Paper Digest: UAI 2019 Highlights

July 21, 2019October 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 2019, it is to be held in Tel Aviv-Yafo, Israel

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 2019 Highlights

	Title	Authors	Highlight
1	Personalized Peer Truth Serum for Eliciting Multi-Attribute Personal Data	Naman Goel, Boi Faltings	In this paper, we consider the problem of eliciting personal attributes (for e.g. body measurements) of the agents.
2	Conditional Expectation Propagation	Zheng Wang, Shandian Zhe	To overcome these practical barriers, we propose conditional expectation propagation (CEP) that performs conditional moment matching given the variables outside each message fixed, and then takes expectation w.r.t their approximate posterior.
3	A Sparse Representation-Based Approach to Linear Regression with Partially Shuffled Labels	Martin Slawski, Mostafa Rahmani, Ping Li	In this paper, we translate the problem of linear regression with unknown permutation to a robust subspace recovery problem, or alternatively, outlier recovery problem.
4	On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don?t Worry About its Nonsmooth Loss Function	Xinguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao	To shed light on such a “free-lunch” phenomenon, we study the square-root-Lasso (SQRT-Lasso) type regression problem.
5	Correlated Learning for Aggregation Systems	Tanvi Verma, Pradeep Varakantham	Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence of a self interested central entity.
6	Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias	Patrick Forr?, Joris M. Mooij	We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions.
7	Variational Regret Bounds for Reinforcement Learning	Ronald Ortner, Pratik Gajane, Peter Auer	For this problem setting, we propose an algorithm and provide performance guarantees for the regret evaluated against the optimal non-stationary policy.
8	Recommendation from Raw Data with Adaptive Compound Poisson Factorization	Olivier Gouvert, Thomas Oberlin, C?dric F?votte	The paper contributions are three-fold: we present a unified framework for discrete data (dcPF), leading to an adaptive and scalable algorithm; we show that our framework achieves a trade-off between Poisson Factorization (PF) applied to raw and binarized data; we study four specific instances that are relevant to recommendation and exhibit new links with combinatorics.
9	One-Shot Marginal MAP Inference in Markov Random Fields	Hao Xiong, Yuanzhen Guo, Yibo Yang, Nicholas Ruozzi	In this work, we propose a novel variational inference strategy that is flexible enough to handle both continuous and discrete random variables, efficient enough to be able to handle repeated statistical inferences, and scalable enough, via modern GPUs, to be practical on MRFs with hundreds of thousands of random variables.
10	Truly Proximal Policy Optimization	Yuhui Wang, Hao He, Xiaoyang Tan	In this paper, we show that PPO could neither strictly restrict the probability ratio as it attempts to do nor enforce a well-defined trust region constraint, which means that it may still suffer from the risk of performance instability.
11	Learning Factored Markov Decision Processes with Unawareness	Craig Innes, Alex Lascarides	In this paper, we give a method to learn factored markov decision problems from both domain exploration and expert assistance, which guarantees convergence to near-optimal behaviour, even when the agent begins unaware of factors critical to success.
12	Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions	Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely	This paper derives BNN architectures mirroring such kernel combinations.
13	Countdown Regression: Sharp and Calibrated Survival Predictions	Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng	In this paper we present the \emph{Survival-CRPS}, a generalization of the CRPS to the survival prediction setting, with right-censored and interval-censored variants.
14	Reducing Exploration of Dying Arms in Mortal Bandits	Stefano Trac?, Cynthia Rudin, Weiyu Yan	We provide adaptations of algorithms, regret bounds, and experiments for this study, showing a clear benefit from regulating greed (exploration/exploitation) for arms that will soon disappear.
15	Comparing EM with GD in Mixture Models of Two Components	Guojun Zhang, Pascal Poupart, George Trimponias	In this paper, we study the regions where one component is missing in two-component mixture models, which we call one-cluster regions.
16	Efficient Search-Based Weighted Model Integration	Zhe Zeng, Guy Van den Broeck	To address this limitation, we propose an efficient model integration algorithm for theories with tree primal graphs.
17	Causal Discovery with General Non-Linear Relationships using Non-Linear ICA	Ricardo Pio Monti, Kun Zhang, Aapo Hyv?rinen	Here, we propose a framework through which we can perform causal discovery in the presence of general non-linear relationships.
