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The bulk photovoltaic effect exhibited by the non-centrosymmetric system gains research interest due to the observed large open-circuit voltage. The ferroelectric systems exhibiting anomalous photovoltaic effect are mostly crystallized with multiphase coexistence. Hence, the computational difficulty in building a multi-phase system restricts the detailed photovoltaic studies through phenomenological and shift current theory. In this work, ferroelectric Ba$_{1- x}$(Bi$_{0.5}$K$_{0.5}$)$_x$TiO$_3$ (BBKT) oxide is designed to crystallize in single-phase tetragonal symmetry with improved polarization characteristics, and it is found to exhibits large PV response. Both experimental and theoretical studies on BBKT samples reveal ~18% reduction in bandgap compared to the parent BaTiO$_3$. Short-circuit current measured as a function of light intensity and light polarization angle reveal linear and sinusoidal response, respectively. The observed features are in accordance with phenomenological theory. Remarkably, x = 0.125 sample displays ~8 times higher open-circuit voltage (7.39 V) than the parent compound. The enhanced PV effect is attributed to the large shift current along z-direction as evidenced from the additional charge-center shift of valence band occupied by O-2p orbital and conduction band occupied by Bi-6p orbital. Notably, the degenerate Bi-p$_z$ state at conduction band minimum in BBKT favours the large shift current response in the z-direction.

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by explicit KG reasoning, where a model starts from a user node, sequentially determines the next step, and walks towards an item node of potential interest to the user. However, this is challenging due to the huge search space, unknown destination, and sparse signals over the KG, so informative and effective guidance is needed to achieve a satisfactory recommendation quality. To this end, we propose a CoArse-to-FinE neural symbolic reasoning approach (CAFE). It first generates user profiles as coarse sketches of user behaviors, which subsequently guide a path-finding process to derive reasoning paths for recommendations as fine-grained predictions. User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user. To better exploit the user profiles, an improved path-finding algorithm called Profile-guided Path Reasoning (PPR) is also developed, which leverages an inventory of neural symbolic reasoning modules to effectively and efficiently find a batch of paths over a large-scale KG. We extensively experiment on four real-world benchmarks and observe substantial gains in the recommendation performance compared with state-of-the-art methods., Comment: Accepted in CIKM 2020

Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen. Pure rule-based symbolic methods provide a transparent reasoning process over knowledge graph but lack generalization ability to unseen examples, while deep learning models enhance powerful feature representation ability but are hard to interpret. Moreover, direct reasoning over large-scale knowledge graph can be costly due to the huge search space of pathfinding. We approach the problem through a novel coarse-to-fine neural symbolic reasoning method called NSER. It first generates a coarse-grained explanation to capture abstract user behavioral pattern, followed by a fined-grained explanation accompanying with explicit reasoning paths and recommendations inferred from knowledge graph. We extensively experiment on four real-world datasets and observe substantial gains of recommendation performance compared with state-of-the-art methods as well as more diversified explanations in different granularity., Comment: Accepted at AAAI 2020 Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA 20)

Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods., Comment: Accepted in SIGIR 2019

A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that order. The publisher picks the order and prices to maximize her revenue. A traditional solution is to learn the demand model and then subsequently solve the optimization problem for the given demand model. This will incur a linear regret. We design an online learning algorithm for solving this problem, which interleaves learning and optimization, and prove that this algorithm has sublinear regret. We evaluate the algorithm on both synthetic and real-world data, and show that it quickly learns high quality pricing strategies. This is the first principled study of learning a waterfall design online by sequential experimentation.

Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies offline and optimize them before they are deployed. We address this problem by proposing evaluation algorithms for estimating the expected number of clicks on ranked lists from historical logged data. The existing algorithms are not guaranteed to be statistically efficient in our problem because the number of recommended lists can grow exponentially with their length. To overcome this challenge, we use models of user interaction with the list of items, the so-called click models, to construct estimators that learn statistically efficiently. We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds. We evaluate our estimators in a series of experiments on a real-world dataset and show that they consistently outperform prior estimators.

Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix from sequential observations. At each step, a learning agent chooses pairs of row and column arms, and receives the noisy product of their latent values as a reward. The main challenge is that the latent values are unobserved. We identify a class of non-negative matrices whose maximum entry can be found statistically efficiently and propose an algorithm for finding them, which we call LowRankElim. We derive a $\DeclareMathOperator{\poly}{poly} O((K + L) \poly(d) \Delta^{-1} \log n)$ upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the number of columns, $d$ is the rank of the matrix, and $\Delta$ is the minimum gap. The bound depends on other problem-specific constants that clearly do not depend $K L$. To the best of our knowledge, this is the first such result in the literature.

Data streams typically have items of large number of dimensions. We study the fundamental heavy-hitters problem in this setting. Formally, the data stream consists of $d$-dimensional items $x_1,\ldots,x_m \in [n]^d$. A $k$-dimensional subcube $T$ is a subset of distinct coordinates $\{ T_1,\cdots,T_k \} \subseteq [d]$. A subcube heavy hitter query ${\rm Query}(T,v)$, $v \in [n]^k$, outputs YES if $f_T(v) \geq \gamma$ and NO if $f_T(v) < \gamma/4$, where $f_T$ is the ratio of number of stream items whose coordinates $T$ have joint values $v$. The all subcube heavy hitters query ${\rm AllQuery}(T)$ outputs all joint values $v$ that return YES to ${\rm Query}(T,v)$. The one dimensional version of this problem where $d=1$ was heavily studied in data stream theory, databases, networking and signal processing. The subcube heavy hitters problem is applicable in all these cases. We present a simple reservoir sampling based one-pass streaming algorithm to solve the subcube heavy hitters problem in $\tilde{O}(kd/\gamma)$ space. This is optimal up to poly-logarithmic factors given the established lower bound. In the worst case, this is $\Theta(d^2/\gamma)$ which is prohibitive for large $d$, and our goal is to circumvent this quadratic bottleneck. Our main contribution is a model-based approach to the subcube heavy hitters problem. In particular, we assume that the dimensions are related to each other via the Naive Bayes model, with or without a latent dimension. Under this assumption, we present a new two-pass, $\tilde{O}(d/\gamma)$-space algorithm for our problem, and a fast algorithm for answering ${\rm AllQuery}(T)$ in $O(k/\gamma^2)$ time. Our work develops the direction of model-based data stream analysis, with much that remains to be explored., Comment: To appear in WWW 2018

We study which property testing and sublinear time algorithms can be transformed into graph streaming algorithms for random order streams. Our main result is that for bounded degree graphs, any property that is constant-query testable in the adjacency list model can be tested with constant space in a single-pass in random order streams. Our result is obtained by estimating the distribution of local neighborhoods of the vertices on a random order graph stream using constant space. We then show that our approach can also be applied to constant time approximation algorithms for bounded degree graphs in the adjacency list model: As an example, we obtain a constant-space single-pass random order streaming algorithms for approximating the size of a maximum matching with additive error $\epsilon n$ ($n$ is the number of nodes). Our result establishes for the first time that a large class of sublinear algorithms can be simulated in random order streams, while $\Omega(n)$ space is needed for many graph streaming problems for adversarial orders., Comment: A preliminary version was presented at the 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)

Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for line-rate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory., Comment: SOSR 2017, Santa Clara, CA

Estimating the size of the maximum matching is a canonical problem in graph algorithms, and one that has attracted extensive study over a range of different computational models. We present improved streaming algorithms for approximating the size of maximum matching with sparse (bounded arboricity) graphs. * Insert-Only Streams: We present a one-pass algorithm that takes O(c log^2 n) space and approximates the size of the maximum matching in graphs with arboricity c within a factor of O(c). This improves significantly on the state-of-the-art O~(cn^{2/3})-space streaming algorithms. * Dynamic Streams: Given a dynamic graph stream (i.e., inserts and deletes) of edges of an underlying c-bounded arboricity graph, we present a one-pass algorithm that uses space O~(c^{10/3}n^{2/3}) and returns an O(c)-estimator for the size of the maximum matching. This algorithm improves the state-of-the-art O~(cn^{4/5})-space algorithms, where the O~(.) notation hides logarithmic in $n$ dependencies. In contrast to the previous works, our results take more advantage of the streaming access to the input and characterize the matching size based on the ordering of the edges in the stream in addition to the degree distributions and structural properties of the sparse graphs., Comment: 12 pages

Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does not scale well beyond a few variables. In this work, we bring together the ideas of graphical models and count sketches; and propose and analyze several approaches to estimating probabilities in structured high-cardinality streams of data. The key idea of our approximations is to use the structure of a graphical model and approximately estimate its factors by "sketches", which hash high-cardinality variables using random projections. Our approximations are computationally efficient and their space complexity is independent of the cardinality of variables. Our error bounds are multiplicative and significantly improve upon those of the CM sketch, a state-of-the-art approach to estimating probabilities in streams. We evaluate our approximations on synthetic and real-world problems, and report an order of magnitude improvements over the CM sketch., Comment: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases

We propose the study of computing the Shapley value for a new class of cooperative games that we call budgeted games, and investigate in particular knapsack budgeted games, a version modeled after the classical knapsack problem. In these games, the "value" of a set $S$ of agents is determined only by a critical subset $T\subseteq S$ of the agents and not the entirety of $S$ due to a budget constraint that limits how large $T$ can be. We show that the Shapley value can be computed in time faster than by the na\"ive exponential time algorithm when there are sufficiently many agents, and also provide an algorithm that approximates the Shapley value within an additive error. For a related budgeted game associated with a greedy heuristic, we show that the Shapley value can be computed in pseudo-polynomial time. Furthermore, we generalize our proof techniques and propose what we term algorithmic representation framework that captures a broad class of cooperative games with the property of efficient computation of the Shapley value. The main idea is that the problem of determining the efficient computation can be reduced to that of finding an alternative representation of the games and an associated algorithm for computing the underlying value function with small time and space complexities in the representation size., Comment: A short version to appear in the 10th Conference on Web and Internet Economics (WINE 2014)

In this work, we aim to understand the mechanisms driving academic collaboration. We begin by building a model for how researchers split their effort between multiple papers, and how collaboration affects the number of citations a paper receives, supported by observations from a large real-world publication and citation dataset, which we call the h-Reinvestment model. Using tools from the field of Game Theory, we study researchers' collaborative behavior over time under this model, with the premise that each researcher wants to maximize his or her academic success. We find analytically that there is a strong incentive to collaborate rather than work in isolation, and that studying collaborative behavior through a game-theoretic lens is a promising approach to help us better understand the nature and dynamics of academic collaboration., Comment: Presented at the 1st WWW Workshop on Big Scholarly Data (2014). 6 pages, 5 figures

Modern applications require processing streams of data for estimating statistical quantities such as quantiles with small amount of memory. In many such applications, in fact, one needs to compute such statistical quantities for each of a large number of groups, which additionally restricts the amount of memory available for the stream for any particular group. We address this challenge and introduce frugal streaming, that is algorithms that work with tiny -- typically, sub-streaming -- amount of memory per group. We design a frugal algorithm that uses only one unit of memory per group to compute a quantile for each group. For stochastic streams where data items are drawn from a distribution independently, we analyze and show that the algorithm finds an approximation to the quantile rapidly and remains stably close to it. We also propose an extension of this algorithm that uses two units of memory per group. We show with extensive experiments with real world data from HTTP trace and Twitter that our frugal algorithms are comparable to existing streaming algorithms for estimating any quantile, but these existing algorithms use far more space per group and are unrealistic in frugal applications; further, the two memory frugal algorithm converges significantly faster than the one memory algorithm., Comment: 12 pages, 11 figures

Over the past decade, advertising has emerged as the primary source of revenue for many web sites and apps. In this paper we report a first-of-its-kind study that seeks to broadly understand the features, mechanisms and dynamics of display advertising on the web - i.e., the Adscape. Our study takes the perspective of users who are the targets of display ads shown on web sites. We develop a scalable crawling capability that enables us to gather the details of display ads including creatives and landing pages. Our crawling strategy is focused on maximizing the number of unique ads harvested. Of critical importance to our study is the recognition that a user's profile (i.e. browser profile and cookies) can have a significant impact on which ads are shown. We deploy our crawler over a variety of websites and profiles and this yields over 175K distinct display ads. We find that while targeting is widely used, there remain many instances in which delivered ads do not depend on user profile; further, ads vary more over user profiles than over websites. We also assess the population of advertisers seen and identify over 3.7K distinct entities from a variety of business segments. Finally, we find that when targeting is used, the specific types of ads delivered generally correspond with the details of user profiles, and also on users' patterns of visit., Comment: 11 pages, 9 figures

