25 Results

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 7:00 Faster Graph Embeddings via Coarsening
Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang
Poster
Tue 8:00 Meta-learning for Mixed Linear Regression
Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh
Poster
Tue 10:00 Error Estimation for Sketched SVD via the Bootstrap
Miles Lopes, N. Benjamin Erichson, Michael Mahoney
Poster
Tue 12:00 On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios
Poster
Tue 13:00 Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, Gilles Louppe
Poster
Tue 18:00 Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Futoshi Futami, Issei Sato, Masashi Sugiyama
Poster
Wed 8:00 Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William Macready
Poster
Wed 8:00 Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu
Poster
Wed 8:00 Stochastic Gradient and Langevin Processes
Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan
Poster
Wed 10:00 Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin, Mark Schmidt, Emti Khan
Poster
Wed 10:00 Batch Stationary Distribution Estimation
Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans
Poster
Wed 13:00 Involutive MCMC: a Unifying Framework
Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
Poster
Wed 13:00 Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Miles Lopes, Jessie X.T. Chen
Poster
Thu 6:00 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matt Hoffman, Yian Ma
Poster
Thu 7:00 Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke
Poster
Thu 7:00 Variational Inference for Sequential Data with Future Likelihood Estimates
Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim
Poster
Thu 7:00 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Michael Zhu, Chang Liu, Jun Zhu
Poster
Thu 8:00 Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
Poster
Thu 8:00 Model Fusion with Kullback--Leibler Divergence
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon
Poster
Thu 12:00 Double-Loop Unadjusted Langevin Algorithm
Paul Rolland, Armin Eftekhari, Ali Kavis, Volkan Cevher
Poster
Thu 13:00 The Boomerang Sampler
Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts
Poster
Thu 14:00 From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause
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 On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka, Tatsuya Matsuoka