490 citations · 1.5k across the 111 of their papers we have counts for
21 papers · 1 filter
Data Diversity Matters for Robust Instruction Tuning
Alexander Bukharin, Shiyang Li, Zhengyang Wang +6
Recent works have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. However, creating su…
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Qingru Zhang, Chandan Singh, Liyuan Liu +4
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the rea…
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
Shenao Zhang, Boyi Liu, Zhaoran Wang +1
ReParameterization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. However, recent studies have reveale…
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Zichong Li, Yanbo Xu, Simiao Zuo +4
Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of e…
Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification
Zichong Li, Qunzhi Xu, Zhenghao Xu +3
Spatio-temporal point processes (STPPs) are potent mathematical tools for modeling and predicting events with both temporal and spatial features. Despite their versatility, most ex…
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
Qingru Zhang, Dhananjay Ram, Cole Hawkins +2
Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture lo…
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu +6
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…
Module-wise Adaptive Distillation for Multimodality Foundation Models
Chen Liang, Jiahui Yu, Ming-Hsuan Yang +5
Pre-trained multimodal foundation models have demonstrated remarkable generalizability but pose challenges for deployment due to their large sizes. One effective approach to reduci…
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult
Yuqing Wang, Zhenghao Xu, Tuo Zhao +1
Large learning rates, when applied to gradient descent for nonconvex optimization, yield various implicit biases including the edge of stability (Cohen et al., 2021), balancing (Wa…
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
Ethan X. Fang, Yajun Mei, Yuyang Shi +2
We consider the linear discriminant analysis problem in the high-dimensional settings. In this work, we propose PANDA(PivotAl liNear Discriminant Analysis), a tuning-insensitive me…
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Zhenghao Xu, Xiang Ji, Minshuo Chen +2
Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses c…
Deep Reinforcement Learning from Hierarchical Preference Design
Alexander Bukharin, Yixiao Li, Pengcheng He +1
Reward design is a fundamental, yet challenging aspect of reinforcement learning (RL). Researchers typically utilize feedback signals from the environment to handcraft a reward fun…
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
Xiang Ji, Huazheng Wang, Minshuo Chen +2
For a real-world decision-making problem, the reward function often needs to be engineered or learned. A popular approach is to utilize human feedback to learn a reward function fo…
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Zixuan Zhang, Kaiqi Zhang, Minshuo Chen +4
Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by convent…
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Zixuan Zhang, Minshuo Chen, Mengdi Wang +2
Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data struc…
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
Yixiao Li, Yifan Yu, Qingru Zhang +4
Transformer models have achieved remarkable results in various natural language tasks, but they are often prohibitively large, requiring massive memories and computational resource…
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin +5
Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-traine…
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Biraj Dahal, Alex Havrilla, Minshuo Chen +2
Generative networks have experienced great empirical successes in distribution learning. Many existing experiments have demonstrated that generative networks can generate high-dime…
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers
Chen Liang, Haoming Jiang, Zheng Li +3
Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on tas…
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Minshuo Chen, Kaixuan Huang, Tuo Zhao +1
Diffusion models achieve state-of-the-art performance in various generation tasks. However, their theoretical foundations fall far behind. This paper studies score approximation, e…