20 citations · 48 across the 8 of their papers we have counts for
6 papers · 1 filter
Differentially Private Image Classification from Features
Harsh Mehta, Walid Krichene, Abhradeep Thakurta +2
Leveraging transfer learning has recently been shown to be an effective strategy for training large models with Differential Privacy (DP). Moreover, somewhat surprisingly, recent w…
Convexifying Transformers: Improving optimization and understanding of transformer networks
Tolga Ergen, Behnam Neyshabur, Harsh Mehta
Understanding the fundamental mechanism behind the success of transformer networks is still an open problem in the deep learning literature. Although their remarkable performance h…
Large Scale Transfer Learning for Differentially Private Image Classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin +1
Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. In the field of deep learning, Differentially Priv…
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
Ashok Cutkosky, Harsh Mehta
We consider non-convex stochastic optimization using first-order algorithms for which the gradient estimates may have heavy tails. We show that a combination of gradient clipping,…
Momentum Improves Normalized SGD
Ashok Cutkosky, Harsh Mehta
We provide an improved analysis of normalized SGD showing that adding momentum provably removes the need for large batch sizes on non-convex objectives. Then, we consider the case…
VALAN: Vision and Language Agent Navigation
Larry Lansing, Vihan Jain, Harsh Mehta +2
VALAN is a lightweight and scalable software framework for deep reinforcement learning based on the SEED RL architecture. The framework facilitates the development and evaluation o…