Paper Digest: UAI 2020 Highlights

August 2, 2020October 6, 2020 admin

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The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. In 2020, it is to be held virtually due to covid-19 pandemic.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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TABLE 1: Paper Digest: UAI 2020 Highlights

	Title	Authors	Highlight
1	Semi-supervised learning, causality, and the conditional cluster assumption	Julius K?gelgen, Alexander Mey, Marco Loog, Bernhard Sch?lkopf	Since both these cases are restrictive, we extend their work by considering classification using cause and effect features at the same time, such as predicting a disease from both risk factors and symptoms.
2	Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise	Yue Wang, Shaofeng Zou	Our techniques in this paper provide a general framework for finite-sample analysis of non-convex value-based reinforcement learning algorithms for optimal control.
3	PAC-Bayesian Contrastive Unsupervised Representation Learning	Kento Nozawa, Pascal Germain, Benjamin Guedj	We present PAC-Bayesian generalisation bounds for CURL, which are then used to derive a new representation learning algorithm.
4	Static and Dynamic Values of Computation in MCTS	Eren Sezener, Peter Dayan	Inthis work, we take a more direct approach, and explicitly quantify thevalue of a computation based on its expected impact on the quality ofthe action eventually chosen.
5	Kernel Conditional Moment Test via Maximum Moment Restriction	Krikamol Muandet, Wittawat Jitkrittum, Jonas K?bler	We propose a new family of specification tests called kernel conditional moment (KCM) tests.
6	Bounding the expected run-time of nonconvex optimization with early stopping	Thomas Flynn, Kwangmin Yu, Abid Malik, Nicholas D?Imperio, Shinjae Yoo	We develop the approach in the general setting of a first-order optimization algorithm, with possibly biased update directions subject to a geometric drift condition.
7	Amortized variance reduction for doubly stochastic objective	Ayman Boustati, Sattar Vakili, James Hensman, ST John	We propose a new approach in which we use a recognition network to cheaply approximate the optimal control variate for each mini-batch, with no additional model gradient computations.
8	Randomized Exploration for Non-Stationary Stochastic Linear Bandits	Baekjin Kim, Ambuj Tewari	For non-stationary linear bandits, where each action is associated with a $d$-dimensional feature and the unknown parameter is time-varying with total variation $B_T$, we propose two randomized algorithms, Discounted Randomized LinUCB (D-RandLinUCB) and Discounted Linear Thompson Sampling (D-LinTS) via the two perturbation approaches.
9	Divergence-Based Motivation for Online EM and Combining Hidden Variable Models	Ehsan Amid, Manfred K. Warmuth	As a result, we formulate an online version of the EM algorithm by adding an analogous inertia term which is a relative entropy divergence to the old model.
10	Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty	Hongjoon Ahn, Taesup Moon	We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem.
11	Nonparametric Fisher Geometry with Application to Density Estimation	Andrew Holbrook, Shiwei Lan, Jeffrey Streets, Babak Shahbaba	Following recent work, we consider the nonparametric generalization of the Fisher geometry.
12	Learning Intrinsic Rewards as a Bi-Level Optimization Problem	Bradly Stadie, Lunjun Zhang, Jimmy Ba	To facilitate our methods, we introduces a new general conditioning layer: Conditional Layer Normalization (CLN).
13	Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems	Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar	When only one system’s dynamics are unknown and there is one-directional communication from the agent controlling the unknown system to the other agent, we propose a MARL algorithm based on the construction of an auxiliary single-agent LQ problem.
14	Learning Behaviors with Uncertain Human Feedback	Xu He, Haipeng Chen, Bo An	To address this challenge, we introduce a novel feedback model that considers the uncertainty of human feedback.
15	Regret Analysis of Bandit Problems with Causal Background Knowledge	Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan	We propose two algorithms, causal upper confidence bound (C-UCB) and causal Thompson Sampling (C-TS), that enjoy improved cumulative regret bounds compared with algorithms that do not use causal information.
16	Evaluation of Causal Structure Learning Algorithms via Risk Estimation	Marco Eigenmann, Sach Mukherjee, Marloes Maathuis	Motivated by this gap, we pose the following question: how can one assess, in a given problem setting, the practical efficacy of one or more causal structure learning methods?
17	Kidney Exchange with Inhomogeneous Edge Existence Uncertainty	hoda bidkhori, John Dickerson, Duncan McElfresh, Ke Ren	Subsequently, we propose a sample-average-approximation (SAA) based approach to solve this problem.
