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Affinity Workshop
Mon 8:55 Computation of Discrete Flows Over Networks via Constrained Wasserstein Barycenters
Ferran Arque, Cesar Uribe
Spotlight
Tue 5:35 Preferential Temporal Difference Learning
Nishanth Anand, Doina Precup
Spotlight
Tue 6:25 Towards Understanding Learning in Neural Networks with Linear Teachers
Roei Sarussi, Alon Brutzkus, Amir Globerson
Spotlight
Tue 6:35 Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu, Francisco R Ruiz, Liping Liu
Spotlight
Tue 6:45 Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
Xuhui Fan, Bin Li, Yaqiong Li, Scott SIsson
Spotlight
Tue 7:25 GLSearch: Maximum Common Subgraph Detection via Learning to Search
Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang
Spotlight
Tue 7:35 MSA Transformer
Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, Alexander Rives
Poster
Tue 9:00 Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu, Francisco R Ruiz, Liping Liu
Poster
Tue 9:00 Preferential Temporal Difference Learning
Nishanth Anand, Doina Precup
Poster
Tue 9:00 MSA Transformer
Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, Alexander Rives
Poster
Tue 9:00 Towards Understanding Learning in Neural Networks with Linear Teachers
Roei Sarussi, Alon Brutzkus, Amir Globerson
Poster
Tue 9:00 GLSearch: Maximum Common Subgraph Detection via Learning to Search
Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang
Poster
Tue 9:00 Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
Xuhui Fan, Bin Li, Yaqiong Li, Scott SIsson
Oral
Tue 17:00 Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Shariq Iqbal, Christian Schroeder, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha
Spotlight
Tue 17:25 The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Florian List
Spotlight
Tue 17:30 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
Spotlight
Tue 17:40 High Confidence Generalization for Reinforcement Learning
James Kostas, Yash Chandak, Scott Jordan, Georgios Theocharous, Philip Thomas
Spotlight
Tue 19:20 Oblivious Sketching-based Central Path Method for Linear Programming
Zhao Song, Zheng Yu
Spotlight
Tue 19:40 RRL: Resnet as representation for Reinforcement Learning
Rutav Shah, Vikash Kumar
Spotlight
Tue 19:40 Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
Neha Wadia, Daniel Duckworth, Samuel Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein
Poster
Tue 21:00 RRL: Resnet as representation for Reinforcement Learning
Rutav Shah, Vikash Kumar
Poster
Tue 21:00 The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Florian List
Poster
Tue 21:00 Oblivious Sketching-based Central Path Method for Linear Programming
Zhao Song, Zheng Yu
Poster
Tue 21:00 Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
Neha Wadia, Daniel Duckworth, Samuel Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein
Poster
Tue 21:00 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls
Poster
Tue 21:00 Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Shariq Iqbal, Christian Schroeder, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha
Poster
Tue 21:00 High Confidence Generalization for Reinforcement Learning
James Kostas, Yash Chandak, Scott Jordan, Georgios Theocharous, Philip Thomas
Oral
Wed 5:00 Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison
Oral Session
Wed 5:00 Probabilistic Methods 1
Spotlight
Wed 5:20 Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen, CHAOJIE WANG, Wenchao Chen, yewen li, Jie Ren, Mingyuan Zhou
Spotlight
Wed 5:25 SMG: A Shuffling Gradient-Based Method with Momentum
Trang Tran, Lam Nguyen, Quoc Tran-Dinh
Spotlight
Wed 5:30 Estimating $\alpha$-Rank from A Few Entries with Low Rank Matrix Completion
Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang
Spotlight
Wed 5:30 XOR-CD: Linearly Convergent Constrained Structure Generation
Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue
Spotlight
Wed 5:35 ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
Alek Dimitriev, Mingyuan Zhou
Spotlight
Wed 5:45 Nonparametric Hamiltonian Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
Spotlight
Wed 6:35 Tractable structured natural-gradient descent using local parameterizations
Wu Lin, Frank Nielsen, Khan Emtiyaz, Mark Schmidt
Spotlight
Wed 6:40 Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Difan Zou, Spencer Frei, Quanquan Gu
Oral
Wed 7:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Oral
