63 citations · 130 across the 22 of their papers we have counts for
23 papers
Advancing Mathematics Research with AI-Driven Formal Proof Search
George Tsoukalas, Anton Kovsharov, Sergey Shirobokov +18
Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to gener…
An Improved Last-Iterate Convergence Rate for Anchored Gradient Descent Ascent
Anja Surina, Arun Suggala, George Tsoukalas +5
We analyze the last-iterate convergence of the Anchored Gradient Descent Ascent algorithm for smooth convex-concave min-max problems. While previous work established a last-iterate…
Offline-to-online hyperparameter transfer for stochastic bandits
Dravyansh Sharma, Arun Sai Suggala
Classic algorithms for stochastic bandits typically use hyperparameters that govern their critical properties such as the trade-off between exploration and exploitation. Tuning the…
CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization
Pranav Ajit Nair, Arun Sai Suggala
Large language models (LLMs) have recently demonstrated remarkable performance across diverse language tasks. But their deployment is often constrained by their substantial computa…
Improving Generalization via Meta-Learning on Hard Samples
Nishant Jain, Arun S. Suggala, Pradeep Shenoy
Learned reweighting (LRW) approaches to supervised learning use an optimization criterion to assign weights for training instances, in order to maximize performance on a representa…
Second Order Methods for Bandit Optimization and Control
Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli +1
Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, exi…