18	On the design of consequential ranking algorithms	Behzad Tabibian, Vicen? G?mez, Abir De, Bernhard Sch?lkopf, Manuel Gomez Rodriguez	To overcome this, we introduce an efficient gradient-based algorithm to learn parameterized consequential ranking models that effectively approximate optimal ones.
19	Fair Contextual Multi-Armed Bandits: Theory and Experiments	Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar, Stefanos Nikolaidis	We introduce a Multi-Armed Bandit algorithm with fairness constraints, where fairness is defined as a minimum rate at which a task or a resource is assigned to a user.
20	Submodular Bandit Problem Under Multiple Constraints	Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma	Thus, motivated by diversified retrieval considering budget constraints,we introduce a submodular bandit problem under the intersection of$l$ knapsacks and a $k$-system constraint.
21	Exploration Analysis in Finite-Horizon Turn-based Stochastic Games	Jialian Li, Yichi Zhou, Tongzheng Ren, Jun Zhu	In this work, we address the exploration issue for the $N$-player finite-horizon turn-based stochastic games (FTSG).
22	Amortized Nesterov?s Momentum: A Robust Momentum and Its Application to Deep Learning	Kaiwen Zhou, Yanghua Jin, Qinghua Ding, James Cheng	This work proposes a novel momentum technique, the Amortized Nesterov’s Momentum, for stochastic convex optimization.
23	Testing Goodness of Fit of Conditional Density Models with Kernels	Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Sch?lkopf	We propose two nonparametric statistical tests of goodness of fit for conditional distributions: given a conditional probability density function p(y\|x) and a joint sample, decide whether the sample is drawn from p(y\|x)q(x) for some density q(x).
24	Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes	Or Dinari, Oren Freifeld	As a remedy, we propose another, but closely related, hierarchical Bayesian nonparametric framework.
25	Statistically Efficient Greedy Equivalence Search	Max Chickering	We present simple synthetic experiments that compare a backward-only variantof the new algorithm to GES using finite data, showing increasing benefit of the new algorithm as the complexity of the generative model increases.
26	Robust Collective Classification against Structural Attacks	Kai Zhou, Yevgeniy Vorobeychik	We formulate the task of learning a robust AMN classifier as a bi-level program, where the inner problem is a challenging non- linear integer program that computes optimal structural changes to the AMN.
27	Efficient Rollout Strategies for Bayesian Optimization	Eric Lee, David Eriksson, David Bindel, Bolong Cheng, Mike Mccourt	We show that a combination of quasi-Monte Carlo, common random numbers, and control variates significantly reduce the computational burden of rollout. We then formulate a policy-search based approach that removes the need to optimize the rollout acquisition function.
28	IDA with Background Knowledge	Zhuangyan Fang, Yangbo He	In this paper, we consider the problem of estimating all possible causal effects from observational data with two types of background knowledge: direct causal information and non-ancestral information.
29	Complete Dictionary Learning via $\ell_p$-norm Maximization	Yifei Shen, Ye Xue, Jun Zhang, Khaled Letaief, Vincent Lau	In this paper, we investigate a family of $\ell_p$-norm ($p>2,p \in N$) maximization approaches for the complete dictionary learning problem from theoretical and algorithmic aspects.
30	Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects	Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng	In this paper, we combine the adjustment set selection procedure with the original IDA framework.
31	Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders	Sorawit Saengkyongam, Ricardo Silva	We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders.
32	Causal screening in dynamical systems	S?ren Wengel Mogensen	We suggest some inexpensive methods for causal screening which provide output with a sound causal interpretation under the assumption of ancestral faithfulness.
33	Bayesian Online Prediction of Change Points	Diego Agudelo-Espa?a, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Sch?lkopf, Jan Peters	We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time).
34	Walking on Two Legs: Learning Image Segmentation with Noisy Labels	Guohua Cheng, Hongli Ji, Yan Tian	In this paper, we propose a novel label refinement and sample reweighting method, and a novel generative adversarial network (GAN) is introduced to fuse these two models into an integrated framework.
35	Election Control by Manipulating Issue Significance	Andrew Estornell, Sanmay Das, Edith Elkind, Yevgeniy Vorobeychik	We study a variation of this model in which voters have judgments about relative importance of issues, and a malicious actor can manipulate these judgments.