Wed 7:00 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake Woodworth, Nati Srebro, Amir Globerson, Daniel Soudry
Spotlight
Wed 7:25 Necessary and sufficient conditions for causal feature selection in time series with latent common causes
Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing
Spotlight
Wed 7:30 Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh
Poster
Wed 9:00 Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
Difan Zou, Spencer Frei, Quanquan Gu
Poster
Wed 9:00 SMG: A Shuffling Gradient-Based Method with Momentum
Trang Tran, Lam Nguyen, Quoc Tran-Dinh
Poster
Wed 9:00 Necessary and sufficient conditions for causal feature selection in time series with latent common causes
Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing
Poster
Wed 9:00 Inferring serial correlation with dynamic backgrounds
Song Wei, Yao Xie, Dobromir Rahnev
Poster
Wed 9:00 Estimating $\alpha$-Rank from A Few Entries with Low Rank Matrix Completion
Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang
Poster
Wed 9:00 Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison
Poster
Wed 9:00 Nonparametric Hamiltonian Monte Carlo
Carol Mak, Fabian Zaiser, Luke Ong
Poster
Wed 9:00 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake Woodworth, Nati Srebro, Amir Globerson, Daniel Soudry
Poster
Wed 9:00 Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen, CHAOJIE WANG, Wenchao Chen, yewen li, Jie Ren, Mingyuan Zhou
Poster
Wed 9:00 XOR-CD: Linearly Convergent Constrained Structure Generation
Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue
Poster
Wed 9:00 ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
Alek Dimitriev, Mingyuan Zhou
Poster
Wed 9:00 Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh
Poster
Wed 9:00 Tractable structured natural-gradient descent using local parameterizations
Wu Lin, Frank Nielsen, Khan Emtiyaz, Mark Schmidt
Spotlight
Wed 17:20 Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions
Pierre Alquier
Spotlight
Wed 17:25 Consensus Control for Decentralized Deep Learning
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian Stich
Spotlight
Wed 17:40 Probabilistic Programs with Stochastic Conditioning
David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang
Oral
Wed 18:00 RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter, Zachary Lipton
Oral
Wed 18:00 Discriminative Complementary-Label Learning with Weighted Loss
Yi Gao, Min-Ling Zhang
Spotlight
Wed 18:20 Gaussian Process-Based Real-Time Learning for Safety Critical Applications
Armin Lederer, Alejandro Ordóñez Conejo, Korbinian Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche
Spotlight
Wed 19:20 DORO: Distributional and Outlier Robust Optimization
Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar
Spotlight
Wed 19:25 Optimal Off-Policy Evaluation from Multiple Logging Policies
Nathan Kallus, Yuta Saito, Masatoshi Uehara
Spotlight
Wed 19:30 Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Spotlight
Wed 19:35 Exact Optimization of Conformal Predictors via Incremental and Decremental Learning
Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi
Spotlight
Wed 19:35 Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
yue cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen
Spotlight
Wed 19:45 How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
Amanda Gentzel, Purva Pruthi, David Jensen
Poster
Wed 21:00 Discriminative Complementary-Label Learning with Weighted Loss
Yi Gao, Min-Ling Zhang
Poster
Wed 21:00 DORO: Distributional and Outlier Robust Optimization
Runtian Zhai, Chen Dan, Zico Kolter, Pradeep Ravikumar
Poster
Wed 21:00 Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Poster
Wed 21:00 RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg, Sivaraman Balakrishnan, Zico Kolter, Zachary Lipton
Poster
Wed 21:00 How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
Amanda Gentzel, Purva Pruthi, David Jensen
Poster
Wed 21:00 Consensus Control for Decentralized Deep Learning
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian Stich
Poster
Wed 21:00 Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions
Pierre Alquier
Poster
Wed 21:00 Optimal Off-Policy Evaluation from Multiple Logging Policies
Nathan Kallus, Yuta Saito, Masatoshi Uehara
Poster
Wed 21:00 Gaussian Process-Based Real-Time Learning for Safety Critical Applications
Armin Lederer, Alejandro Ordóñez Conejo, Korbinian Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche
Poster
Wed 21:00 Probabilistic Programs with Stochastic Conditioning
David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang
Poster
Wed 21:00 Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
yue cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang Shen
Oral
Thu 5:00 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Oral
Thu 5:00 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Artem Artemev, David Burt, Mark van der Wilk
Oral Session
Thu 5:00 Probabilistic Methods 2
Spotlight
Thu 5:20 Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen, Søren Hauberg
Spotlight
Thu 5:20 DAGs with No Curl: An Efficient DAG Structure Learning Approach
Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji
Spotlight
Thu 5:25 Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor, Theofanis Karaletsos, Thang Bui
Spotlight
Thu 5:25 Generalized Doubly Reparameterized Gradient Estimators
Matthias Bauer, Andriy Mnih
Spotlight
Thu 5:30 Whittle Networks: A Deep Likelihood Model for Time Series
Zhongjie Yu, Fabrizio Ventola, Kristian Kersting
Spotlight
Thu 5:30 Robust Testing and Estimation under Manipulation Attacks
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
Spotlight
Thu 5:30 Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone
Spotlight
Thu 5:35 BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
Dominic Danks, Christopher Yau
Spotlight
Thu 5:35 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V Bonilla, Theo Damoulas, Terry Lyons
Spotlight
Thu 5:35 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients
Difan Zou, Quanquan Gu
Spotlight
Thu 5:40 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
Spotlight
Thu 5:45 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Wilson, Roger Grosse
Spotlight
Thu 5:45 Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data
Sung Woo Park, Junseok Kwon
Oral
Thu 6:00 Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
Adam Foster, Desi Ivanova, ILYAS MALIK, Tom Rainforth
Spotlight
Thu 6:20 Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
Gustavo Malkomes, Harvey Cheng, Eric Lee, Michael McCourt
Spotlight
Thu 6:25 Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
Spotlight
Thu 6:25 Learning to Generate Noise for Multi-Attack Robustness
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Spotlight
Thu 6:30 Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
James Brofos, Roy Lederman
Spotlight
Thu 6:35 Streaming Bayesian Deep Tensor Factorization
Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe
Spotlight
Thu 6:40 Efficient Training of Robust Decision Trees Against Adversarial Examples
Daniël Vos, Sicco Verwer
Spotlight
Thu 6:40 Active Learning of Continuous-time Bayesian Networks through Interventions
Dominik Linzner, Heinz Koeppl
Oral Session
Thu 7:00 Probabilistic Methods 3
Oral
Thu 7:00 DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
Oral
Thu 7:00 Annealed Flow Transport Monte Carlo
Michael Arbel, Alexander Matthews, Arnaud Doucet
Spotlight
Thu 7:20 Nonparametric Decomposition of Sparse Tensors
Conor Tillinghast, Shandian Zhe
Spotlight
Thu 7:25 Parallel tempering on optimized paths
Saif Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté
Spotlight
Thu 7:30 Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
Ethan Perez, Douwe Kiela, Kyunghyun Cho
Spotlight
Thu 7:35 Matrix Completion with Model-free Weighting
Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan
Spotlight
Thu 7:35 Geometric convergence of elliptical slice sampling
Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk
Spotlight
Thu 7:40 Few-Shot Conformal Prediction with Auxiliary Tasks
Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay
Spotlight
Thu 7:40 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Spotlight
Thu 7:45 On the difficulty of unbiased alpha divergence minimization
Tomas Geffner, Justin Domke
Spotlight
Thu 7:45 A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization
Andrew Campbell, Wenlong Chen, Vincent Stimper, Jose Miguel Hernandez-Lobato, Yichuan Zhang
Poster
Thu 9:00 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Wilson, Roger Grosse
Poster
Thu 9:00 Active Learning of Continuous-time Bayesian Networks through Interventions
Dominik Linzner, Heinz Koeppl
Poster
Thu 9:00 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 Exact Optimization of Conformal Predictors via Incremental and Decremental Learning
