4 citations · 4 across the 1 of their papers we have counts for
20 papers
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain
Dmitrii M. Ostrovskii, Babak Barazandeh, Meisam Razaviyayn
We study the problem of finding approximate first-order stationary points in optimization problems of the form , where the sets are conv…
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…
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…
Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs
Amin Banayeeanzade, Qingchuan Yang, Dhruv Tarsadiya +6
Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a narrow subset of plausible ou…
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Yinbin Han, Meisam Razaviyayn, Renyuan Xu
Diffusion models have become a leading paradigm in generative AI, with score estimation via denoising score matching as a central component. While recent theory provides strong sta…
Early Stopping for Large Reasoning Models via Confidence Dynamics
Parsa Hosseini, Sumit Nawathe, Mahdi Salmani +2
Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reasoning often incurs substantial computational cost and can even degrade p…