36	Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples	Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger	We introduce a scalable optimization algorithm with no correction steps (in contrast to Frank–Wolfe and its variants), a variant of gradient ascent for coreset selection in graphs, that greedily selects a weighted subset of vertices that are deemed most important to sample.
37	Robust Spatial-Temporal Incident Prediction	Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, Yevgeniy Vorobeychik	We present a general approach for incident forecasting that is robust to spatial shifts.
38	Lagrangian Decomposition for Neural Network Verification	Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip Torr, M. Pawan Kumar	We propose a novel approach based on Lagrangian Decomposition.
39	Robust modal regression with direct gradient approximation of modal regression risk	Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori	In this paper, we take a novel approach of \emph{directly} approximating the gradient of MRR in modal regression.
40	A Simple Online Algorithm for Competing with Dynamic Comparators	Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou	In this paper, we design a simple algorithm based on the online ensemble, which provably enjoys the same (even slightly stronger) guarantee as the state-of-the-art rate, yet is much more efficient because our algorithm does not involve any non-convex problem solving.
41	Skewness Ranking Optimization for Personalized Recommendation	Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai	In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation.
42	High Dimensional Discrete Integration over the Hypergrid	Raj Kumar Maity, Arya Mazumdar, Soumyabrata Pal	In this paper, we show an improved technique to obtain an approximation factor of $4+O(1/q^2)$ to this problem.
43	Neural Likelihoods via Cumulative Distribution Functions	Pawel Chilinski, Ricardo Silva	We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs).
44	Unknown mixing times in apprenticeship and reinforcement learning	Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour	We derive and analyze learning algorithms for apprenticeship learning, policy evaluation and policy gradient for average reward criteria.
45	TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP	Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein	Thus, for TX-Ray, we modify the established computer vision explainability principle of ‘visualizing preferred inputs of neurons’ to make it usable for both NLP and for transfer analysis.
46	What You See May Not Be What You Get: UCB Bandit Algorithms Robust to $\varepsilon$-Contamination	Laura Niss, Ambuj Tewari	Motivated by applications of bandit algorithms in education, we consider a stochastic multi-armed bandit problem with $\varepsilon$-contaminated rewards.
47	The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks	Sikun Yang, Heinz Koeppl	We propose a novel probabilistic framework to model continuously generated interaction events data.
48	Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect	Priyank Agrawal, Theja Tulabandula	We provide novel algorithms that achieves sublinear regret in time and the relevant wear-in/wear-out parameters.
49	Compositional uncertainty in deep Gaussian processes	Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill Campbell, Carl Henrik Ek	We show that the application of simplifying mean-field assumptions across the hierarchy leads to the layers of a DGP collapsing to near-deterministic transformations.
50	Streaming Nonlinear Bayesian Tensor Decomposition	Zhimeng Pan, Zheng Wang, Shandian Zhe	To address this problem, we propose Streaming Nonlinear Bayesian Tensor Decomposition (SNBTD) that can conduct high-quality, closed-form and iteration-free updates upon receiving new tensor entries.
51	Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models	Xi Wang, Junming Yin	In this work, we propose a multivariate generalization of the Relaxed Bernoulli distribution, which can be reparameterized and can capture the correlation between variables via a Gaussian copula.
52	One-Bit Compressed Sensing via One-Shot Hard Thresholding	Jie Shen	We study a non-convex sparsity-constrained program and present a novel and concise analysis that moves away from the widely used notion of Gaussian width.
53	GPIRT: A Gaussian Process Model for Item Response Theory	JBrandon Duck-Mayr, Roman Garnett, Jacob Montgomery	We propose a Bayesian nonparametric model that solves this problem by placing Gaussian process priors on the latent functions defining the IRFs.
54	Identifying causal effects in maximally oriented partially directed acyclic graphs	Emilija Perkovic	We develop a necessary and sufficient causal identification criterion for maximally oriented partially directed acyclic graphs (MPDAGs).
55	Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator	Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou, Xiaoning Qian	We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing.
56	Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison	Tengyang Xie, Nan Jiang	We prove performance guarantees of two algorithms for approximating Q* in batch reinforcement learning.
57	Towards Threshold Invariant Fair Classification	Mingliang Chen, Min Wu	To achieve this goal, this paper proposes to equalize the risk distributions among the groups via two approximation methods.
58	Optimal Statistical Hypothesis Testing for Social Choice	Lirong Xia	We address the following question in this paper: “What are the most robust statistical methods for social choice?”