18	BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback	Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesv?ri, Masrour Zoghi	In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists.
19	Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory	Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Sch?lkopf	Addressing (2), we propose two assistant algorithms that sequentially learn from users’ reactions, together with optimality/ convergence guarantees.
20	The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA	Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Sch?lkopf	We present novel identifiability proofs that this is possible when the multiple views are considered jointly, showing that the mixing can theoretically be undone using function approximators such as deep neural networks.
21	Random Clique Covers for Graphs with Local Density and Global Sparsity	Sinead A. Williamson, Mauricio Tec	We develop a Bayesian nonparametric graph model based on random edge clique covers, and show that this model can capture power law degree distribution, global sparsity and non-vanishing local clustering coefficient.
22	Randomized Iterative Algorithms for Fisher Discriminant Analysis	Agniva Chowdhury, Jiasen Yang, Petros Drineas	In this paper, we present a simple, iterative, sketching-based algorithm for RFDA that comes with provable accuracy guarantees when compared to the conventional approach.
23	Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests	Taoan Huang, Bohui Fang, Xiaohui Bei, Fei Fang	Built upon CST-function, we design a randomized best-fit algorithm for scheduled requests and an online planning algorithm for on-demand requests given the scheduled requests as constraints.
24	Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation	Cong Xie, Oluwasanmi Koyejo, Indranil Gupta	In this paper, we break two prevailing Byzantine-tolerant techniques.
25	Adaptive Hashing for Model Counting	Jonathan Kuck, Tri Dao, Shengjia Zhao, Burak Bartan, Ashish Sabharwal, Stefano Ermon	In this paper we propose a scheme where the family of hash functions is chosen adaptively, based on properties of the specific input formula.
26	Towards a Better Understanding and Regularization of GAN Training Dynamics	Weili Nie, Ankit B. Patel	Thus we propose a new JAcobian REgularization (JARE) for GANs, which simultaneously addresses both factors by construction.
27	Domain Generalization via Multidomain Discriminant Analysis	Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan	In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations.
28	Efficient Planning Under Uncertainty with Incremental Refinement	Juan Carlos Sabor?o, Joachim Hertzberg	We address this issue by introducing a method to estimate relevance values for elements of a planning domain, that allow an agent to focus on promising features.
29	Cubic Regularization with Momentum for Nonconvex Optimization	Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan	In this paper, we apply the momentum scheme to cubic regularized (CR) Newton’s method and explore the potential for acceleration.
30	Stability of Linear Structural Equation Models of Causal Inference	Karthik Abhinav Sankararaman, Anand Louis, Navin Goyal	The goal of the present paper is complementary to this line of work: we want to understand the stability of the recovery problem in the cases when efficient recovery is possible.
31	Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning	Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Xiaoting Shao, Martin Trapp, Kristian Kersting, Zoubin Ghahramani	In this paper, we follow a simple “deep learning” approach, by generating unspecialized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization.
32	Towards Robust Relational Causal Discovery	Sanghack Lee, Vasant Honavar	We consider the problem of learning causal relationships from relational data.
33	The Role of Memory in Stochastic Optimization	Antonio Orvieto, Jonas Kohler, Aurelien Lucchi	Building on this observation, we use stochastic differential equations (SDEs) to explicitly study the role of memory in gradient-based algorithms.
34	Random Search and Reproducibility for Neural Architecture Search	Liam Li, Ameet Talwalkar	In order to help ground the empirical results in this field, we propose new NAS baselines that build off the following observations: (i) NAS is a specialized hyperparameter optimization problem; and (ii) random search is a competitive baseline for hyperparameter optimization.
35	Joint Nonparametric Precision Matrix Estimation with Confounding	Sinong Geng, Mladen Kolar, Oluwasanmi Koyejo	Following the scientific motivation, we propose a graphical model, which in turn motivates a joint nonparametric estimator.
36	General Identifiability with Arbitrary Surrogate Experiments	Sanghack Lee, Juan D. Correa, Elias Bareinboim	In this paper, we introduce a general strategy to prove non-gID based on \textit{hedgelets} and \textit{thickets}, which leads to a necessary and sufficient graphical condition for the corresponding decision problem.
37	Differentiable Probabilistic Models of Scientific Imaging with the Fourier Slice Theorem	Karen Ullrich, Rianne van den Berg, Marcus Brubaker, David Fleet, Max Welling	In this paper we show how back-propagation through the projection operator in Fourier space can be achieved.
38	Approximate Inference in Structured Instances with Noisy Categorical Observations	Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas	We present a new approximate algorithm for graphs with categorical variables that achieves low Hamming error in the presence of noisy vertex and edge observations.
39	Randomized Value Functions via Multiplicative Normalizing Flows	Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent	In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains.
40	A Fast Proximal Point Method for Computing Exact Wasserstein Distance	Yujia Xie, Xiangfeng Wang, Ruijia Wang, Hongyuan Zha	We address this challenge by developing an Inexact Proximal point method for exact Optimal Transport problem (IPOT) with the proximal operator approximately evaluated at each iteration using projections to the probability simplex.
41	Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks	Qi She, Anqi Wu	In this paper, we propose a novel latent dynamic model that is capable of capturing nonlinear, non- Markovian, long short-term time-dependent dynamics via recurrent neural networks and tackling complex nonlinear embedding via non-parametric Gaussian process.
42	Fisher-Bures Adversary Graph Convolutional Networks	Ke Sun, Piotr Koniusz, Zhen Wang	We present three different geometries on the manifold of graphs: the intrinsic geometry measures the information theoretic dynamics of a graph; the extrinsic geometry characterizes how such dynamics can affect externally a graph neural network; the embedding geometry is for measuring node embeddings.
43	Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning	Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee	In this paper, we focus on model-free RL using the epsilon-greedy exploration policy, which despite its simplicity, remains one of the most frequently used forms of exploration.
44	Periodic Kernel Approximation by Index Set Fourier Series Features	Anthony Tompkins, Fabio Ramos	We introduce a method that efficiently decomposes multi-dimensional periodic kernels into a set of basis functions by exploiting multivariate Fourier series.
45	Efficient Neural Network Verification with Exactness Characterization	Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli	We introduce a Lagrangian relaxation based on the SDP formulation and a novel algorithm to solve the relaxation that scales to networks that are two orders of magnitude larger than the off-the-shelf SDP solvers.
46	Augmenting and Tuning Knowledge Graph Embeddings	Robert Bamler, Farnood Salehi, Stephan Mandt	We propose an efficient method for large scale hyperparameter tuning by interpreting these models in a probabilistic framework.
47	A Tighter Analysis of Randomised Policy Iteration	Meet Taraviya, Shivaram Kalyanakrishnan	We prove a novel result on the structure of the policy space for k-action MDPs, k=2, which generalises a result known for k = 2.
48	Perturbed-History Exploration in Stochastic Linear Bandits	Branislav Kveton, Csaba Szepesv?ri, Mohammad Ghavamzadeh, Craig Boutilier	We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit.
49	An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient	Pan Xu, Felicia Gao, Quanquan Gu	We revisit the stochastic variance-reduced policy gradient (SVRPG) method proposed by \citet{papini2018stochastic} for reinforcement learning.
50	Deep Mixture of Experts via Shallow Embedding	Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez	We explore a mixture of experts (MoE) approach to deep dynamic routing, which activates certain experts in the network on a per-example basis.
51	Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation	Manuel Hau?mann, Fred A. Hamprecht, Melih Kandemir	We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation.
52	Sliced Score Matching: A Scalable Approach to Density and Score Estimation	Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon	We show this difficulty can be mitigated by projecting the scores onto random vectors before comparing them.
53	Beyond Structural Causal Models: Causal Constraints Models	Tineke Blom, Stephan Bongers, Joris M. Mooij	In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium.
54	Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits	Shreyas S, Aadirupa Saha, Chiranjib Bhattacharyya	We study the classical linear bandit problem on \emph{graphs} modelling arm rewards through an underlying graph structure G(N,E) such that rewards of neighboring nodes are similar.
55	Approximate Causal Abstractions	Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern	Here we extend this account to the more realistic case where an abstract causal model only offers an approximation of the underlying system.
56	The Sensitivity of Counterfactual Fairness to Unmeasured Confounding	Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva	In this work we design tools to assess the sensitivity of fairness measures to this confounding for the popular class of non-linear additive noise models (ANMs).