We present the problem of finding comparable researchers for any given researcher. This problem has many motivations. Firstly, know thyself. The answers of where we stand among research community and who we are most alike may not be easily found by existing evaluations of ones' research mainly based on citation counts. Secondly, there are many situations where one needs to find comparable researchers e.g., for reviewing peers, constructing programming committees or compiling teams for grants. It is often done through an ad hoc and informal basis. Utilizing the large scale scholarly data accessible on the web, we address the problem of automatically finding comparable researchers. We propose a standard to quantify the quality of research output, via the quality of publishing venues. We represent a researcher as a sequence of her publication records, and develop a framework of comparison of researchers by sequence matching. Several variations of comparisons are considered including matching by quality of publication venue and research topics, and performing prefix matching. We evaluate our methods on a large corpus and demonstrate the effectiveness of our methods through examples. In the end, we identify several promising directions for further work., Comment: BigScholar at WWW 2014

In many cases, recommendations are consumed by groups of users rather than individuals. In this paper, we present a system which recommends social events to groups. The system helps groups to organize a joint activity and collectively select which activity to perform among several possible options. We also facilitate the consensus making, following the principle of group consensus decision making. Our system allows users to asynchronously vote, add and comment on alternatives. We observe social influence within groups through post-recommendation feedback during the group decision making process. We propose a decision cascading model and estimate such social influence, which can be used to improve the performance of group recommendation. We conduct experiments to measure the prediction performance of our model. The result shows that the model achieves better results than that of independent decision making model.

Citations among research papers, and the networks they form, are the primary object of study in scientometrics. The act of making a citation reflects the citer's knowledge of the related literature, and of the work being cited. We aim to gain insight into this process by studying citation keys: user-chosen labels to identify a cited work. Our main observation is that the first listed author is disproportionately represented in such labels, implying a strong mental bias towards the first author., Comment: Computational Scientometrics: Theory and Applications at The 22nd CIKM 2013

In the classical experimental design setting, an experimenter E has access to a population of $n$ potential experiment subjects $i\in \{1,...,n\}$, each associated with a vector of features $x_i\in R^d$. Conducting an experiment with subject $i$ reveals an unknown value $y_i\in R$ to E. E typically assumes some hypothetical relationship between $x_i$'s and $y_i$'s, e.g., $y_i \approx \beta x_i$, and estimates $\beta$ from experiments, e.g., through linear regression. As a proxy for various practical constraints, E may select only a subset of subjects on which to conduct the experiment. We initiate the study of budgeted mechanisms for experimental design. In this setting, E has a budget $B$. Each subject $i$ declares an associated cost $c_i >0$ to be part of the experiment, and must be paid at least her cost. In particular, the Experimental Design Problem (EDP) is to find a set $S$ of subjects for the experiment that maximizes $V(S) = \log\det(I_d+\sum_{i\in S}x_i\T{x_i})$ under the constraint $\sum_{i\in S}c_i\leq B$; our objective function corresponds to the information gain in parameter $\beta$ that is learned through linear regression methods, and is related to the so-called $D$-optimality criterion. Further, the subjects are strategic and may lie about their costs. We present a deterministic, polynomial time, budget feasible mechanism scheme, that is approximately truthful and yields a constant factor approximation to EDP. In particular, for any small $\delta > 0$ and $\epsilon > 0$, we can construct a (12.98, $\epsilon$)-approximate mechanism that is $\delta$-truthful and runs in polynomial time in both $n$ and $\log\log\frac{B}{\epsilon\delta}$. We also establish that no truthful, budget-feasible algorithms is possible within a factor 2 approximation, and show how to generalize our approach to a wide class of learning problems, beyond linear regression.

We study computing the convolution of a private input $x$ with a public input $h$, while satisfying the guarantees of $(\epsilon, \delta)$-differential privacy. Convolution is a fundamental operation, intimately related to Fourier Transforms. In our setting, the private input may represent a time series of sensitive events or a histogram of a database of confidential personal information. Convolution then captures important primitives including linear filtering, which is an essential tool in time series analysis, and aggregation queries on projections of the data. We give a nearly optimal algorithm for computing convolutions while satisfying $(\epsilon, \delta)$-differential privacy. Surprisingly, we follow the simple strategy of adding independent Laplacian noise to each Fourier coefficient and bounding the privacy loss using the composition theorem of Dwork, Rothblum, and Vadhan. We derive a closed form expression for the optimal noise to add to each Fourier coefficient using convex programming duality. Our algorithm is very efficient -- it is essentially no more computationally expensive than a Fast Fourier Transform. To prove near optimality, we use the recent discrepancy lowerbounds of Muthukrishnan and Nikolov and derive a spectral lower bound using a characterization of discrepancy in terms of determinants.

