46 Results

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 Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu
Poster
Tue 8:00 Neural Clustering Processes
Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, Department of Statistics Liam Paninski
Poster
Tue 9:00 Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Peter 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 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 12:00 Meta-Learning with Shared Amortized Variational Inference
Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari
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 14:00 Thompson Sampling via Local Uncertainty
Zhendong Wang, Mingyuan Zhou
Poster
Tue 18:00 Accelerating the diffusion-based ensemble sampling by non-reversible dynamics
Futoshi Futami, Issei Sato, Masashi Sugiyama
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 VFlow: More Expressive Generative Flows with Variational Data Augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian
Poster
Wed 5:00 On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Hengrui Cai, Wenbin Lu, Rui Song
Poster
Wed 8:00 A general recurrent state space framework for modeling neural dynamics during decision-making
David Zoltowski, Jonathan Pillow, Scott Linderman
Poster
Wed 8:00 Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William Macready
Poster
Wed 8:00 Self-Modulating Nonparametric Event-Tensor Factorization
Zheng Wang, Xinqi Chu, Shandian Zhe
Poster
Wed 10:00 Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Poster
Wed 10:00 Handling the Positive-Definite Constraint in the Bayesian Learning Rule
Wu Lin, Mark Schmidt, Emti Khan
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 Automatic Reparameterisation of Probabilistic Programs
Maria Gorinova, Dave Moore, Matt Hoffman
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 13:00 Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth, Harald Oberhauser
Poster
Wed 14:00 Sparse Gaussian Processes with Spherical Harmonic Features
Vincent Dutordoir, Nicolas Durrande, James Hensman
Poster
Wed 15:00 LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
Vlad Niculae, Andre Filipe Torres Martins
Poster
Wed 16:00 Spread Divergence
Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber
Poster
Thu 6:00 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics
Matt Hoffman, Yian Ma
Poster
Thu 6:00 Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag
Poster
Thu 6:00 Stochastic Flows and Geometric Optimization on the Orthogonal Group
Krzysztof Choromanski, David Cheikhi, Jared Quincy Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani
Poster
Thu 6:00 Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Tianju Xue, Alex Beatson, Sigrid Adriaenssens , Ryan P. Adams
Poster
Thu 7:00 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Michael Zhu, Chang Liu, Jun Zhu
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 8:00 Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong, Bryan Seybold, Kevin Murphy, Hung Bui
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 Stochastic Differential Equations with Variational Wishart Diffusions
Martin Jørgensen, Marc Deisenroth, Hugh Salimbeni
Poster
Thu 14:00 Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg
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 14:00 Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng, Roman Bachmann, Emti Khan
Poster
Thu 14:00 The continuous categorical: a novel simplex-valued exponential family
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John Cunningham
Poster
Thu 17:00 On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes
Naoto Ohsaka, Tatsuya Matsuoka