57	Belief Propagation: Accurate Marginals or Accurate Partition Function ? Where is the Difference?	Christian Knoll, Franz Pernkopf	We analyze belief propagation on patch potential models – these are attractive models with varying local potentials – obtain all of the possibly many fixed points, and gather novel insights into belief propagation’s properties.
58	Finding Minimal d-separators in Linear Time and Applications	Benito van der Zander, Maciej Liskiewicz	The study of graphical causal models is fundamentally the study of separations and conditional independences.
59	A Bayesian Approach to Robust Reinforcement Learning	Esther Derman, Daniel Mankowitz, Timothy Mann, Shie Mannor	In this study, we address the issue of learning in RMDPs using a Bayesian approach.
60	Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization	Guanghui Wang, Shiyin Lu, Lijun Zhang	In this paper, we study adaptive online convex optimization, and aim to design a universal algorithm that achieves optimal regret bounds for multiple common types of loss functions.
61	Evacuate or Not? A POMDP Model of the Decision Making of Individuals in Hurricane Evacuation Zones	Adithya Raam Sankar, Prashant Doshi, Adam Goodie	We aim to understand the observable and hidden variables involved in the decision-making process and model these in a partially observable Markov decision process, which predicts whether a person will evacuate or not given his or her current situation.
62	Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling	Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist, Thomas B. Sch?n	We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwellization via delayed sampling.
63	Variational Sparse Coding	Francesco Tonolini, Bj?rn Sand Jensen, Roderick Murray-Smith	We derive an evidence lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors.
64	Learning with Non-Convex Truncated Losses by SGD	Yi Xu, Shenghuo Zhu, Sen Yang, Chi Zhang, Rong Jin, Tianbao Yang	In this paper, we study a family of objective functions formed by truncating traditional loss functions, which is applicable to both shallow learning and deep learning.
65	Active Multi-Information Source Bayesian Quadrature	Alexandra Gessner, Javier Gonzalez, Maren Mahsereci	We construct meaningful cost-sensitive multi-source acquisition-rates as an extension to common utility functions from vanilla BQ (VBQ), and discuss pitfalls that arise from blindly generalizing.
66	Cascading Linear Submodular Bandits: Accounting for Position Bias and Diversity in Online Learning to Rank	Gaurush Hiranandani, Harvineet Singh, Prakhar Gupta, Iftikhar Ahamath Burhanuddin, Zheng Wen, Branislav Kveton	Motivated by these limitations, we propose a novel click model, and the associated online learning variant to address both position bias and diversified retrieval in a unified framework.
67	Sinkhorn AutoEncoders	Giorgio Patrini, Rianne van den Berg, Patrick Forr?, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen	We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error.
68	How to Exploit Structure while Solving Weighted Model Integration Problems	Samuel Kolb, Pedro Zuidberg Dos Martires, Luc De Raedt	We introduce a new algorithm, F-XSDD, that lifts these restrictions and can exploit factorizability in WMI problems with multivariate conditions and partially factorizable weight functions.
69	Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation	Th?o Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper	We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function.
70	A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations	Biswajit Paria, Kirthevasan Kandasamy, Barnab?s P?czos	In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem.
71	Efficient Multitask Feature and Relationship Learning	Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon	In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively.
72	Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning	Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson	We propose a highly flexible and practical approach to multi-fidelity Bayesian optimization, focused on efficiently optimizing hyperparameters for iteratively trained supervised learning models.
73	Adaptively Truncating Backpropagation Through Time to Control Gradient Bias	Christopher Aicher, Nicholas J. Foti, Emily B. Fox	We propose an adaptive TBPTT scheme that converts the problem from choosing a temporal lag to one of choosing a tolerable amount of gradient bias.
74	Sampling-free Uncertainty Estimation in Gated Recurrent Units with Applications to Normative Modeling in Neuroimaging	Seong Jae Hwang, Ronak R. Mehta, Hyunwoo J. Kim, Sterling C. Johnson, Vikas Singh	We show how classical ideas in the literature on exponential families on probabilistic networks provide an excellent starting point to derive uncertainty estimates in Gated Recurrent Units (GRU).
75	Online Factorization and Partition of Complex Networks by Random Walk	Lin Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang	In this paper, we focus on the online factorization and partition of implicit large lumpable networks based on observations from an associated random walk.