A variety of bibliometric measures have been proposed to quantify the impact of researchers and their work. The h-index is a notable and widely-used example which aims to improve over simple metrics such as raw counts of papers or citations. However, a limitation of this measure is that it considers authors in isolation and does not account for contributions through a collaborative team. To address this, we propose a natural variant that we dub the Social h-index. The idea is to redistribute the h-index score to reflect an individual's impact on the research community. In addition to describing this new measure, we provide examples, discuss its properties, and contrast with other measures., Comment: 5 pages, 3 figures, 1 table

We consider a multi-armed bandit problem where payoffs are a linear function of an observed stochastic contextual variable. In the scenario where there exists a gap between optimal and suboptimal rewards, several algorithms have been proposed that achieve $O(\log T)$ regret after $T$ time steps. However, proposed methods either have a computation complexity per iteration that scales linearly with $T$ or achieve regrets that grow linearly with the number of contexts $|\myset{X}|$. We propose an $\epsilon$-greedy type of algorithm that solves both limitations. In particular, when contexts are variables in $\reals^d$, we prove that our algorithm has a constant computation complexity per iteration of $O(poly(d))$ and can achieve a regret of $O(poly(d) \log T)$ even when $|\myset{X}| = \Omega (2^d) $. In addition, unlike previous algorithms, its space complexity scales like $O(Kd^2)$ and does not grow with $T$., Comment: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013), Prague, Czech Republic, September 23-27, 2013. Proceedings. Springer, 2013

We study cardinal auctions for selling multiple copies of a good, in which bidders specify not only their bid or how much they are ready to pay for the good, but also a cardinality constraint on the number of copies that will be sold via the auction. We perform first known Price of Anarchy type analyses with detailed comparison of the classical Vickrey-Clarke-Groves (VCG) auction and one based on minimum pay property (MPP) which is similar to Generalized Second Price auction commonly used in sponsored search. Without cardinality constraints, MPP has the same efficiency (total value to bidders) and at least as much revenue (total income to the auctioneer) as VCG; this also holds for certain other generalizations of MPP (e.g., prefix constrained auctions, as we show here). In contrast, our main results are that, with cardinality constraints, (a) equilibrium efficiency of MPP is 1/2 of that of VCG and this factor is tight, and (b) in equilibrium MPP may collect as little as 1/2 the revenue of VCG. These aspects arise because in presence of cardinality constraints, more strategies are available to bidders in MPP, including bidding above their value, and this makes analyses nontrivial.

Display advertisements on the web are sold via ad exchanges that use real time auction. We describe the challenges of designing a suitable auction, and present a simple auction called the Optional Second Price (OSP) auction that is currently used in Doubleclick Ad Exchange.

A range counting problem is specified by a set $P$ of size $|P| = n$ of points in $\mathbb{R}^d$, an integer weight $x_p$ associated to each point $p \in P$, and a range space ${\cal R} \subseteq 2^{P}$. Given a query range $R \in {\cal R}$, the target output is $R(\vec{x}) = \sum_{p \in R}{x_p}$. Range counting for different range spaces is a central problem in Computational Geometry. We study $(\epsilon, \delta)$-differentially private algorithms for range counting. Our main results are for the range space given by hyperplanes, that is, the halfspace counting problem. We present an $(\epsilon, \delta)$-differentially private algorithm for halfspace counting in $d$ dimensions which achieves $O(n^{1-1/d})$ average squared error. This contrasts with the $\Omega(n)$ lower bound established by the classical result of Dinur and Nissim [PODS 2003] for arbitrary subset counting queries. We also show a matching lower bound on average squared error for any $(\epsilon, \delta)$-differentially private algorithm for halfspace counting. Both bounds are obtained using discrepancy theory. For the lower bound, we use a modified discrepancy measure and bound approximation of $(\epsilon, \delta)$-differentially private algorithms for range counting queries in terms of this discrepancy. We also relate the modified discrepancy measure to classical combinatorial discrepancy, which allows us to exploit known discrepancy lower bounds. This approach also yields a lower bound of $\Omega((\log n)^{d-1})$ for $(\epsilon, \delta)$-differentially private orthogonal range counting in $d$ dimensions, the first known superconstant lower bound for this problem. For the upper bound, we use an approach inspired by partial coloring methods for proving discrepancy upper bounds, and obtain $(\epsilon, \delta)$-differentially private algorithms for range counting with polynomially bounded shatter function range spaces.