59	A SUPER* Algorithm to Optimize Paper Bidding in Peer Review	Tanner Fiez, Nihar Shah, Lillian Ratliff	In this paper, we develop a framework to study this problem in a principled manner.
60	Measurement Dependence Inducing Latent Causal Models	Alex Markham, Moritz Grosse-Wentrup	We show that this task can be framed in terms of the graph theoretic problem of finding edge clique covers,resulting in an algorithm for returning minimal MeDIL causal models (minMCMs).
61	The Indian Chefs Process	Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut G??l?, Max Hinne, Philippe Gigu?re, Marcel Gerven, Fran?ois Laviolette	This paper introduces the Indian chefs process (ICP) as a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes the Indian buffet process.
62	Spectral Methods for Ranking with Scarce Data	Lalit Jain, Anna Gilbert, Umang Varma	In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information.
63	Anchored Causal Inference in the Presence of Measurement Error	Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler	We consider the problem of learning a causal graph in the presence of measurement error.
64	How Private Are Commonly-Used Voting Rules?	Ao LIU, Yun Lu, Lirong Xia, Vassilis Zikas	In this work, we present the first framework for answering the question:“How private are commonly-used voting rules?"
65	Differentially Private Small Dataset Release Using Random Projections	Lovedeep Gondara, Ke Wang	As a solution, we propose DPRP (Differentially Private Data Release via Random Projections), a reconstruction based approach for releasing differentially private small datasets.
66	Semi-supervised Sequential Generative Models	Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood	We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available.
67	Robust contrastive learning and nonlinear ICA in the presence of outliers	Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvarinen	We develop two robust nonlinear ICA methods based on the ?-divergence, which is a robust alternative to the KL-divergence in logistic regression.
68	Selling Data at an Auction under Privacy Constraints	Mengxiao Zhang, Fernando Beltran, Jiamou Liu	For a data broker who wants to purchase data from such data owners, we propose the SingleMindedQuery (SMQ) mechanism, which uses a reverse auction to select data owners and determine compensations.
69	Mixed-Membership Stochastic Block Models for Weighted Networks	Adrien Dulac, Eric Gaussier, Christine Largeron	We address in this study the problem of modeling weighted networks through generalized stochastic block models.
70	MaskAAE: Latent space optimization for Adversarial Auto-Encoders	Arnab Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Himanshu Asnani, Parag Singla, Prathosh A P	In this work, we hypothesise that the dimensionality of the AE model’s latent space has a critical effect on the quality of generated data.
71	Slice Sampling for General Completely Random Measures	Peiyuan Zhu, Alexandre Bouchard-Cote, Trevor Campbell	In this paper we present a novel Markov chain Monte Carlo algorithm for posterior inference that adaptively sets the truncation level using auxiliary slice variables, enabling efficient, parallelized computation without sacrificing flexibility.
72	Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful	Jingge Zhu	In this work, we consider statistical models where the data distributions can be characterized by continuous parameters.
73	Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks	Meet Vadera, Brian Jalaian, Benjamin Marlin	In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior work on the Bayesian Dark Knowledge framework.
74	Complex Markov Logic Networks: Expressivity and Liftability	Ondrej Kuzelka	We introduce complex MLNs, which use complex-valued weights, and show that, unlike standard MLNs with real-valued weights, complex MLNs are"fully expressive".
75	Faster algorithms for Markov equivalence	Zhongyi Hu, Robin Evans	Several criteria for this already exist, but in this paper we give a new non-parametric characterization in terms of the heads and tails that arise in the parameterization for discrete models.
76	Verifying Individual Fairness in Machine Learning Models	Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha	Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem.
77	An Interpretable and Sample Efficient Deep Kernel for Gaussian Process	Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis, Shuguang Cui	We propose a novel Gaussian process kernel that takes advantage of a deep neural network (DNN) structure but retains good interpretability.
78	Amortized Bayesian Optimization over Discrete Spaces	Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy	Our key insight is that we can train a generative model to generate candidates that maximize the acquisition function.
79	Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation	Marko Jarvenpaa, Aki Vehtari, Pekka Marttinen	In this article we improve this approach in several ways.
80	Deep Sigma Point Processes	Martin Jankowiak, Geoff Pleiss, Jacob Gardner	We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs).