76	On Densification for Minwise Hashing	Tung Mai, Anup Rao, Matt Kapilevich, Ryan Rossi, Yasin Abbasi-Yadkori, Ritwik Sinha	In this paper, we give a necessary and sufficient condition for a densification routine to result in LSH sketches when applied to OPH sketches.
77	N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification	Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee	In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work.
78	Problem-dependent Regret Bounds for Online Learning with Feedback Graphs	Bingshan Hu, Nishant A. Mehta, Jianping Pan	We devise a UCB-based algorithm, UCB-NE, to provide a problem-dependent regret bound that depends on a clique covering.
79	Wasserstein Fair Classification	Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa	We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances.
80	Variational Training for Large-Scale Noisy-OR Bayesian Networks	Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth	We propose a stochastic variational inference algorithm for training large-scale Bayesian networks, where noisy-OR conditional distributions are used to capture higher-order relationships.
81	Guaranteed Scalable Learning of Latent Tree Models	Furong Huang, Niranjan Uma Naresh, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar	We present an integrated approach to structure and parameter estimation in latent tree graphical models, where some nodes are hidden.
82	On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits	Roman Pogodin, Tor Lattimore	We make three contributions to the theory of k-armed adversarial bandits.
83	Noise Contrastive Priors for Functional Uncertainty	Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson	We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates.
84	Fake It Till You Make It: Learning-Compatible Performance Support	Jonathan Bragg, Emma Brunskill	We term such assistance Learning-Compatible Performance Support, and present the Stochastic Q Bumpers algorithm for greatly improving learning outcomes while still providing high levels of performance support.
85	Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning	Smitha Milli, Anca D. Dragan	In this work, we focus on misspecification: we argue that robots might not know whether people are being pedagogic or literal and that it is important to ask which assumption is safer to make.
86	Convergence Analysis of Gradient-Based Learning in Continuous Games	Benjamin Chasnov, Lillian Ratliff, Eric Mazumdar, Samuel Burden	Considering a class of gradient-based multi-agent learning algorithms in non-cooperative settings, we provide convergence guarantees to a neighborhood of a stable Nash equilibrium.
87	End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations	Gregory Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt	To understand this relationship, we develop a multimodal modeling and inference framework that estimates shared latent structure of joint gene expression levels and medical image features.
88	Approximate Relative Value Learning for Average-reward Continuous State MDPs	Hiteshi Sharma, Mehdi Jafarnia-Jahromi, Rahul Jain	In this paper, we propose an approximate relative value learning (ARVL) algorithm for non- parametric MDPs with continuous state space and finite actions and average reward criterion.
89	Exact Sampling of Directed Acyclic Graphs from Modular Distributions	Topi Talvitie, Aleksis Vuoksenmaa, Mikko Koivisto	We consider the problem of sampling directed acyclic graphs (DAGs) from a given distribution.
90	Intervening on Network Ties	Eli Sherman, Ilya Shpitser	In this paper, we propose a type of structural intervention [12] relevant in network contexts: the network intervention.
91	Generating and Sampling Orbits for Lifted Probabilistic Inference	Steven Holtzen, Todd Millstein, Guy Van den Broeck	In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs.
92	Real-Time Robotic Search using Structural Spatial Point Processes	Olov Andersson, Per Sid?n, Johan Dahlin, Patrick Doherty, Mattias Villani	We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery.
93	Social Reinforcement Learning to Combat Fake News Spread	Mahak Goindani, Jennifer Neville	In this work, we develop a social reinforcement learning approach to combat the spread of fake news.
94	P3O: Policy-on Policy-off Policy Optimization	Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola	This paper develops a simple algorithm named P3O that interleaves off-policy updates with on-policy updates.
95	Causal Inference Under Interference And Network Uncertainty	Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser	In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori.
96	Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow	Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood	Here, we revisit the reweighted wake-sleep (RWS) [5] algorithm, and through extensive evaluations, show that it outperforms current state-of-the-art methods in learning SCFMs.
97	Learnability for the Information Bottleneck	Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark	In this paper, we show that if ? is improperly chosen, learning cannot happen – the trivial representation P(Z\|X) = P(Z) becomes the global minimum of the IB objective.
98	Learning Belief Representations for Imitation Learning in POMDPs	Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng	In this work, we investigate the belief representation learning problem for generative adversarial imitation learning in POMDPs.
