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The light scalar field with a coupling to standard model particles provide a possible source of the dark matter, long-range Yukawa forces or violation of the weak equivalence principle, which can be potentially explored by precision gravity experiments. We describe the searches for such light scalar fields with the three types of gravity experiments, including the $G$-measurement experiments, Inverse-Square Law (ISL) experiments, and equivalence principle experiments. We investigate the potential influences of the scalar field as a function of its mass, and focus on the experimental constraints from torsion-balance gravity experiments. HUST-18 $G$-measurement torsion-balance experiments place bounds on the photon coupling and electron coupling at up to $\Lambda_{\gamma}=7\times10^{17}$ GeV and $\Lambda_{e}=1\times10^{17}$ GeV in the mass ranges $10^{-9}-10^{-4}$ eV. Results from the ISL experiments by the Universities of Washington, Stanford, IUPUI, HUST, Colorado, Irvine, Yale and others allow us to set limits on the photon coupling and electron coupling at up to $\Lambda_{\gamma}=5\times10^{17}$ GeV and $\Lambda_{e}=3\times10^{16}$ GeV for scalar field mass ranges between $10^{-5}$ and $10^{-1}$ eV. Additionally, we also discuss the limits from equivalence principle experiments, and $MICROSCOPE$ final result updates the constrains on the coupling parameters at up to $\Lambda_{\gamma}=7\times10^{22}$ GeV and $\Lambda_{e}=4\times10^{21}$ GeV for mass ranges $\lesssim 10^{-13}$ eV. These results contribute experimental constraints to relatively unexplored mass regions of {light scalar field} parameter space and improve upon previous limits in some mass ranges. This work paves the way for long-range Yukawa forces mediated by light scalar fields in future high-precision gravity experiments., Comment: 21 pages, 6 fiures

Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex responses can be modelled whilst retaining interpretability. Previous work showed that additive Gaussian process models require high-dimensional interaction terms. We propose the orthogonal additive kernel (OAK), which imposes an orthogonality constraint on the additive functions, enabling an identifiable, low-dimensional representation of the functional relationship. We connect the OAK kernel to functional ANOVA decomposition, and show improved convergence rates for sparse computation methods. With only a small number of additive low-dimensional terms, we demonstrate the OAK model achieves similar or better predictive performance compared to black-box models, while retaining interpretability., Comment: 39th International Conference on Machine Learning (ICML 2022)

In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial domain, assuming that the relationships may exhibit complex spatially dynamic patterns that cannot be captured by constant regression coefficients. Our method integrates the robust finite mixture Gaussian regression model with spatial constraints, to simultaneously handle the spatial nonstationarity, local homogeneity, and outlier contaminations. Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships. As such, the proposed model not only accounts for nonstationarity in the spatial trend, but also clusters observations into a few distinct and homogenous groups. This provides an advantage on interpretation with a few stationary sub-processes identified that capture the predominant relationships between response and predictor variables. Moreover, the proposed method incorporates robust procedures to handle contaminations from both regression outliers and spatial outliers. By doing so, we robustly segment the spatial domain into distinct local regions with similar regression coefficients, and sporadic locations that are purely outliers. Rigorous statistical hypothesis testing procedure has been designed to test the significance of such segmentation. Experimental results on many synthetic and real-world datasets demonstrate the robustness, accuracy, and effectiveness of our proposed method, compared with other robust finite mixture regression, spatial regression and spatial segmentation methods., Comment: Accepted by ICDM IEEE 2021

Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal relationships between objects. A learned causal dynamics model can be used to transfer between variants of an environment with exchangeable perceptual features among objects but with the same underlying causal dynamics. We adapt continuous optimization for structure learning techniques to explicitly learn the cause and effects of the actions in an interactive environment and transfer to the target domain by categorization of the objects based on causal knowledge. We demonstrate the advantages of our approach in a gridworld setting by combining causal model-based approach with model-free approach in reinforcement learning., Comment: Presented at the Inductive Biases, Invariances and Generalization in Reinforcement Learning Workshop, ICML 2020

This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications and, more generally, in all fields where the goal is to optimize an output metric of a system of interconnected nodes. Our approach combines ideas from causal inference, uncertainty quantification and sequential decision making. In particular, it generalizes Bayesian optimization, which treats the input variables of the objective function as independent, to scenarios where causal information is available. We show how knowing the causal graph significantly improves the ability to reason about optimal decision making strategies decreasing the optimization cost while avoiding suboptimal solutions. We propose a new algorithm called Causal Bayesian Optimization (CBO). CBO automatically balances two trade-offs: the classical exploration-exploitation and the new observation-intervention, which emerges when combining real interventional data with the estimated intervention effects computed via do-calculus. We demonstrate the practical benefits of this method in a synthetic setting and in two real-world applications.