Papers
Reinforcement Learning with Convex Constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daume III et al.
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Srinath Sridhar, Davis Rempe, Julien Valentin et al.
Multiple Futures Prediction
Charlie Tang, Ruslan Salakhutdinov
A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions
Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu et al.
Continual Unsupervised Representation Learning
Dushyant Rao, Francesco Visin, Andrei Rusu et al.
Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely, Elad Granot
Optimal Decision Tree with Noisy Outcomes
Su Jia, viswanath nagarajan, Fatemeh Navidi et al.
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
Erwan Lecarpentier, Emmanuel Rachelson
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren et al.
Powerset Convolutional Neural Networks
Chris Wendler, Markus Püschel, Dan Alistarh
Decentralized Cooperative Stochastic Bandits
David Martínez-Rubio, Varun Kanade, Patrick Rebeschini
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Elliot Meyerson, Risto Miikkulainen
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Sindy Löwe, Peter O'Connor, Bastiaan Veeling
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
Aaron Defazio, Leon Bottou
Bayesian Batch Active Learning as Sparse Subset Approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick et al.
No-Press Diplomacy: Modeling Multi-Agent Gameplay
Philip Paquette, Yuchen Lu, SETON STEVEN BOCCO et al.
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
Minmin Chen, Ramki Gummadi, Chris Harris et al.
Shadowing Properties of Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser, Stephan Günnemann, Zachary Lipton
GENO -- GENeric Optimization for Classical Machine Learning
Soeren Laue, Matthias Mitterreiter, Joachim Giesen
On Single Source Robustness in Deep Fusion Models
Taewan Kim, Joydeep Ghosh
Learning Multiple Markov Chains via Adaptive Allocation
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Random Path Selection for Continual Learning
Jathushan Rajasegaran, Munawar Hayat, Salman H Khan et al.
Curriculum-guided Hindsight Experience Replay
Meng Fang, Tianyi Zhou, Yali Du et al.