Scientific literature has itself been the subject of much scientific study, for a variety of reasons: understanding how results are communicated, how ideas spread, and assessing the influence of areas or individuals. However, most prior work has focused on extracting and analyzing citation and stylistic patterns. In this work, we introduce the notion of 'scienceography', which focuses on the writing of science. We provide a first large scale study using data derived from the arXiv e-print repository. Crucially, our data includes the "source code" of scientific papers-the LaTEX source-which enables us to study features not present in the "final product", such as the tools used and private comments between authors. Our study identifies broad patterns and trends in two example areas-computer science and mathematics-as well as highlighting key differences in the way that science is written in these fields. Finally, we outline future directions to extend the new topic of scienceography., Comment: 13 pages,16 figures. Sixth International Conference on FUN WITH ALGORITHMS, 2012

Consider an input text string T[1,N] drawn from an unbounded alphabet. We study partial computation in suffix-based problems for Data Compression and Text Indexing such as (I) retrieve any segment of K<=N consecutive symbols from the Burrows-Wheeler transform of T, and (II) retrieve any chunk of K<=N consecutive entries of the Suffix Array or the Suffix Tree. Prior literature would take O(N log N) comparisons (and time) to solve these problems by solving the total problem of building the entire Burrows-Wheeler transform or Text Index for T, and performing a post-processing to single out the wanted portion. We introduce a novel adaptive approach to partial computational problems above, and solve both the partial problems in O(K log K + N) comparisons and time, improving the best known running times of O(N log N) for K=o(N). These partial-computation problems are intimately related since they share a common bottleneck: the suffix multi-selection problem, which is to output the suffixes of rank r_1,r_2,...,r_K under the lexicographic order, where r_1

In monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses - computing predicate sums - with privacy. Formally, we study the problem of estimating predicate sums {\em privately}, for sliding windows (and other well-known decay models of data, i.e. exponential and polynomial decay). We extend the recently proposed continual privacy model of Dwork et al. We present algorithms for decayed sum which are $\eps$-differentially private, and are accurate. For window and exponential decay sums, our algorithms are accurate up to additive $1/\eps$ and polylog terms in the range of the computed function; for polynomial decay sums which are technically more challenging because partial solutions do not compose easily, our algorithms incur additional relative error. Further, we show lower bounds, tight within polylog factors and tight with respect to the dependence on the probability of error.

In light of the growing market of Ad Exchanges for the real-time sale of advertising slots, publishers face new challenges in choosing between the allocation of contract-based reservation ads and spot market ads. In this setting, the publisher should take into account the tradeoff between short-term revenue from an Ad Exchange and quality of allocating reservation ads. In this paper, we formalize this combined optimization problem as a stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange bids. We prove asymptotic optimality of this policy in terms of any trade-off between quality of delivered reservation ads and revenue from the exchange, and provide a rigorous bound for its convergence rate to the optimal policy. We also give experimental results on data derived from real publisher inventory, showing that our policy can achieve any pareto-optimal point on the quality vs. revenue curve. Finally, we study a parametric training-based algorithm in which instead of learning the dual variables from a sample data (as is done in non-parametric training-based algorithms), we learn the parameters of the distribution and construct those dual variables from the learned parameter values. We compare parametric and non-parametric ways to estimate from data both analytically and experimentally in the special case without the ad exchange, and show that though both methods converge to the optimal policy as the sample size grows, our parametric method converges faster, and thus performs better on smaller samples.

When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled. These labels can indicate demographic values, interest, beliefs or other characteristics of the nodes (users). A core problem is to use this information to extend the labeling so that all nodes are assigned a label (or labels). In this chapter, we survey classification techniques that have been proposed for this problem. We consider two broad categories: methods based on iterative application of traditional classifiers using graph information as features, and methods which propagate the existing labels via random walks. We adopt a common perspective on these methods to highlight the similarities between different approaches within and across the two categories. We also describe some extensions and related directions to the central problem of node classification., Comment: To appear in Social Network Data Analytics (Springer) Ed. Charu Aggarwal, March 2011

Consider updates arriving online in which the $t$th input is $(i_t,d_t)$, where $i_t$'s are thought of as IDs of users. Informally, a randomized function $f$ is {\em differentially private} with respect to the IDs if the probability distribution induced by $f$ is not much different from that induced by it on an input in which occurrences of an ID $j$ are replaced with some other ID $k$ Recently, this notion was extended to {\em pan-privacy} where the computation of $f$ retains differential privacy, even if the internal memory of the algorithm is exposed to the adversary (say by a malicious break-in or by fiat by the government). This is a strong notion of privacy, and surprisingly, for basic counting tasks such as distinct counts, heavy hitters and others, Dwork et al~\cite{dwork-pan} present pan-private algorithms with reasonable accuracy. The pan-private algorithms are nontrivial, and rely on sampling. We reexamine these basic counting tasks and show improved bounds. In particular, we estimate the distinct count $\Dt$ to within $(1\pm \eps)\Dt \pm O(\polylog m)$, where $m$ is the number of elements in the universe. This uses suitably noisy statistics on sketches known in the streaming literature. We also present the first known lower bounds for pan-privacy with respect to a single intrusion. Our lower bounds show that, even if allowed to work with unbounded memory, pan-private algorithms for distinct counts can not be significantly more accurate than our algorithms. Our lower bound uses noisy decoding. For heavy hitter counts, we present a pan private streaming algorithm that is accurate to within $O(k)$ in worst case; previously known bound for this problem is arbitrarily worse. An interesting aspect of our pan-private algorithms is that, they deliberately use very small (polylogarithmic) space and tend to be streaming algorithms, even though using more space is not forbidden., Comment: 18 pages

We design algorithms for computing approximately revenue-maximizing {\em sequential posted-pricing mechanisms (SPM)} in $K$-unit auctions, in a standard Bayesian model. A seller has $K$ copies of an item to sell, and there are $n$ buyers, each interested in only one copy, who have some value for the item. The seller must post a price for each buyer, the buyers arrive in a sequence enforced by the seller, and a buyer buys the item if its value exceeds the price posted to it. The seller does not know the values of the buyers, but have Bayesian information about them. An SPM specifies the ordering of buyers and the posted prices, and may be {\em adaptive} or {\em non-adaptive} in its behavior. The goal is to design SPM in polynomial time to maximize expected revenue. We compare against the expected revenue of optimal SPM, and provide a polynomial time approximation scheme (PTAS) for both non-adaptive and adaptive SPMs. This is achieved by two algorithms: an efficient algorithm that gives a $(1-\frac{1}{\sqrt{2\pi K}})$-approximation (and hence a PTAS for sufficiently large $K$), and another that is a PTAS for constant $K$. The first algorithm yields a non-adaptive SPM that yields its approximation guarantees against an optimal adaptive SPM -- this implies that the {\em adaptivity gap} in SPMs vanishes as $K$ becomes larger., Comment: 16 pages

Ads on the Internet are increasingly sold via ad exchanges such as RightMedia, AdECN and Doubleclick Ad Exchange. These exchanges allow real-time bidding, that is, each time the publisher contacts the exchange, the exchange ``calls out'' to solicit bids from ad networks. This aspect of soliciting bids introduces a novel aspect, in contrast to existing literature. This suggests developing a joint optimization framework which optimizes over the allocation and well as solicitation. We model this selective call out as an online recurrent Bayesian decision framework with bandwidth type constraints. We obtain natural algorithms with bounded performance guarantees for several natural optimization criteria. We show that these results hold under different call out constraint models, and different arrival processes. Interestingly, the paper shows that under MHR assumptions, the expected revenue of generalized second price auction with reserve is constant factor of the expected welfare. Also the analysis herein allow us prove adaptivity gap type results for the adwords problem., Comment: 24 pages, 10 figures

Internet advertising is a sophisticated game in which the many advertisers "play" to optimize their return on investment. There are many "targets" for the advertisements, and each "target" has a collection of games with a potentially different set of players involved. In this paper, we study the problem of how advertisers allocate their budget across these "targets". In particular, we focus on formulating their best response strategy as an optimization problem. Advertisers have a set of keywords ("targets") and some stochastic information about the future, namely a probability distribution over scenarios of cost vs click combinations. This summarizes the potential states of the world assuming that the strategies of other players are fixed. Then, the best response can be abstracted as stochastic budget optimization problems to figure out how to spread a given budget across these keywords to maximize the expected number of clicks. We present the first known non-trivial poly-logarithmic approximation for these problems as well as the first known hardness results of getting better than logarithmic approximation ratios in the various parameters involved. We also identify several special cases of these problems of practical interest, such as with fixed number of scenarios or with polynomial-sized parameters related to cost, which are solvable either in polynomial time or with improved approximation ratios. Stochastic budget optimization with scenarios has sophisticated technical structure. Our approximation and hardness results come from relating these problems to a special type of (0/1, bipartite) quadratic programs inherent in them. Our research answers some open problems raised by the authors in (Stochastic Models for Budget Optimization in Search-Based Advertising, Algorithmica, 58 (4), 1022-1044, 2010)., Comment: FINAL version

Inspired by Internet ad auction applications, we study the problem of allocating a single item via an auction when bidders place very different values on the item. We formulate this as the problem of prior-free auction and focus on designing a simple mechanism that always allocates the item. Rather than designing sophisticated pricing methods like prior literature, we design better allocation methods. In particular, we propose quasi-proportional allocation methods in which the probability that an item is allocated to a bidder depends (quasi-proportionally) on the bids. We prove that corresponding games for both all-pay and winners-pay quasi-proportional mechanisms admit pure Nash equilibria and this equilibrium is unique. We also give an algorithm to compute this equilibrium in polynomial time. Further, we show that the revenue of the auctioneer is promisingly high compared to the ultimate, i.e., the highest value of any of the bidders, and show bounds on the revenue of equilibria both analytically, as well as using experiments for specific quasi-proportional functions. This is the first known revenue analysis for these natural mechanisms (including the special case of proportional mechanism which is common in network resource allocation problems).

We study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet. In the online, but adversarial case, the celebrated result of Karp, Vazirani and Vazirani gives an approximation ratio of $1-1/e$. In the online, stochastic case when nodes are drawn repeatedly from a known distribution, the greedy algorithm matches this approximation ratio, but still, no algorithm is known that beats the $1 - 1/e$ bound. Our main result is a 0.67-approximation online algorithm for stochastic bipartite matching, breaking this $1 - {1/e}$ barrier. Furthermore, we show that no online algorithm can produce a $1-\epsilon$ approximation for an arbitrarily small $\epsilon$ for this problem. We employ a novel application of the idea of the power of two choices from load balancing: we compute two disjoint solutions to the expected instance, and use both of them in the online algorithm in a prescribed preference order. To identify these two disjoint solutions, we solve a max flow problem in a boosted flow graph, and then carefully decompose this maximum flow to two edge-disjoint (near-)matchings. These two offline solutions are used to characterize an upper bound for the optimum in any scenario. This is done by identifying a cut whose value we can bound under the arrival distribution.

Given string $S[1..N]$ and integer $k$, the {\em suffix selection} problem is to determine the $k$th lexicographically smallest amongst the suffixes $S[i... N]$, $1 \leq i \leq N$. We study the suffix selection problem in the cache-aware model that captures two-level memory inherent in computing systems, for a \emph{cache} of limited size $M$ and block size $B$. The complexity of interest is the number of block transfers. We present an optimal suffix selection algorithm in the cache-aware model, requiring $\Thetah{N/B}$ block transfers, for any string $S$ over an unbounded alphabet (where characters can only be compared), under the common tall-cache assumption (i.e. $M=\Omegah{B^{1+\epsilon}}$, where $\epsilon<1$). Our algorithm beats the bottleneck bound for permuting an input array to the desired output array, which holds for nearly any nontrivial problem in hierarchical memory models.

Ad auctions in sponsored search support ``broad match'' that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present an linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions., Comment: World Wide Web Conference (WWW09), 10 pages, 2 figures