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