UAI - RankMe – RankMe

141 papers

Year	Title / Authors
2020	99% of Worker-Master Communication in Distributed Optimization Is Not Needed. Konstantin Mishchenko, Filip Hanzely, Peter Richtárik
2020	A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace. Zhiqiang Xu, Ping Li
2020	A SUPER* Algorithm to Optimize Paper Bidding in Peer Review. Tanner Fiez, Nihar B. Shah, Lillian J. Ratliff
2020	A Simple Online Algorithm for Competing with Dynamic Comparators. Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou
2020	Active Learning of Conditional Mean Embeddings via Bayesian Optimisation. Sayak Ray Chowdhury, Rafael Oliveira, Fabio Ramos
2020	Active Model Estimation in Markov Decision Processes. Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric
2020	Adapting Text Embeddings for Causal Inference. Victor Veitch, Dhanya Sridhar, David M. Blei
2020	Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	Adversarial Learning for 3D Matching. Wei Xing, Brian D. Ziebart
2020	Amortized Bayesian Optimization over Discrete Spaces. Kevin Swersky, Yulia Rubanova, David Dohan, Kevin Murphy
2020	Amortized Nesterov's Momentum: A Robust Momentum and Its Application to Deep Learning. Kaiwen Zhou, Yanghua Jin, Qinghua Ding, James Cheng
2020	Amortized variance reduction for doubly stochastic objective. Ayman Boustati, Sattar Vakili, James Hensman, S. T. John
2020	An Interpretable and Sample Efficient Deep Kernel for Gaussian Process. Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis, Shuguang Cui
2020	Anchored Causal Inference in the Presence of Measurement Error. Basil Saeed, Anastasiya Belyaeva, Yuhao Wang, Caroline Uhler
2020	Automated Dependence Plots. David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
2020	Batch norm with entropic regularization turns deterministic autoencoders into generative models. Amur Ghose, Abdullah Rashwan, Pascal Poupart
2020	Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. Marko Järvenpää, Aki Vehtari, Pekka Marttinen
2020	Bayesian Online Prediction of Change Points. Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters
2020	Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits. Xinming Liu, Joseph Y. Halpern
2020	Bounding the expected run-time of nonconvex optimization with early stopping. Thomas Flynn, Kwangmin Yu, Abid Malik, Nicholas D'Imperio, Shinjae Yoo
2020	C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation. Arnab Kumar Mondal, Arnab Bhattacharjee, Sudipto Mukherjee, Himanshu Asnani, Sreeram Kannan, Prathosh A. P.
2020	Causal screening in dynamical systems. Søren Wengel Mogensen
2020	Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects. Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng
2020	Complete Dictionary Learning via ℓ Yifei Shen, Ye Xue, Jun Zhang, Khaled B. Letaief, Vincent Lau
2020	Complex Markov Logic Networks: Expressivity and Liftability. Ondrej Kuzelka
2020	Compositional uncertainty in deep Gaussian processes. Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
2020	Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. Joris M. Mooij, Tom Claassen
2020	Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples. Saeed Vahidian, Baharan Mirzasoleiman, Alexander Cloninger
2020	Deep Sigma Point Processes. Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2020	Deriving Bounds And Inequality Constraints Using Logical Relations Among Counterfactuals. Noam Finkelstein, Ilya Shpitser
2020	Differentially Private Small Dataset Release Using Random Projections. Lovedeep Gondara, Ke Wang
2020	Differentially Private Top-k Selection via Stability on Unknown Domain. Ricardo Silva Carvalho, Ke Wang, Lovedeep Gondara, Chunyan Miao
2020	Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Jakob Runge
2020	Distortion estimates for approximate Bayesian inference. Hanwen Xing, Geoff Nicholls, Jeong Lee
2020	Divergence-Based Motivation for Online EM and Combining Hidden Variable Models. Ehsan Amid, Manfred K. Warmuth
2020	Dueling Posterior Sampling for Preference-Based Reinforcement Learning. Ellen R. Novoseller, Yibing Wei, Yanan Sui, Yisong Yue, Joel Burdick
2020	Efficient Rollout Strategies for Bayesian Optimization. Eric Hans Lee, David Eriksson, David Bindel, Bolong Cheng, Mike Mccourt
2020	EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices. Jun Ho Yoon, Seyoung Kim
2020	Election Control by Manipulating Issue Significance. Andrew Estornell, Sanmay Das, Edith Elkind, Yevgeniy Vorobeychik
2020	Estimation Rates for Sparse Linear Cyclic Causal Models. Jan-Christian Hütter, Philippe Rigollet
2020	Evaluation of Causal Structure Learning Algorithms via Risk Estimation. Marco Eigenmann, Sach Mukherjee, Marloes H. Maathuis
2020	Exploration Analysis in Finite-Horizon Turn-based Stochastic Games. Jialian Li, Yichi Zhou, Tongzheng Ren, Jun Zhu
2020	Fair Contextual Multi-Armed Bandits: Theory and Experiments. Yifang Chen, Alex Cuellar, Haipeng Luo, Jignesh Modi, Heramb Nemlekar, Stefanos Nikolaidis
2020	Faster algorithms for Markov equivalence. Zhongyi Hu, Robin Evans
2020	Finite-Memory Near-Optimal Learning for Markov Decision Processes with Long-Run Average Reward. Jan Kretínský, Fabian Michel, Lukas Michel, Guillermo A. Pérez
2020	Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise. Yue Wang, Shaofeng Zou
