1 citations · 1 across the 5 of their papers we have counts for
5 papers
Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts
Youngseog Chung, Dhruv Malik, Jeff Schneider +2
The traditional viewpoint on Sparse Mixture of Experts (MoE) models is that instead of training a single large expert, which is computationally expensive, we can train many small e…
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs
Aakash Lahoti, Stefani Karp, Ezra Winston +2
Vision tasks are characterized by the properties of locality and translation invariance. The superior performance of convolutional neural networks (CNNs) on these tasks is widely a…
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
Eric Luxenberg, Dhruv Malik, Yuanzhi Li +2
We consider robust empirical risk minimization (ERM), where model parameters are chosen to minimize the worst-case empirical loss when each data point varies over a given convex un…
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
Dhruv Malik, Conor Igoe, Yuanzhi Li +1
In recommender system or crowdsourcing applications of online learning, a human's preferences or abilities are often a function of the algorithm's recent actions. Motivated by this…
Complete Policy Regret Bounds for Tallying Bandits
Dhruv Malik, Yuanzhi Li, Aarti Singh
Policy regret is a well established notion of measuring the performance of an online learning algorithm against an adaptive adversary. We study restrictions on the adversary that e…