2 citations · 5 across the 9 of their papers we have counts for
18 papers
Gradient-Hessian Query Tradeoffs: Matching Lower Bounds in Logarithmic Dimension
Wendao Wu, Haihan Zhang, Chenheng Zhang +5
We establish matching lower bounds for the tradeoff between gradient and Hessian queries in nonconvex optimization with Lipschitz Hessian. In normalized units, the initial objectiv…
Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning
Yanyi Li, Wendao Wu, Haihan Zhang +6
Tracking changing comparators is a central problem in online learning. For exp-concave losses, previous work established the dependence on the horizon and Eu…
Matching Randomized First-Order Bounds for Higher-Order Smooth Nonconvex Optimization
Haihan Zhang, Wendao Wu, Fangqing Zhang +5
Does randomization improve the worst-case first-order complexity of nonconvex optimization when the objective is very smooth? We prove that, for every fixed finite integer ,…
Matching Randomized Bounds for Finite-Sum Nonconvex-Concave Optimization
Wendao Wu, Haihan Zhang, Qian Chen +5
We characterize the randomized component-oracle complexity of finite-sum nonconvex-concave optimization with a bounded dual domain. For $Φ(x)=\max_{y\in Y}n^{-1}\sum_{i=1}^n f_i(x,…
Matching Complexity for Second-Order Stationary Point with Sample Reuse
Qian Chen, Wendao Wu, Haihan Zhang +5
We consider stochastic nonconvex optimization with sample-wise Lipschitz gradients and Hessians, and reusable opaque sample handles. Gradient and complete-Hessian evaluations are c…
Matching Full-Hessian Oracle Complexity for Nonconvex--Strongly-Concave Minimax Optimization
Fangqing Zhang, Haihan Zhang, Wendao Wu +5
We characterize the oracle complexity of finding first-order stationary points in smooth nonconvex--strongly-concave (NC--SC) minimax optimization when each query provides the full…
Matrix-Vector Complexity of Low-Rank Approximation
Haihan Zhang, Wendao Wu, Chenheng Zhang +5
We establish matching polynomial query bounds for low-rank approximation from exact matrix--vector products. Given an unknown matrix , at each step a ra…
Tool Retrievers Are Underestimated: Annotation Expansion Reveals True Capability
Yanyu Zhu, Chenheng Zhang, Shaoshen Chen +8
In open-world scenarios with massive and evolving tool repositories, tool-augmented large language models rely on a retriever to surface relevant tools for a given query. Because s…
Near-Optimal Lower Bounds for Randomized Algorithms in Exact Value Zeroth-Order Convex Optimization
Haihan Zhang, Chenheng Zhang, Zhiquan Qi +1
Whether exact scalar feedback intrinsically incurs the additional dimension paid by known zeroth-order methods remains open even for Lipschitz convex optimization. For a univer…
UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams
Siyu Xia, Chenheng Zhang, Yanting Wu +8
Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving ta…
GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization
Xiaosong Jia, Bowen Yang, Zuhao Ge +17
Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…
Rethinking Personalization in Large Language Models at the Token Level
Chenheng Zhang, Yijun Lu, Lizhe Fang +7
With large language models (LLMs) now performing strongly across diverse tasks, there is growing demand for them to personalize outputs for individual users. Personalization is typ…
Autoregressive Models Rival Diffusion Models at ANY-ORDER Generation
Tianqi Du, Lizhe Fang, Weijie Yang +4
Diffusion language models enable any-order generation and bidirectional conditioning, offering appealing flexibility for tasks such as infilling, rewriting, and self-correction. Ho…
Language Ranker: A Lightweight Ranking framework for LLM Decoding
Chenheng Zhang, Tianqi Du, Jizhe Zhang +4
Conventional research on large language models (LLMs) has primarily focused on refining output distributions, while paying less attention to the decoding process that transforms th…
Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads
Zhoutong Wu, Yuan Zhang, Yiming Dong +4
Transformer models have driven breakthroughs across various language tasks by their strong capability to learn rich contextual representations. Scaling them to improve representati…
SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning
Xiaojun Guo, Runyu Zhou, Yifei Wang +8
Vision-language models (VLMs) have shown remarkable abilities by integrating large language models with visual inputs. However, they often fail to utilize visual evidence adequatel…
Advancing LLM Safe Alignment with Safety Representation Ranking
Tianqi Du, Zeming Wei, Quan Chen +2
The rapid advancement of large language models (LLMs) has demonstrated milestone success in a variety of tasks, yet their potential for generating harmful content has raised signif…
What is Wrong with Perplexity for Long-context Language Modeling?
Lizhe Fang, Yifei Wang, Zhaoyang Liu +5
Handling long-context inputs is crucial for large language models (LLMs) in tasks such as extended conversations, document summarization, and many-shot in-context learning. While r…