Search Results for author:
Found 6 papers, 0 papers with code
Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
In this paper we provide oracle complexity lower bounds for finding a point in a given set using a memory-constrained algorithm that has access to a separation oracle.
Adversarial Rewards in Universal Learning for Contextual Bandits
We show that optimistic universal learning for contextual bandits with adversarial rewards is impossible in general, contrary to all previously studied settings in online learning -- including standard supervised learning.
Contextual Bandits and Optimistically Universal Learning
Lastly, we consider the case of added continuity assumptions on rewards and show that these lead to universal consistency for significantly larger classes of data-generating processes.
Universal Online Learning with Unbounded Losses: Memory Is All You Need
We resolve an open problem of Hanneke on the subject of universally consistent online learning with non-i. i. d.
Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions
In this paper, we analyze the number of neurons and training parameters that a neural network needs to approximate multivariate functions of bounded second mixed derivatives --- Korobov functions.
The Representation Power of Neural Networks: Breaking the Curse of Dimensionality
In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Korobov functions.
