42 Results

Poster
Tue 7:00 Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Richard Zhang, Daniel Golovin
Poster
Tue 7:00 Recurrent Hierarchical Topic-Guided RNN for Language Generation
Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou
Poster
Tue 7:00 Variational Bayesian Quantization
Yibo Yang, Robert Bamler, Stephan Mandt
Poster
Tue 7:00 Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent
Poster
Tue 8:00 Meta-learning for Mixed Linear Regression
Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh
Poster
Tue 9:00 Being Bayesian about Categorical Probability
Taejong Joo, Uijung Chung, Min-Gwan Seo
Poster
Tue 9:00 Variance Reduction in Stochastic Particle-Optimization Sampling
Jianyi Zhang, Yang Zhao, Changyou Chen
Poster
Tue 9:00 How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
Poster
Tue 9:00 On Semi-parametric Inference for BART
Veronika Rockova
Poster
Tue 9:00 Source Separation with Deep Generative Priors
Vivek Jayaram, John Thickstun
Poster
Tue 10:00 Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Schober, Philipp Hennig
Poster
Tue 11:00 Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Steven Kleinegesse, Michael Gutmann
Poster
Tue 11:00 Near-linear time Gaussian process optimization with adaptive batching and resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Poster
Tue 12:00 On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios
Poster
Tue 13:00 Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Ahmed Alaa, Mihaela van der Schaar
Poster
Tue 13:00 Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, Gilles Louppe
Poster
Tue 14:00 Thompson Sampling via Local Uncertainty
Zhendong Wang, Mingyuan Zhou
Poster
Tue 18:00 Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama
Poster
Wed 5:00 Robust Bayesian Classification Using An Optimistic Score Ratio
Viet Anh Nguyen, Nian Si, Jose Blanchet
Poster
Wed 5:00 Sequential Cooperative Bayesian Inference
Junqi Wang, Pei Wang, Patrick Shafto
Poster
Wed 5:00 Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations
Stephen Keeley, David Zoltowski, Yiyi Yu, Spencer Smith, Jonathan Pillow
Poster
Wed 5:00 Online Bayesian Moment Matching based SAT Solver Heuristics
Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh
Poster
Wed 8:00 The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, David Wipf
Poster
Wed 8:00 Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William Macready
Poster
Wed 8:00 Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck
Poster
Wed 9:00 SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong, Jimeng Sun, Chao Zhang
Poster
Wed 9:00 Ordinal Non-negative Matrix Factorization for Recommendation
Olivier Gouvert, Thomas Oberlin, Cedric Fevotte
Poster
Wed 12:00 Estimating Model Uncertainty of Neural Networks in Sparse Information Form
Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel
Poster
Wed 12:00 Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee-Whye Teh, Tom Rainforth
Poster
Wed 12:00 Convergence Rates of Variational Inference in Sparse Deep Learning
Badr-Eddine Chérief-Abdellatif
Poster
Wed 12:00 Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
Subho Banerjee, Saurabh Jha, Zbigniew Kalbarczyk, Ravishankar Iyer
Poster
Wed 14:00 Healing Products of Gaussian Process Experts
samuel cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth
Poster
Thu 6:00 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matt Hoffman, Yian Ma
Poster
Thu 6:00 BINOCULARS for efficient, nonmyopic sequential experimental design
Shali Jiang, Henry Chai, Javier Gonzalez, Roman Garnett
Poster
Thu 6:00 Causal Inference using Gaussian Processes with Structured Latent Confounders
Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka
Poster
Thu 8:00 Model Fusion with Kullback--Leibler Divergence
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon
Poster
Thu 12:00 Inter-domain Deep Gaussian Processes
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Poster
Thu 12:00 The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan Veeling, Linh Tran, Josh V Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
Poster
Thu 13:00 Projective Preferential Bayesian Optimization
Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski
Poster
Thu 13:00 The Boomerang Sampler
Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts
Poster
Thu 15:00 Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone, Matias Quiroz, Robert kohn, Mattias Villani, Minh-Ngoc Tran
Poster
Thu 17:00 Multi-objective Bayesian Optimization using Pareto-frontier Entropy
Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama