30 citations · 35 across the 7 of their papers we have counts for
7 papers
ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
Lawrence Liu, Alexander Liu, Mengdi Wang +2
Large language models (LLMs) present significant deployment challenges due to their immense computational and memory requirements. While semi-structured pruning, particularly 2:4 s…
NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models
Lawrence Liu, Inesh Chakrabarti, Yixiao Li +3
Large language models (LLMs) exhibit remarkable performance across various natural language processing tasks but suffer from immense computational and memory demands, limiting thei…
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP
Jinghan Wang, Mengdi Wang, Lin F. Yang
This work considers the sample complexity of obtaining an -optimal policy in an average reward Markov Decision Process (AMDP), given access to a generative model (simu…
Sketching Transformed Matrices with Applications to Natural Language Processing
Yingyu Liang, Zhao Song, Mengdi Wang +2
Suppose we are given a large matrix that cannot be stored in memory but is in a disk or is presented in a data stream. However, we need to compute a matrix decomposit…
Continuous Control with Contexts, Provably
Simon S. Du, Ruosong Wang, Mengdi Wang +1
A fundamental challenge in artificial intelligence is to build an agent that generalizes and adapts to unseen environments. A common strategy is to build a decoder that takes the c…
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity
Aaron Sidford, Mengdi Wang, Lin F. Yang +1
In this paper, we settle the sampling complexity of solving discounted two-player turn-based zero-sum stochastic games up to polylogarithmic factors. Given a stochastic game with d…