81	Robust $k$-means++	Amit Deshpande, Praneeth Kacham, Rameshwar Pratap	For any 0
82	On Counterfactual Explanations under Predictive Multiplicity	Martin Pawelczyk, Klaus Broelemann, Gjergji. Kasneci	In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity.
83	A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace	Zhiqiang Xu, Ping Li	In this work, we propose a practical Riemannian algorithm based on the first-order optimization on generalized Stiefel manifolds while efficiently leveraging second-order information.
84	No-regret Exploration in Contextual Reinforcement Learning	Aditya Modi, Ambuj Tewari	In this paper, we propose a no-regret online RL algorithm in the setting where the MDP parameters are obtained from the context using generalized linear mappings (GLMs).
85	Layering-MCMC for Structure Learning in Bayesian Networks	Jussi Viinikka, Mikko Koivisto	This renders the computation of the (unnormalized) posterior probability of a state, called layering, more involved, for which task we give an efficient dynamic programming algorithm.
86	C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation	Arnab Mondal, Arnab Bhattacharjee, Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan, Prathosh A P	In this work, we focus on conditional mutual information (CMI) estimation by utilizing its formulation as a \textit{minmax} optimization problem.
87	Stochastic Variational Inference for Dynamic Correlated Topic Models	Federico Tomasi, Praveen Chandar, Gal Levy-Fix, Mounia Lalmas-Roelleke, Zhenwen Dai	We propose an extension to CTM that models the evolution of both topic correlation and word co-occurrence over time.
88	Adversarial Learning for 3D Matching	Wei Xing, Brian Ziebart	In this paper, we explore extending an adversarial approach to learning bipartite matchings that avoids these weaknesses to the three dimensional setting.
89	Ordering Variables for Weighted Model Integration	Vincent Derkinderen, Evert Heylen, Pedro Zuidberg Dos Martires, Samuel Kolb, Luc Raedt	We show how variable ordering heuristics from the discrete domain can be ported to the discrete-continuous domain.
90	Online Parameter-Free Learning of Multiple Low Variance Tasks	Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos	We propose a method to learn a common bias vector for a growing sequence of low-variance tasks.
91	Zeroth Order Non-convex optimization with Dueling-Choice Bandits	Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski	We give the COMP-GP-UCB algorithm based on GP-UCB (Srinivas et al., 2009),, where instead of directly querying the point with the maximum Upper Confidence Bound (UCB), we perform constrained optimization and use comparisons to filter out suboptimal points.
92	Semi-bandit Optimization in the Dispersed Setting	Travis Dick, Wesley Pegden, Maria-Florina Balcan	In the online setting, we must choose algorithm parameters for each instance as they arrive, and our goal is to be competitive with the best fixed algorithm in hindsight.There are two major challenges in online data-driven algorithm design.
93	Adapting Text Embeddings for Causal Inference	Victor Veitch, Dhanya Sridhar, David Blei	This paper develops a method to estimate such causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality.
94	Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models	Zhijian Ou, Yunfu Song	In this paper, we show that the annoying difficulty of obtaining reliable stochastic gradients for the inference model and the drawback of indirectly optimizing the target log-likelihood can be gracefully addressed in a new method based on stochastic approximation (SA) theory of the Robbins-Monro type.
95	Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series	Hermanni H?lv?, Aapo Hyvarinen	The central idea in such works is that the latent components are assumed to be independent conditional on some observed auxiliary variables, such as the time-segment index.
96	Identification and Estimation of Causal Effects Defined by Shift Interventions	Numair Sani, Jaron Lee, Ilya Shpitser	We give sound and complete identification algorithms for both types of shift interventions, and derive efficient semi-parametric estimators for the mean response to a shift intervention in a special case motivated by a healthcare problem.
97	Risk Bounds for Low Cost Bipartite Ranking	San Gultekin, John Paisley	In this paper we consider an alternative approach, which leverages the structure of the widely-adopted pairwise squared loss, to obtain a stochastic and low cost algorithm that does not require stochastic gradients or learning rates.
98	Multitask Soft Option Learning	Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N Siddharth, Wendelin Boehmer, Shimon Whiteson	We present Multitask Soft Option Learning (MSOL), a hierarchical multitask framework based on Planning as Inference.
99	99% of Worker-Master Communication in Distributed Optimization Is Not Needed	Konstantin Mishchenko, Filip Hanzely, Peter Richtarik	In this paper we discuss sparsification of worker-to-server communication in large distributed systems.