Giovanni Cherubin, Konstantinos Chatzikokolakis, Martin Jaggi
Poster
Thu 9:00 Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
Adam Foster, Desi Ivanova, ILYAS MALIK, Tom Rainforth
Poster
Thu 9:00 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients
Difan Zou, Quanquan Gu
Poster
Thu 9:00 Learning to Generate Noise for Multi-Attack Robustness
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Poster
Thu 9:00 Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor, Theofanis Karaletsos, Thang Bui
Poster
Thu 9:00 Robust Testing and Estimation under Manipulation Attacks
Jayadev Acharya, Ziteng Sun, Huanyu Zhang
Poster
Thu 9:00 Nonparametric Decomposition of Sparse Tensors
Conor Tillinghast, Shandian Zhe
Poster
Thu 9:00 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Artem Artemev, David Burt, Mark van der Wilk
Poster
Thu 9:00 DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
Poster
Thu 9:00 Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone
Poster
Thu 9:00 Efficient Training of Robust Decision Trees Against Adversarial Examples
Daniël Vos, Sicco Verwer
Poster
Thu 9:00 Whittle Networks: A Deep Likelihood Model for Time Series
Zhongjie Yu, Fabrizio Ventola, Kristian Kersting
Poster
Thu 9:00 Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data
Sung Woo Park, Junseok Kwon
Poster
Thu 9:00 Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen, Søren Hauberg
Poster
Thu 9:00 On the difficulty of unbiased alpha divergence minimization
Tomas Geffner, Justin Domke
Poster
Thu 9:00 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
Poster
Thu 9:00 Towards Practical Mean Bounds for Small Samples
My Phan, Philip Thomas, Erik Learned-Miller
Poster
Thu 9:00 Parallel tempering on optimized paths
Saif Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté
Poster
Thu 9:00 Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
James Brofos, Roy Lederman
Poster
Thu 9:00 Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
Ethan Perez, Douwe Kiela, Kyunghyun Cho
Poster
Thu 9:00 Streaming Bayesian Deep Tensor Factorization
Shikai Fang, Zheng Wang, Zhimeng Pan, Ji Liu, Shandian Zhe
Poster
Thu 9:00 Annealed Flow Transport Monte Carlo
Michael Arbel, Alexander Matthews, Arnaud Doucet
Poster
Thu 9:00 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V Bonilla, Theo Damoulas, Terry Lyons
Poster
Thu 9:00 Few-Shot Conformal Prediction with Auxiliary Tasks
Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay
Poster
Thu 9:00 Matrix Completion with Model-free Weighting
Jiayi Wang, Raymond K. W. Wong, Xiaojun Mao, Kwun Chuen Gary Chan
Poster
Thu 9:00 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet
Poster
Thu 9:00 Geometric convergence of elliptical slice sampling
Viacheslav Natarovskii, Daniel Rudolf, Björn Sprungk
Poster
Thu 9:00 Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
Poster
Thu 9:00 Generalized Doubly Reparameterized Gradient Estimators
Matthias Bauer, Andriy Mnih
Poster
Thu 9:00 BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
Dominic Danks, Christopher Yau
Poster
Thu 9:00 A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization
Andrew Campbell, Wenlong Chen, Vincent Stimper, Jose Miguel Hernandez-Lobato, Yichuan Zhang
Poster
Thu 9:00 DAGs with No Curl: An Efficient DAG Structure Learning Approach
Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji
Poster
Thu 9:00 Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
Gustavo Malkomes, Harvey Cheng, Eric Lee, Michael McCourt
Oral
Thu 17:00 Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
Arkopal Dutt, Andrey Lokhov, Marc Vuffray, Sidhant Misra
Oral
Thu 17:00 Probabilistic Generating Circuits
Honghua Zhang, Brendan Juba, Guy Van den Broeck
Oral Session
Thu 17:00 Probabilistic Methods 4
Spotlight
Thu 17:20 Model Fusion for Personalized Learning
Thanh Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
Spotlight
Thu 17:20 Objective Bound Conditional Gaussian Process for Bayesian Optimization
Taewon Jeong, Heeyoung Kim
Spotlight
Thu 17:25 Crystallization Learning with the Delaunay Triangulation
Jiaqi Gu, Guosheng Yin
Spotlight
Thu 17:25 Automatic variational inference with cascading flows
Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
Spotlight
Thu 17:30 Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning
Yonghan Jung, Jin Tian, Elias Bareinboim
Spotlight
Thu 17:35 Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John Cunningham
Spotlight
