Accepted Papers

Note: Papers listed here do not constitute as proceedings for this workshop.

To Trust Or Not To Trust A Classifier Heinrich Jiang, Been Kim, Maya Gupta
Ambient Hidden Space of Generative Adversarial Networks Xinhan Di, Pengqian Yu, Meng Tian
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
Deep Contextual Multi-armed Bandits Mark Collier, Hector Urdiales Llorens
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study John P. Lalor, Hao Wu, Tsendsuren Munkhdalai, Hong Yu
Approximate Empirical Bayes for Deep Neural Networks Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoff Gordon
Deep Matrix-variate Gaussian Processes Young-Jin Park, Piyush M. Tagade, Han-Lim Choi
Countdown Regression: Sharp and Calibrated Survival Predictions Anand Avati, Tony Duan, Kenneth Jung, Nigam Shah, Andrew Ng
Deep State Space Models for Unconditional Word Generation Florian Schmidt, Thomas Hofmann
Probabilistic Deep Learning using Random Sum-Product Networks Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
Soft Label Memorization-Generalization for Natural Language Inference John P. Lalor, Hao Wu, Hong Yu
Amortized Monte Carlo Integration Adam Golinski, Yee Whye Teh, Frank Wood, Tom Rainforth
Probabilistic Meta-Representations Of Neural Networks Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah
Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs Wesley Maddox, Timur Garipov, Pavel Izmailov, Andrew Gordon Wilson
Learn to Adapt Uncertainty with Stochastic Activation Actor-Critic Methods Wenling Shang, Douwe van der Wal, Herke van Hoof, Max Welling
Variational Compressive Sensing using Uncertainty Autoencoders Aditya Grover, Stefano Ermon
Uncertainty in the Variational Information Bottleneck Alexander A. Alemi, Ian Fischer, Joshua V. Dillon
Dependent Type Networks- A Probabilistic Logic via the Curry-Howard Correspondence in a System of Probabilistic Dependent Types Jonathan Warrell, Mark Gerstein
Learning Logistic Circuits Yitao Liang, Guy Van den Broeck
Deep Gaussian Processes with Convolutional Kernels Vinayak Kumar, Vaibhav Singh, P.K. Srijith, Andreas Damianou
Trading-off Learning and Inference in Deep Latent Variable Models Daniel Levy, Stefano Ermon
Fast Metropolis-Hastings and Natural Gradient John Canny, Daniel Seita, Anoop Korattikara
Improving Stability in Deep Reinforcement Learning with Weight Averaging Evgenii Nikishin, Pavel Izmailov, Ben Athiwaratkun, Dmitrii Podoprikhin, Timur Garipov, Pavel Shvechikov, Dmitry Vetrov, Andrew Gordon Wilson
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification Xinhan Di, Pengqian Yu, Meng Tian
Tensor Monte Carlo: particle methods for the GPU era Laurence Aitchison

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