Papers
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Shunta Akiyama, Taiji Suzuki
On Limited-Memory Subsampling Strategies for Bandits
Dorian Baudry, Yoan Russac, Olivier Cappé
Online A-Optimal Design and Active Linear Regression
Xavier Fontaine, Pierre Perrault, Michal Valko et al.
On Linear Identifiability of Learned Representations
Geoffrey Roeder, Luke Metz, Durk Kingma
Online Graph Dictionary Learning
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary et al.
Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games
Ilai Bistritz, Nicholas Bambos
Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu et al.
Online Learning with Optimism and Delay
Genevieve E Flaspohler, Francesco Orabona, Judah Cohen et al.
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
Ofir Nabati, Tom Zahavy, Shie Mannor
Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
Yun Kuen Cheung, Georgios Piliouras
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt$T Regret
Asaf B Cassel, Tomer Koren
Online Selection Problems against Constrained Adversary
Zhihao Jiang, Pinyan Lu, Zhihao Gavin Tang et al.
Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems
Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause et al.
Online Unrelated Machine Load Balancing with Predictions Revisited
Shi Li, Jiayi Xian
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization
Xu Cai, Jonathan Scarlett
On Monotonic Linear Interpolation of Neural Network Parameters
James R Lucas, Juhan Bae, Michael R Zhang et al.
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Zahra Babaiee, Ramin Hasani, Mathias Lechner et al.
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
Zeyu Yan, Fei Wen, Rendong Ying et al.
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
Yiming Zhang, Keith W Ross
On Proximal Policy Optimization’s Heavy-tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto et al.
On Recovering from Modeling Errors Using Testing Bayesian Networks
Haiying Huang, Adnan Darwiche
On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
Tianhao Wu, Yunchang Yang, Simon Du et al.
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
Shuang Qiu, Jieping Ye, Zhaoran Wang et al.
On Robust Mean Estimation under Coordinate-level Corruption
Zifan Liu, Jong Ho Park, Theodoros Rekatsinas et al.
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal et al.