Thu 17:35 Simple and Effective VAE Training with Calibrated Decoders
Oleg Rybkin, Kostas Daniilidis, Sergey Levine
Spotlight
Thu 17:35 Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis
Spotlight
Thu 17:40 SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
Yu Wang, Alfred Hero
Spotlight
Thu 17:40 Context-Aware Online Collective Inference for Templated Graphical Models
Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor
Spotlight
Thu 17:45 Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
Elias Chaibub Neto
Spotlight
Thu 17:45 Black-box density function estimation using recursive partitioning
Erik Bodin, Zhenwen Dai, Neill Campbell, Carl Henrik Ek
Oral
Thu 18:00 Integer Programming for Causal Structure Learning in the Presence of Latent Variables
Rui Chen, Sanjeeb Dash, Tian Gao
Oral
Thu 18:00 Solving high-dimensional parabolic PDEs using the tensor train format
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
Oral Session
Thu 18:00 Probabilistic Methods 5
Spotlight
Thu 18:25 SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples
Anshoo Tandon, Aldric Han, Vincent Tan
Spotlight
Thu 18:25 CountSketches, Feature Hashing and the Median of Three
Kasper Green Larsen, Rasmus Pagh, Jakub Tětek
Spotlight
Thu 18:35 On Recovering from Modeling Errors Using Testing Bayesian Networks
Haiying Huang, Adnan Darwiche
Spotlight
Thu 18:40 Towards Practical Mean Bounds for Small Samples
My Phan, Philip Thomas, Erik Learned-Miller
Spotlight
Thu 18:45 Monte Carlo Variational Auto-Encoders
Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
Oral Session
Thu 19:00 Probabilistic Methods 6
Oral
Thu 19:00 Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
Oral
Thu 19:20 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Wilson
Spotlight
Thu 19:25 Link Prediction with Persistent Homology: An Interactive View
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
Spotlight
Thu 19:40 Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Mateusz Wilinski, Andrey Lokhov
Spotlight
Thu 19:45 Marginalized Stochastic Natural Gradients for Black-Box Variational Inference
Geng Ji, Debora Sujono, Erik Sudderth
Spotlight
Thu 20:30 A Proxy Variable View of Shared Confounding
Yixin Wang, David Blei
Spotlight
Thu 20:30 Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai, Selwyn Gomes, Jonathan Scarlett
Spotlight
Thu 20:35 Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael Hutchinson, Yee-Whye Teh
Spotlight
Thu 20:35 Budgeted Heterogeneous Treatment Effect Estimation
Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou
Spotlight
Thu 20:35 Optimal Thompson Sampling strategies for support-aware CVaR bandits
Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric-Ambrym Maillard
Spotlight
Thu 20:40 Value-at-Risk Optimization with Gaussian Processes
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Spotlight
Thu 20:40 Permutation Weighting
David Arbour, Drew Dimmery, Arjun Sondhi
Spotlight
Thu 20:40 Regularizing towards Causal Invariance: Linear Models with Proxies
Mike Oberst, Nikolaj Thams, Jonas Peters, David Sontag
Spotlight
Thu 20:45 High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig
Spotlight
Thu 20:45 Valid Causal Inference with (Some) Invalid Instruments
Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
Spotlight
Thu 20:45 A Language for Counterfactual Generative Models
zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama
Spotlight
Thu 20:50 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya
Spotlight
Thu 20:50 Operationalizing Complex Causes: A Pragmatic View of Mediation
Limor Gultchin, David Watson, Matt J. Kusner, Ricardo Silva
Spotlight
Thu 20:50 Bayesian Structural Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
Poster
Thu 21:00 Integer Programming for Causal Structure Learning in the Presence of Latent Variables
Rui Chen, Sanjeeb Dash, Tian Gao
Poster
Thu 21:00 Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
Poster
Thu 21:00 Valid Causal Inference with (Some) Invalid Instruments
Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
Poster
Thu 21:00 Permutation Weighting
David Arbour, Drew Dimmery, Arjun Sondhi
Poster
Thu 21:00 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya
Poster
Thu 21:00 Objective Bound Conditional Gaussian Process for Bayesian Optimization
Taewon Jeong, Heeyoung Kim
Poster
Thu 21:00 Budgeted Heterogeneous Treatment Effect Estimation
Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou
Poster