100	Graphical continuous Lyapunov models	Gherardo Varando, Niels Richard Hansen	This parametrization canbe interpreted as a new graphical model class, and we show how the model classbehaves under marginalization and introduce a method for structure learning via$\ell_1$-penalized loss minimization.
101	Structure Learning for Cyclic Linear Causal Models	Carlos Amendola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu	We consider the problem of structure learning for linear causal models based on observational data.
102	Sensor Placement for Spatial Gaussian Processes with Integral Observations	Krista Longi, Chang Rajani, Tom Sillanp??, Joni M?kinen, Timo Rauhala, Ari Salmi, Edward Haeggstr?m, Arto Klami	We provide tools for optimizing sensor locations for GPs using integral observations, extending both model-based and geometric strategies for GP sensor placement.We demonstrate the techniques in ultrasonic detection of fouling in closed pipes.
103	Active Model Estimation in Markov Decision Processes	Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric	To address this issue, we propose an algorithm that is based on maximum weighted entropy, a heuristic that stems from common sense and our theoretical analysis.
104	Dueling Posterior Sampling for Preference-Based Reinforcement Learning	Ellen Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick	Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present DUELING POSTERIOR SAMPLING (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the preference feedback.
105	Permutation-Based Causal Structure Learning with Unknown Intervention Targets	Chandler Squires, Yuhao Wang, Caroline Uhler	We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown.
106	MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models	Ioan Gabriel Bucur, Tom Claassen, Tom Heskes	We propose a general and efficient causal inference algorithm that accounts for model uncertainty by performing Bayesian model averaging over the most promising many-dimensional instrumental variable models, while at the same time employing weaker assumptions regarding the data generating process.
107	Popularity Agnostic Evaluation of Knowledge Graph Embeddings	Aisha Mohamed, Shameem Parambath, Zoi Kaoudi, Ashraf Aboulnaga	In this paper, we show that the distribution of entities and relations in common knowledge graphs is highly skewed, with some entities and relations being much more popular than the rest.
108	Learning LWF Chain Graphs: A Markov Blanket Discovery Approach	Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi	We provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data and prove its correctness when the Markov blanket discovery algorithms in this paper are used.
109	Batch norm with entropic regularization turns deterministic autoencoders into generative models	Amur Ghose, Abdullah Rashwan, Pascal Poupart	We show in this work that utilizing batch normalization as a source for non-determinism suffices to turn deterministic autoencoders into generative models on par with variational ones, so long as we add a suitable entropic regularization to the training objective.
110	Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation	Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky	We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.
111	Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits	Aurelien Bibaut, Antoine Chambaz, Mark Laan	We propose the Generalized Policy Elimination (GPE) algorithm, an oracle-efficient contextual bandit (CB) algorithm inspired by the Policy Elimination algorithm of Dudik et al. [2011].
112	Differentially Private Top-k Selection via Stability on Unknown Domain	Ricardo Silva Carvalho, Ke Wang, Lovedeep Gondara, Chunyan Miao	We propose a new method that satisfies approximate differential privacy for top-$k$ selection with unordered output in the unknown data domain setting, not relying on the full knowledge of the domain universe.
113	Active Learning of Conditional Mean Embeddings via Bayesian Optimisation	Sayak Ray Chowdhury, Rafael Oliveira, Fabio Ramos	We consider the problem of sequentially optimising the conditional expectation of an objective function, with both the conditional distribution and the objective function assumed to be fixed but unknown.
114	Flexible Prior Elicitation via the Prior Predictive Distribution	Marcelo Hartmann, Georgi Agiashvili, Paul B?rkner, Arto Klami	We propose an alternative approach that only requires knowledge about the observable outcomes – knowledge which is often much easier for experts to provide.
115	Model-Augmented Conditional Mutual Information Estimation for Feature Selection	Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum	We propose a novel two-step approach which facilitates Markov blanket feature selection in high dimensions.
116	Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward	Jan Kretinsky, Fabian Michel, Lukas Michel, Guillermo Perez	We consider learning policies online in Markov decision processes with the long-run average reward (a.k.a. mean payoff).
117	Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles	Joris M. Mooij, Tom Claassen	In this work, we show that—surprisingly—the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback.
118	Estimation Rates for Sparse Linear Cyclic Causal Models	Jan-Christian Huetter, Philippe Rigollet	In this work, allowing for cycles, we study linear structural equations models with homoscedastic Gaussian noise and in the presence of interventions that make the model identifiable.
