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46 Results
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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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Thu 8:00 |
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC Wei Deng · Qi Feng · Liyao Gao · Faming Liang · Guang Lin |
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Poster
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Tue 11:00 |
Near-linear time Gaussian process optimization with adaptive batching and resparsification Daniele Calandriello · Luigi Carratino · Alessandro Lazaric · Michal Valko · Lorenzo Rosasco |
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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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Wed 12:00 |
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters Subho Banerjee · Saurabh Jha · Zbigniew Kalbarczyk · Ravishankar Iyer |
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Poster
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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 |
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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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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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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 · Joshua V Dillon · Jasper Snoek · Stephan Mandt · Tim Salimans · Rodolphe Jenatton · Sebastian Nowozin |