Thu 21:00 High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig
Poster
Thu 21:00 Model Fusion for Personalized Learning
Thanh Lam, Nghia Hoang, Bryan Kian Hsiang Low, Patrick Jaillet
Poster
Thu 21:00 Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
Elias Chaibub Neto
Poster
Thu 21:00 Statistical Estimation from Dependent Data
Vardis Kandiros, Yuval Dagan, Nishanth Dikkala, Surbhi Goel, Constantinos Daskalakis
Poster
Thu 21:00 Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning
Yonghan Jung, Jin Tian, Elias Bareinboim
Poster
Thu 21:00 Automatic variational inference with cascading flows
Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
Poster
Thu 21:00 Monte Carlo Variational Auto-Encoders
Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
Poster
Thu 21:00 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Wilson
Poster
Thu 21:00 Context-Aware Online Collective Inference for Templated Graphical Models
Charles Dickens, Connor Pryor, Eriq Augustine, Alexander Miller, Lise Getoor
Poster
Thu 21:00 Crystallization Learning with the Delaunay Triangulation
Jiaqi Gu, Guosheng Yin
Poster
Thu 21:00 Probabilistic Generating Circuits
Honghua Zhang, Brendan Juba, Guy Van den Broeck
Poster
Thu 21:00 A Proxy Variable View of Shared Confounding
Yixin Wang, David Blei
Poster
Thu 21:00 Value-at-Risk Optimization with Gaussian Processes
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Poster
Thu 21:00 Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Mateusz Wilinski, Andrey Lokhov
Poster
Thu 21:00 Simple and Effective VAE Training with Calibrated Decoders
Oleg Rybkin, Kostas Daniilidis, Sergey Levine
Poster
Thu 21:00 Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John Cunningham
Poster
Thu 21:00 SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples
Anshoo Tandon, Aldric Han, Vincent Tan
Poster
Thu 21:00 Black-box density function estimation using recursive partitioning
Erik Bodin, Zhenwen Dai, Neill Campbell, Carl Henrik Ek
Poster
Thu 21:00 CountSketches, Feature Hashing and the Median of Three
Kasper Green Larsen, Rasmus Pagh, Jakub Tětek
Poster
Thu 21:00 Marginalized Stochastic Natural Gradients for Black-Box Variational Inference
Geng Ji, Debora Sujono, Erik Sudderth
Poster
Thu 21:00 On Recovering from Modeling Errors Using Testing Bayesian Networks
Haiying Huang, Adnan Darwiche
Poster
Thu 21:00 Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai, Selwyn Gomes, Jonathan Scarlett
Poster
Thu 21:00 Regularizing towards Causal Invariance: Linear Models with Proxies
Mike Oberst, Nikolaj Thams, Jonas Peters, David Sontag
Poster
Thu 21:00 Operationalizing Complex Causes: A Pragmatic View of Mediation
Limor Gultchin, David Watson, Matt J. Kusner, Ricardo Silva
Poster
Thu 21:00 Solving high-dimensional parabolic PDEs using the tensor train format
Lorenz Richter, Leon Sallandt, Nikolas Nüsken
Poster
Thu 21:00 Bayesian Structural Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
Poster
Thu 21:00 Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael Hutchinson, Yee-Whye Teh
Poster
Thu 21:00 A Language for Counterfactual Generative Models
zenna Tavares, James Koppel, Xin Zhang, Ria Das, Armando Solar-Lezama
Poster
Thu 21:00 SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
Yu Wang, Alfred Hero
Poster
Thu 21:00 Optimal Thompson Sampling strategies for support-aware CVaR bandits
Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric-Ambrym Maillard
Poster
Thu 21:00 Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
Arkopal Dutt, Andrey Lokhov, Marc Vuffray, Sidhant Misra
Poster
Thu 21:00 Link Prediction with Persistent Homology: An Interactive View
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen
Workshop
Sat 12:23 Selective Focusing Learning in Conditional GANs
Kyeongbo Kong, Kyunghun Kim, Woo-jin Song, Suk-Ju Kang
Workshop
Selective Focusing Learning in Conditional GANs
Kyeongbo Kong, Kyunghun Kim, Woo-jin Song, Suk-Ju Kang
Workshop
Counterfactual Explanations for Graph Neural Networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, Fabrizio Silvestri
Workshop
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
Benjamin Spetter-Goldstein, Nataniel Ruiz, Sarah Bargal
Workshop
A unified PAC-Bayesian framework for machine unlearning via information risk minimization
Sharu Jose, Osvaldo Simeone
Workshop
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks
Eshaan Nichani, Adit Radhakrishnan, Caroline Uhler