Papers
Online Control with Adversarial Disturbances
Naman Agarwal, Brian Bullins, Elad Hazan et al.
Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour
Online Learning to Rank with Features
Shuai Li, Tor Lattimore, Csaba Szepesvari
Online learning with kernel losses
Niladri Chatterji, Aldo Pacchiano, Peter Bartlett
Online Learning with Sleeping Experts and Feedback Graphs
Corinna Cortes, Giulia Desalvo, Claudio Gentile et al.
Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade et al.
Online Variance Reduction with Mixtures
Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy et al.
On Medians of (Randomized) Pairwise Means
Pierre Laforgue, Stephan Clemencon, Patrice Bertail
On Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie, Minshuo Chen, Haoming Jiang et al.
On Sparse Linear Regression in the Local Differential Privacy Model
Di Wang, Jinhui Xu
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
On the Complexity of Approximating Wasserstein Barycenters
Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh et al.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann, Sebastian Lunz, Peter Maass et al.
On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey et al.
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