99	Object Conditioning for Causal Inference	David Jensen, Javier Burroni, Matthew Rattigan	We describe and analyze a form of conditioning that is widely applied within social science and applied statistics but that is virtually unknown within causal graphical models.
100	CCMI : Classifier based Conditional Mutual Information Estimation	Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan	In this paper, we leverage advances in classifiers and generative models to design methods for CMI estimation.
101	Empirical Mechanism Design: Designing Mechanisms from Data	Enrique Areyan Viqueira, Cyrus Cousins, Yasser Mohammad, Amy Greenwald	We introduce a methodology for the design of parametric mechanisms, which are multiagent systems inhabited by strategic agents, with knobs that can be adjusted to achieve specific goals.
102	On the Relationship Between Satisfiability and Markov Decision Processes	Ricardo Salmon, Pascal Poupart	We describe constructive reductions between SSAT and flat POMDPs that open the door to comparisons and future cross-fertilization between the solution techniques of those problems.
103	Interpretable Almost Matching Exactly With Instrumental Variables	M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky	We propose a matching framework for IV in the presence of observed categorical confounders that addresses these weaknesses.
104	Low Frequency Adversarial Perturbation	Chuan Guo, Jared S. Frank, Kilian Q. Weinberger	In this paper we propose to restrict the search for adversarial images to a low frequency domain.
105	Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption	Ondrej Ku?elka, Jesse Davis	In this paper we show that the answer to this question is positive.
106	Identification In Missing Data Models Represented By Directed Acyclic Graphs	Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins	In this paper we consider the identifiability of the target distribution within this class of models, and show that the most general identification strategies proposed so far retain a significant gap in that they fail to identify a wide class of identifiable distributions.
107	A Weighted Mini-Bucket Bound for Solving Influence Diagram	Junkyu Lee, Radu Marinescu, Alexander Ihler, Rina Dechter	In this paper, we develop a weighted mini-bucket approach for bounding the MEU.
108	Subspace Inference for Bayesian Deep Learning	Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson	In this paper, we construct low-dimensional subspaces of parameter space, such as the first principal components of the stochastic gradient descent (SGD) trajectory, which contain diverse sets of high performing models.
109	Off-Policy Policy Gradient with Stationary Distribution Correction	Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill	Here we build on recent progress for estimating the ratio of the state distributions under behavior and evaluation policies for policy evaluation, and present an off-policy policy gradient optimization technique that can account for this mismatch in distributions.
110	Co-training for Policy Learning	Jialin Song, Ravi Lanka, Yisong Yue, Masahiro Ono	We present sufficient conditions under which learning from two views can improve upon learning from a single view alone.
111	Variational Inference of Penalized Regression with Submodular Functions	Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara	In this paper, we consider a hierarchical probabilistic model of linear regression and its kernel extension with this type of regularization, and develop a variational inference scheme for the posterior estimate on this model.
112	Probability Distillation: A Caveat and Alternatives	Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville	We identify a pathological optimization issue with the adopted stochastic minimization of the reverse-KL divergence: the curse of dimensionality results in a skewed gradient distribution that renders training inefficient.
113	Bayesian Optimization with Binary Auxiliary Information	Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low	This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machine learning models with early stopping.
114	On Open-Universe Causal Reasoning	Duligur Ibeling, Thomas Icard	We extend two kinds of causal models, structural equation models and simulation models, to infinite variable spaces.
115	Embarrassingly Parallel MCMC using Deep Invertible Transformations	Diego Mesquita, Paul Blomstedt, Samuel Kaski	In this work, we introduce a novel method that addresses these issues simultaneously.
116	Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems	Yingzhen Yang, Jiahui Yu	In this paper, we propose fast proximal gradient descent based methods to solve a class of non-convex and non-smooth sparse learning problems, i.e. the $\ell^0$ regularization problems.
117	Block Neural Autoregressive Flow	Nicola De Cao, Wilker Aziz, Ivan Titov	We propose block neural autoregressive flow (B-NAF), a much more compact universal approximator of density functions, where we model a bijection directly using a single feed-forward network.
118	Exclusivity Graph Approach to Instrumental Inequalities	Davide Poderini, Rafael Chaves, Iris Agresti, Gonzalo Carvacho, Fabio Sciarrino	In this work, we further explore this bridge between causality and quantum theory and apply a technique, originally developed in the field of quantum foundations, to express the constraints implied by causal relations in the language of graph theory.