From the 1 of 6 linked papers with an AI index.
6 papers
Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning
Dongyue Li, Zhenshuo Zhang, Minxuan Duan +2
The paper proposes branching neural networks that automatically partition multiple algorithmic reasoning tasks into a hierarchical tree structure, enabling efficient multitask lear…
WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
Dongyue Li, Zechun Liu, Kai Yi +6
Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized model. The main bottleneck is…
Efficient Estimation of Kernel Surrogate Models for Task Attribution
Zhenshuo Zhang, Minxuan Duan, Hongyang R. Zhang
Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text prediction -- simultaneously. A key qu…
Scalable Multi-Objective and Meta Reinforcement Learning via Gradient Estimation
Zhenshuo Zhang, Minxuan Duan, Youran Ye +1
We study the problem of efficiently estimating policies that simultaneously optimize multiple objectives in reinforcement learning (RL). Given objectives (or tasks), we seek th…
One-Sided Matrix Completion from Ultra-Sparse Samples
Hongyang R. Zhang, Zhenshuo Zhang, Huy L. Nguyen +1
Matrix completion is a classical problem that has received recurring interest across a wide range of fields. In this paper, we revisit this problem in an ultra-sparse sampling regi…
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
Ziniu Zhang, Zhenshuo Zhang, Dongyue Li +3
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of examples, how can we quickly select out of to…