119	Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles	T?rik S. Salem, Helge Langseth, Heri Ramampiaro	In this paper, we present a method for generating prediction intervals along with point estimates from an ensemble of neural networks.
120	On the Relationship Between Probabilistic Circuits and Determinantal Point Processes	Honghua Zhang, Steven Holtzen, Guy Broeck	We propose a unified analysis and shared language for discussing DPPs and PCs.
121	Probabilistic Safety for Bayesian Neural Networks	Matthew Wicker, Luca Laurenti, Andrea Patane, Marta Kwiatkowska	We apply our methods to BNNs trained on a regression task, airborne collision avoidance, and MNIST, empirically showing that our approach allows one to certify probabilistic safety of BNNs with millions of parameters.
122	Distortion estimates for approximate Bayesian inference	Hanwen Xing, Geoff Nicholls, Jeong (Kate) Lee	We give graphical diagnostics for posterior approximation at the observed data.
123	Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch	Michael Wan, Tanmay Gangwani, Jian Peng	In this paper, we propose a new framework for transfer learning where the teacher and the student can have arbitrarily different state- and action-spaces.
124	Provably Efficient Third-Person Imitation from Offline Observation	Aaron Zweig, Joan Bruna	We present problem-dependent, statistical learning guarantees for third-person imitation from observation in an offline setting, and a lower bound on performance in the online setting.
125	Automated Dependence Plots	David Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar	To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model.
126	EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices	Jun Ho Yoon, Seyoung Kim	In this paper, we address the problem of jointly estimating dependencies across samples and dependencies across multiple features, where each set of dependencies is modeled as an inverse covariance matrix.
127	Improved Vector Pruning in Exact Algorithms for Solving POMDPs	Eric Hansen, Thomas Bowman	In this paper, we show how to similarly limit the number of variables.
128	Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings	Tal Friedman, Guy Broeck	We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data.
129	Learning to learn generative programs with Memoised Wake-Sleep	Luke Hewitt, Tuan Anh Le, Joshua Tenenbaum	To tackle the challenge of performing program induction as an ‘inner-loop’ to learning, we propose the Memoised Wake-Sleep (MWS) algorithm, which extends Wake Sleep by explicitly storing and reusing the best programs discovered by the inference network throughout training.
130	Flexible Approximate Inference via Stratified Normalizing Flows	Chris Cundy, Stefano Ermon	We develop an approximate inference procedure that allows explicit control of the bias/variance tradeoff, interpolating between the sampling and the variational regime.
131	Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits	Xinming Liu, Joseph Halpern	To test this hypothesis, we consider the multi-armed bandit (MAB) problem.
132	Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation	Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry Vetrov	The primary goal of this paper is to demonstrate that test-time augmentation policies can be successfully learned too.
133	Non Parametric Graph Learning for Bayesian Graph Neural Networks	Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates	Inthis paper, we propose a novel non-parametricgraph model for constructing the posterior distributionof graph adjacency matrices.
134	Stable Policy Optimization via Off-Policy Divergence Regularization	Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent	In this paper we revisit the theoretical foundations of these algorithms and propose a new algorithm which stabilizes the policy improvement through a proximity term that constrains the discounted state-action visitation distribution induced by consecutive policies to be close to one another.
135	PoRB-Nets: Poisson Process Radial Basis Function Networks	Beau Coker, Melanie Fernandez Pradier, Finale Doshi-Velez	We present a novel prior over radial basis function networks (RBFNs) that allows for independent specification of functional amplitude variance and lengthscale (i.e., smoothness), where the inverse lengthscale corresponds to the concentration of radial basis functions.
136	Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals	Noam Finkelstein, Ilya Shpitser	We develop a new general method for obtaining bounds on causal parameters using rules of probability and restrictions on counterfactuals implied by causal graphical models.
137	Locally Masked Convolution for Autoregressive Models	Ajay Jain, Pieter Abbeel, Deepak Pathak	To generate data in arbitrary orders, we introduce LMConv: a simple modification to the standard 2D convolution that allows arbitrary masks to be applied to the weights at each location in the image.
138	Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework	Rishabh Singh, Jose Principe	We demonstrate in this paper using synthesized datasets that such uncertainty features provide a better ability for online detection of statistical change points in time series data when compared to existing non-parametric and unsupervised methods.
139	OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation	Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg	We propose a variational inference framework OCEAN to perform online task inference for compositional tasks.
140	Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets	Jakob Runge	The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case.