Papers
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization
Yifan Hu, Xin Chen, Niao He
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Junyu Zhang, Chengzhuo Ni, zheng Yu et al.
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Shuyu Cheng, Guoqiang Wu, Jun Zhu
On the Convergence of Step Decay Step-Size for Stochastic Optimization
Xiaoyu Wang, Sindri Magnússon, Mikael Johansson
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
Alireza Fallah, Kristian Georgiev, Aryan Mokhtari et al.
On the Cryptographic Hardness of Learning Single Periodic Neurons
Min Jae Song, Ilias Zadik, Joan Bruna
On the Equivalence between Neural Network and Support Vector Machine
Yilan Chen, Wei Huang, Lam Nguyen et al.
On the Estimation Bias in Double Q-Learning
Zhizhou Ren, Guangxiang Zhu, Hao Hu et al.
On the Existence of The Adversarial Bayes Classifier
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
On the Expected Complexity of Maxout Networks
Hanna Tseran, Guido F. Montufar
On the Expressivity of Markov Reward
David Abel, Will Dabney, Anna Harutyunyan et al.
On the Frequency Bias of Generative Models
Katja Schwarz, Yiyi Liao, Andreas Geiger
On the Generative Utility of Cyclic Conditionals
Chang Liu, Haoyue Tang, Tao Qin et al.
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang, Andrew Geng, Yixuan Li
On the interplay between data structure and loss function in classification problems
Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun et al.
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Mingtian Zhang, Andi Zhang, Steven McDonagh
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova et al.
On the Power of Differentiable Learning versus PAC and SQ Learning
Emmanuel Abbe, Pritish Kamath, Eran Malach et al.
On the Power of Edge Independent Graph Models
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos et al.
On the Provable Generalization of Recurrent Neural Networks
Lifu Wang, Bo Shen, Bo Hu et al.
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond
Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Ziang Chen, Jianfeng Lu, Yulong Lu
On the Representation Power of Set Pooling Networks
Christian Bueno, Alan Hylton
On the Role of Optimization in Double Descent: A Least Squares Study
Ilja Kuzborskij, Csaba Szepesvari, Omar Rivasplata et al.
On the Sample Complexity of Learning under Geometric Stability
Alberto Bietti, Luca Venturi, Joan Bruna