2020	Flexible Approximate Inference via Stratified Normalizing Flows. Chris Cundy, Stefano Ermon
2020	Flexible Prior Elicitation via the Prior Predictive Distribution. Marcelo Hartmann, Georgi Agiashvili, Paul C. Bürkner, Arto Klami
2020	GPIRT: A Gaussian Process Model for Item Response Theory. JBrandon Duck-Mayr, Roman Garnett, Jacob M. Montgomery
2020	Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks. Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin
2020	Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. Aurélien Bibaut, Antoine Chambaz, Mark J. van der Laan
2020	Graphical continuous Lyapunov models. Gherardo Varando, Niels Richard Hansen
2020	Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry P. Vetrov
2020	Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. Hermanni Hälvä, Aapo Hyvärinen
2020	High Dimensional Discrete Integration over the Hypergrid. Raj Kumar Maity, Arya Mazumdar, Soumyabrata Pal
2020	How Private Are Commonly-Used Voting Rules? Ao Liu, Yun Lu, Lirong Xia, Vassilis Zikas
2020	IDA with Background Knowledge. Zhuangyan Fang, Yangbo He
2020	Identification and Estimation of Causal Effects Defined by Shift Interventions. Numair Sani, Jaron J. R. Lee, Ilya Shpitser
2020	Identifying causal effects in maximally oriented partially directed acyclic graphs. Emilija Perkovic
2020	Improved Vector Pruning in Exact Algorithms for Solving POMDPs. Eric A. Hansen, Thomas Bowman
2020	Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty. Hongjoon Ahn, Taesup Moon
2020	Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models. Zhijian Ou, Yunfu Song
2020	Kernel Conditional Moment Test via Maximum Moment Restriction. Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler
2020	Kidney Exchange with Inhomogeneous Edge Existence Uncertainty. Hoda Bidkhori, John Dickerson, Duncan C. McElfresh, Ke Ren
2020	Lagrangian Decomposition for Neural Network Verification. Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2020	Layering-MCMC for Structure Learning in Bayesian Networks. Jussi Viinikka, Mikko Koivisto
2020	Learning Behaviors with Uncertain Human Feedback. Xu He, Haipeng Chen, Bo An
2020	Learning Intrinsic Rewards as a Bi-Level Optimization Problem. Bradly C. Stadie, Lunjun Zhang, Jimmy Ba
2020	Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders. Sorawit Saengkyongam, Ricardo Silva
2020	Learning LWF Chain Graphs: A Markov Blanket Discovery Approach. Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
2020	Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect. Priyank Agrawal, Theja Tulabandhula
2020	Learning to learn generative programs with Memoised Wake-Sleep. Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum
2020	Locally Masked Convolution for Autoregressive Models. Ajay Jain, Pieter Abbeel, Deepak Pathak
2020	MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2020	MaskAAE: Latent space optimization for Adversarial Auto-Encoders. Arnab Kumar Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Himanshu Asnani, Parag Singla, Prathosh A. P.
2020	Measurement Dependence Inducing Latent Causal Models. Alex Markham, Moritz Grosse-Wentrup
2020	Mixed-Membership Stochastic Block Models for Weighted Networks. Adrien Dulac, Éric Gaussier, Christine Largeron
2020	Model-Augmented Conditional Mutual Information Estimation for Feature Selection. Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
2020	Multitask Soft Option Learning. Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson
2020	Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch. Michael Wan, Tanmay Gangwani, Jian Peng
2020	Neural Likelihoods via Cumulative Distribution Functions. Pawel M. Chilinski, Ricardo Silva
2020	No-regret Exploration in Contextual Reinforcement Learning. Aditya Modi, Ambuj Tewari
2020	Non Parametric Graph Learning for Bayesian Graph Neural Networks. Soumyasundar Pal, Saber Malekmohammadi, Florence Regol, Yingxue Zhang, Yishi Xu, Mark Coates
2020	Nonparametric Fisher Geometry with Application to Density Estimation. Andrew Holbrook, Shiwei Lan, Jeffrey Streets, Babak Shahbaba
2020	OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg
2020	On Counterfactual Explanations under Predictive Multiplicity. Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
2020	On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. Honghua Zhang, Steven Holtzen, Guy Van den Broeck
2020	On the design of consequential ranking algorithms. Behzad Tabibian, Vicenç Gómez, Abir De, Bernhard Schölkopf, Manuel Gomez Rodriguez
2020	One-Bit Compressed Sensing via One-Shot Hard Thresholding. Jie Shen
2020	Online Parameter-Free Learning of Multiple Low Variance Tasks. Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos
2020	Optimal Statistical Hypothesis Testing for Social Choice. Lirong Xia
2020	Ordering Variables for Weighted Model Integration. Vincent Derkinderen, Evert Heylen, Pedro Zuidberg Dos Martires, Samuel Kolb, Luc De Raedt
2020	PAC-Bayesian Contrastive Unsupervised Representation Learning. Kento Nozawa, Pascal Germain, Benjamin Guedj
2020	Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator. Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou, Xiaoning Qian
