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Poster
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Tue 7:00 |
Recurrent Hierarchical Topic-Guided RNN for Language Generation Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou |
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Poster
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Tue 7:00 |
Variational Bayesian Quantization Yibo Yang, Robert Bamler, Stephan Mandt |
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Poster
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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 |
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Poster
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Tue 8:00 |
Nonparametric Score Estimators Yuhao Zhou, Jiaxin Shi, Jun Zhu |
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Poster
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Tue 8:00 |
Neural Clustering Processes Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, Department of Statistics Liam Paninski |
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Poster
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Tue 9:00 |
Variable Skipping for Autoregressive Range Density Estimation Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Peter Chen |
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Poster
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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 |
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Poster
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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 |
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Poster
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Tue 12:00 |
Meta-Learning with Shared Amortized Variational Inference Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari |
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Poster
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Tue 12:00 |
On Contrastive Learning for Likelihood-free Inference Conor Durkan, Iain Murray, George Papamakarios |
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Poster
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Tue 13:00 |
Likelihood-free MCMC with Amortized Approximate Ratio Estimators Joeri Hermans, Volodimir Begy, Gilles Louppe |
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Poster
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Tue 14:00 |
Thompson Sampling via Local Uncertainty Zhendong Wang, Mingyuan Zhou |
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Poster
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Tue 18:00 |
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics Futoshi Futami, Issei Sato, Masashi Sugiyama |
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Poster
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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 |
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Poster
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Wed 5:00 |
VFlow: More Expressive Generative Flows with Variational Data Augmentation Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian |
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Poster
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Wed 5:00 |
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies Hengrui Cai, Wenbin Lu, Rui Song |
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Poster
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Wed 8:00 |
A general recurrent state space framework for modeling neural dynamics during decision-making David Zoltowski, Jonathan Pillow, Scott Linderman |
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Poster
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Wed 8:00 |
Undirected Graphical Models as Approximate Posteriors Arash Vahdat, Evgeny Andriyash, William Macready |
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Poster
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Wed 8:00 |
Self-Modulating Nonparametric Event-Tensor Factorization Zheng Wang, Xinqi Chu, Shandian Zhe |
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Poster
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Wed 10:00 |
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
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Poster
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Wed 10:00 |
Handling the Positive-Definite Constraint in the Bayesian Learning Rule Wu Lin, Mark Schmidt, Emti Khan |
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Poster
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Wed 12:00 |
Estimating Model Uncertainty of Neural Networks in Sparse Information Form Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel |
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Poster
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Wed 12:00 |
Automatic Reparameterisation of Probabilistic Programs Maria Gorinova, Dave Moore, Matt Hoffman |
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Poster
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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 |
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Poster
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Wed 12:00 |
Convergence Rates of Variational Inference in Sparse Deep Learning Badr-Eddine Chérief-Abdellatif |
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Poster
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Wed 13:00 |
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Csaba Toth, Harald Oberhauser |
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Poster
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Wed 14:00 |
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir, Nicolas Durrande, James Hensman |
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Poster
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Wed 15:00 |
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction Vlad Niculae, Andre Filipe Torres Martins |
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Poster
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Wed 16:00 |
Spread Divergence Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber |
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Poster
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Thu 6:00 |
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics Matt Hoffman, Yian Ma |
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Poster
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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 |
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Poster
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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 |
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Poster
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Thu 6:00 |
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization Tianju Xue, Alex Beatson, Sigrid Adriaenssens , Ryan P. Adams |
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Poster
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Thu 7:00 |
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference Michael Zhu, Chang Liu, Jun Zhu |
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Poster
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Thu 7:00 |
Provable Smoothness Guarantees for Black-Box Variational Inference Justin Domke |
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Poster
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Thu 7:00 |
Variational Inference for Sequential Data with Future Likelihood Estimates Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim |
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Poster
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Thu 8:00 |
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems Zhe Dong, Bryan Seybold, Kevin Murphy, Hung Bui |
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Poster
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Thu 8:00 |
Model Fusion with Kullback--Leibler Divergence Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon |
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Poster
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Thu 12:00 |
Inter-domain Deep Gaussian Processes Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal |
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Poster
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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 |
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Poster
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Thu 13:00 |
Stochastic Differential Equations with Variational Wishart Diffusions Martin Jørgensen, Marc Deisenroth, Hugh Salimbeni |
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Poster
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Thu 14:00 |
Variational Autoencoders with Riemannian Brownian Motion Priors Dimitris Kalatzis, David Eklund, Georgios Arvanitidis, Søren Hauberg |
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Poster
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Thu 14:00 |
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause |
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Poster
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Thu 14:00 |
Training Binary Neural Networks using the Bayesian Learning Rule Xiangming Meng, Roman Bachmann, Emti Khan |
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Poster
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Thu 14:00 |
The continuous categorical: a novel simplex-valued exponential family Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John Cunningham |
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Poster
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Thu 17:00 |
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes Naoto Ohsaka, Tatsuya Matsuoka |