Papers
On Fast Leverage Score Sampling and Optimal Learning
Alessandro Rudi, Daniele Calandriello, Luigi Carratino et al.
Reinforcement Learning of Theorem Proving
Cezary Kaliszyk, Josef Urban, Henryk Michalewski et al.
Discrimination-aware Channel Pruning for Deep Neural Networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang et al.
Informative Features for Model Comparison
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy et al.
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Yuanzhi Li, Yingyu Liang
Monte-Carlo Tree Search for Constrained POMDPs
Jongmin Lee, Geon-hyeong Kim, Pascal Poupart et al.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, Gianfranco Doretto
A probabilistic population code based on neural samples
Sabyasachi Shivkumar, Richard Lange, Ankani Chattoraj et al.
A flexible model for training action localization with varying levels of supervision
Guilhem Chéron, Jean-Baptiste Alayrac, Ivan Laptev et al.
Link Prediction Based on Graph Neural Networks
Muhan Zhang, Yixin Chen
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D Toderici
Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao, Hongyu Ren, Arianna Yuan et al.
Single-Agent Policy Tree Search With Guarantees
Laurent Orseau, Levi Lelis, Tor Lattimore et al.
Regularizing by the Variance of the Activations' Sample-Variances
Etai Littwin, Lior Wolf
On Learning Intrinsic Rewards for Policy Gradient Methods
Zeyu Zheng, Junhyuk Oh, Satinder Singh
Generalizing Graph Matching beyond Quadratic Assignment Model
Tianshu Yu, Junchi Yan, Yilin Wang et al.
From Stochastic Planning to Marginal MAP
Hao(Jackson) Cui, Radu Marinescu, Roni Khardon
Completing State Representations using Spectral Learning
Nan Jiang, Alex Kulesza, Satinder Singh
Online Learning with an Unknown Fairness Metric
Stephen Gillen, Christopher Jung, Michael Kearns et al.
Regularization Learning Networks: Deep Learning for Tabular Datasets
Ira Shavitt, Eran Segal
Deep Poisson gamma dynamical systems
Dandan Guo, Bo Chen, Hao Zhang et al.
Reversible Recurrent Neural Networks
Matthew MacKay, Paul Vicol, Jimmy Ba et al.
A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman et al.
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation
Tianyu He, Xu Tan, Yingce Xia et al.
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
Tal Friedman, Guy Van den Broeck