Coarse-ID approach enables efficient computation of a stabilizing controller in regimes where simple control schemes that do not take the model uncertainty into account fail to stabilize the true system., Comment: Contains a new analysis of finite-dimensional truncation, a new data-dependent estimation bound, and an expanded exposition on necessary background in control theory and System Level Synthesis
- Published
- 2017
50. Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users
- Author
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Fede, Samantha J, Grodin, Erica N, Dean, Sarah F, Diazgranados, Nancy, and Momenan, Reza
- Subjects
Biological Psychology ,Psychology ,Alcoholism ,Alcohol Use and Health ,Biomedical Imaging ,Clinical Research ,Behavioral and Social Science ,Mental Health ,Neurosciences ,Basic Behavioral and Social Science ,Pediatric ,Brain Disorders ,Substance Misuse ,Underage Drinking ,Neurological ,Mental health ,Good Health and Well Being ,Adult ,Alcoholism ,Connectome ,Female ,Humans ,Machine Learning ,Magnetic Resonance Imaging ,Male ,Middle Aged ,Models ,Neurological ,Nerve Net ,Severity of Illness Index ,Young Adult ,Alcohol use disorder ,MRI ,Machine learning ,Multimodal ,Connectivity ,Imaging ,Biological psychology ,Clinical and health psychology - Abstract
BACKGROUND:In the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences. METHODS:Adults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24). RESULTS:The resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model. CONCLUSIONS:These findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.
- Published
- 2019
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