1.9k citations · 6.5k across the 132 of their papers we have counts for
12 papers · 1 filter
Federated Online and Bandit Convex Optimization
Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha +1
We study the problems of distributed online and bandit convex optimization against an adaptive adversary. We aim to minimize the average regret on machines working in parallel…
Applying statistical learning theory to deep learning
Cédric Gerbelot, Avetik Karagulyan, Stefani Karp +3
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how diff…
When is Agnostic Reinforcement Learning Statistically Tractable?
Zeyu Jia, Gene Li, Alexander Rakhlin +2
We study the problem of agnostic PAC reinforcement learning (RL): given a policy class , how many rounds of interaction with an unknown MDP (with a potentially large state and a…
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
Nirmit Joshi, Gal Vardi, Nathan Srebro
Understanding how overparameterized neural networks generalize despite perfect interpolation of noisy training data is a fundamental question. Mallinar et. al. 2022 noted that neur…
Uniform Convergence with Square-Root Lipschitz Loss
Lijia Zhou, Zhen Dai, Frederic Koehler +1
We establish generic uniform convergence guarantees for Gaussian data in terms of the Rademacher complexity of the hypothesis class and the Lipschitz constant of the square root of…
Continual Learning in Linear Classification on Separable Data
Itay Evron, Edward Moroshko, Gon Buzaglo +4
We analyze continual learning on a sequence of separable linear classification tasks with binary labels. We show theoretically that learning with weak regularization reduces to sol…