Papers
On the Design of Estimators for Bandit Off-Policy Evaluation
Nikos Vlassis, Aurelien Bibaut, Maria Dimakopoulou et al.
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Rohin Shah, Noah Gundotra, Pieter Abbeel et al.
On the Generalization Gap in Reparameterizable Reinforcement Learning
Huan Wang, Stephan Zheng, Caiming Xiong et al.
On the Impact of the Activation function on Deep Neural Networks Training
Soufiane Hayou, Arnaud Doucet, Judith Rousseau
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke et al.
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari, Vedant Nanda, Krishna Gummadi
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen, Daphna Weinshall
On the Spectral Bias of Neural Networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit et al.
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
Xiaohan Wei, Zhuoran Yang, Zhaoran Wang
On the Universality of Invariant Networks
Haggai Maron, Ethan Fetaya, Nimrod Segol et al.
On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord et al.
Open-ended learning in symmetric zero-sum games
David Balduzzi, Marta Garnelo, Yoram Bachrach et al.
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Milan Cvitkovic, Badal Singh, Animashree Anandkumar
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards
Shiyin Lu, Guanghui Wang, Yao Hu et al.
Optimal Auctions through Deep Learning
Paul Duetting, Zhe Feng, Harikrishna Narasimhan et al.
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
Yatao Bian, Joachim Buhmann, Andreas Krause
Optimality Implies Kernel Sum Classifiers are Statistically Efficient
Raphael Meyer, Jean Honorio
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika et al.
Optimal Mini-Batch and Step Sizes for SAGA
Nidham Gazagnadou, Robert Gower, Joseph Salmon
Optimal Minimal Margin Maximization with Boosting
Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund
Optimal Transport for structured data with application on graphs
Vayer Titouan, Nicolas Courty, Romain Tavenard et al.
Optimistic Policy Optimization via Multiple Importance Sampling
Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo et al.
Orthogonal Random Forest for Causal Inference
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo, Mark Van Der Wilk, James Hensman et al.