2020	Permutation-Based Causal Structure Learning with Unknown Intervention Targets. Chandler Squires, Yuhao Wang, Caroline Uhler
2020	PoRB-Nets: Poisson Process Radial Basis Function Networks. Beau Coker, Melanie Fernandes Pradier, Finale Doshi-Velez
2020	Popularity Agnostic Evaluation of Knowledge Graph Embeddings. Aisha Mohamed, Shameem Puthiya Parambath, Zoi Kaoudi, Ashraf Aboulnaga
2020	Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. Tárik S. Salem, Helge Langseth, Heri Ramampiaro
2020	Probabilistic Safety for Bayesian Neural Networks. Matthew Wicker, Luca Laurenti, Andrea Patane, Marta Kwiatkowska
2020	Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020 Ryan P. Adams, Vibhav Gogate
2020	Provably Efficient Third-Person Imitation from Offline Observation. Aaron Zweig, Joan Bruna
2020	Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. Tengyang Xie, Nan Jiang
2020	Randomized Exploration for Non-Stationary Stochastic Linear Bandits. Baekjin Kim, Ambuj Tewari
2020	Regret Analysis of Bandit Problems with Causal Background Knowledge. Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan
2020	Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems. Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar
2020	Relaxed Multivariate Bernoulli Distribution and Its Applications to Deep Generative Models. Xi Wang, Junming Yin
2020	Risk Bounds for Low Cost Bipartite Ranking. San Gultekin, John W. Paisley
2020	Robust Collective Classification against Structural Attacks. Kai Zhou, Yevgeniy Vorobeychik
2020	Robust Spatial-Temporal Incident Prediction. Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel J. Kochenderfer, Milind Tambe, Yevgeniy Vorobeychik
2020	Robust contrastive learning and nonlinear ICA in the presence of outliers. Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
2020	Robust k-means++. Amit Deshpande, Praneeth Kacham, Rameshwar Pratap
2020	Robust modal regression with direct gradient approximation of modal regression risk. Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
2020	Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes. Or Dinari, Oren Freifeld
2020	Selling Data at an Auction under Privacy Constraints. Mengxiao Zhang, Fernando Beltrán, Jiamou Liu
2020	Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful. Jingge Zhu
2020	Semi-bandit Optimization in the Dispersed Setting. Travis Dick, Wesley Pegden, Maria-Florina Balcan
2020	Semi-supervised Sequential Generative Models. Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood
2020	Semi-supervised learning, causality, and the conditional cluster assumption. Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf
2020	Sensor Placement for Spatial Gaussian Processes with Integral Observations. Krista Longi, Chang Rajani, Tom Sillanpää, Joni Mäkinen, Timo Rauhala, Ari Salmi, Edward Hæggström, Arto Klami
2020	Skewness Ranking Optimization for Personalized Recommendation. Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai
2020	Slice Sampling for General Completely Random Measures. Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
2020	Spectral Methods for Ranking with Scarce Data. Lalit Jain, Anna C. Gilbert, Umang Varma
2020	Stable Policy Optimization via Off-Policy Divergence Regularization. Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent
2020	Static and Dynamic Values of Computation in MCTS. Eren Sezener, Peter Dayan
2020	Statistically Efficient Greedy Equivalence Search. Max Chickering
2020	Stochastic Variational Inference for Dynamic Correlated Topic Models. Federico Tomasi, Praveen Chandar, Gal Levy-Fix, Mounia Lalmas-Roelleke, Zhenwen Dai
2020	Streaming Nonlinear Bayesian Tensor Decomposition. Zhimeng Pan, Zheng Wang, Shandian Zhe
2020	Structure Learning for Cyclic Linear Causal Models. Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu
2020	Submodular Bandit Problem Under Multiple Constraints. Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma
2020	Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. Tal Friedman, Guy Van den Broeck
2020	TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP. Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein
2020	Testing Goodness of Fit of Conditional Density Models with Kernels. Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf
2020	The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks. Sikun Yang, Heinz Koeppl
2020	The Indian Chefs Process. Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel van Gerven, François Laviolette
2020	Time Series Analysis using a Kernel based Multi-Modal Uncertainty Decomposition Framework. Rishabh Singh, José C. Príncipe
2020	Towards Threshold Invariant Fair Classification. Mingliang Chen, Min Wu
2020	Unknown mixing times in apprenticeship and reinforcement learning. Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour
2020	Verifying Individual Fairness in Machine Learning Models. Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha
2020	Walking on Two Legs: Learning Image Segmentation with Noisy Labels. Guohua Cheng, Hongli Ji, Yan Tian
2020	What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination. Laura Niss, Ambuj Tewari
2020	Zeroth Order Non-convex optimization with Dueling-Choice Bandits. Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski