1 citations · 1 across the 5 of their papers we have counts for
7 papers
Direct Pass@ Optimization for Thought Pruning: Balancing Quality and Diversity in Test-Time Reasoning
Mahdi Salmani, Adel Javanmard, Devansh Gupta +1
Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one co…
Differentially Private Learning of Markov Chains: Near-Optimal Sample Complexity for Item and User Level Privacy
Devansh Gupta, Cristóbal Guzman, Enayat Ullah
Sequential data such as documents, conversations, and trajectories are naturally partitioned across users, motivating privacy guarantees that protect each user's entire trajectory.…
Efficient DP-SGD for LLMs with Randomized Clipping
Enayat Ullah, Sai Aparna Aketi, Devansh Gupta +2
Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for formal privacy guarantees, pr…
On the Inherent Privacy of Zeroth Order Projected Gradient Descent
Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan
Differentially private zeroth-order optimization methods have recently gained popularity in private fine tuning of machine learning models due to their reduced memory requirements.…
Memory-Efficient Differentially Private Training with Gradient Random Projection
Alex Mulrooney, Devansh Gupta, James Flemings +4
Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead due to per-sample gradient clip…
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
Devansh Gupta, A. S. Poornash, Andrew Lowy +1
Machine learning models are often trained on sensitive data (e.g., medical records and race/gender) that is distributed across different "silos" (e.g., hospitals). These federated…
Stochastic Differentially Private and Fair Learning
Andrew Lowy, Devansh Gupta, Meisam Razaviyayn
Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against cert…