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Xiang Chen 0010

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5

ICML Conference 2025 Conference Paper

Fully Dynamic Embedding into ℓp Spaces

  • Kiarash Banihashem
  • Xiang Chen 0010
  • MohammadTaghi Hajiaghayi
  • Sungchul Kim
  • Kanak Mahadik
  • Ryan A. Rossi
  • Tong Yu 0001

Metric embeddings are fundamental in machine learning, enabling similarity search, dimensionality reduction, and representation learning. They underpin modern architectures like transformers and large language models, facilitating scalable training and improved generalization. Theoretically, the classic problem in embedding design is mapping arbitrary metrics into $\ell_p$ spaces while approximately preserving pairwise distances. We study this problem in a fully dynamic setting, where the underlying metric is a graph metric subject to edge insertions and deletions. Our goal is to maintain an efficient embedding after each update. We present the first fully dynamic algorithm for this problem, achieving $O(\log(n))^{2q} O(\log(nW))^{q-1}$ expected distortion with $O(m^{1/q + o(1)})$ update time and $O(q \log(n) \log(nW))$ query time, where $q \ge 2$ is an integer parameter.

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ICLR Conference 2025 Conference Paper

OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models

  • Junda Wu
  • Xintong Li 0001
  • Ruoyu Wang 0038
  • Yu Xia 0007
  • Yuxin Xiong
  • Jianing Wang 0002
  • Tong Yu 0001
  • Xiang Chen 0010

Offline evaluation of LLMs is crucial in understanding their capacities, though current methods remain underexplored in existing research. In this work, we focus on the offline evaluation of the chain-of-thought capabilities and show how to optimize LLMs based on the proposed evaluation method. To enable offline feedback with rich knowledge and reasoning paths, we use knowledge graphs (KGs) (e.g., Wikidata5M) to provide feedback on the generated chain of thoughts. Due to the heterogeneity between LLM reasoning and KG structures, direct interaction and feedback from knowledge graphs on LLM behavior are challenging, as they require accurate entity linking and grounding of LLM-generated chains of thought in the KG. To address the above challenge, we propose an offline chain-of-thought evaluation framework, OCEAN, which models chain-of-thought reasoning in LLMs as a Markov Decision Process (MDP), and evaluate the policy’s alignment with KG preference modeling. To overcome the reasoning heterogeneity and grounding problems, we leverage on-policy KG exploration and reinforcement learning to model a KG policy that generates token-level likelihood distributions for LLM-generated chain-of-thought reasoning paths, simulating KG reasoning preference. Then we incorporate the knowledge-graph feedback on the validity and alignment of the generated reasoning paths into inverse propensity scores and propose KG-IPS estimator. Theoretically, we prove the unbiasedness of the proposed KG-IPS estimator and provide a lower bound on its variance. With the off-policy evaluated value function, we can directly enable off-policy optimization to further enhance chain-of-thought alignment. Our empirical study shows that OCEAN can be efficiently optimized for generating chain-of-thought reasoning paths with higher estimated values without affecting LLMs’ general abilities in downstream tasks or their internal knowledge.

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ICML Conference 2024 Conference Paper

Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble

  • Chenhui Xu
  • Fuxun Yu
  • Zirui Xu
  • Nathan Inkawhich
  • Xiang Chen 0010

Recent research works demonstrate that one of the significant factors for the model Out-of-Distirbution detection performance is the scale of the OOD feature representation field. Consequently, model ensemble emerges as a trending method to expand this feature representation field leveraging expected model diversity. However, by proposing novel qualitative and quantitative model ensemble evaluation methods (i. e. , Loss Basin/Barrier Visualization and Self-Coupling Index), we reveal that the previous ensemble methods incorporate affine-transformable weights with limited variability and fail to provide desired feature representation diversity. Therefore, we escalate the traditional model ensemble dimensions (different weight initialization, data holdout, etc.) into distinct supervision tasks, which we name as Multi-Comprehension (MC) Ensemble. MC Ensemble leverages various training tasks to form different comprehensions of the data and labels, resulting in the extension of the feature representation field. In experiments, we demonstrate the superior performance of the MC Ensemble strategy in the OOD detection task compared to both the naive Deep Ensemble method and the standalone model of comparable size.

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ICLR Conference 2024 Conference Paper

ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

  • Yuchen Zhuang
  • Xiang Chen 0010
  • Tong Yu 0001
  • Saayan Mitra
  • Victor S. Bursztyn
  • Ryan A. Rossi
  • Somdeb Sarkhel
  • Chao Zhang 0014

Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can interact with diverse tools (e.g., functional APIs) and generate solution plans that execute a series of API function calls in a step-by-step manner. The multitude of candidate API function calls significantly expands the action space, amplifying the critical need for efficient action space navigation. However, existing methods either struggle with unidirectional exploration in expansive action spaces, trapped into a locally optimal solution, or suffer from exhaustively traversing all potential actions, causing inefficient navigation. To address these issues, we propose ToolChain*, an efficient tree search-based planning algorithm for LLM-based agents. It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan. By incorporating the A$^*$ search algorithm with task-specific cost function design, it efficiently prunes high-cost branches that may involve incorrect actions, identifying the most low-cost valid path as the solution. Extensive experiments on multiple tool-use and reasoning tasks demonstrate that ToolChain* efficiently balances exploration and exploitation within an expansive action space. It outperforms state-of-the-art baselines on planning and reasoning tasks by 3.1% and 3.5% on average while requiring 7.35x and 2.31x less time, respectively.

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ICML Conference 2023 Conference Paper

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

  • Jianyi Zhang
  • Ang Li 0005
  • Minxue Tang
  • Jingwei Sun 0002
  • Xiang Chen 0010
  • Fan Zhang 0069
  • Changyou Chen
  • Yiran Chen 0001

Due to the often limited communication bandwidth of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training at each communication round. Compared with engaging all the available clients, such a random-selection mechanism could lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we present our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on this observation, we design an efficient heterogeneity-aware client sampling mechanism, namely, Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the grouped dataset from the intentionally selected clients. We first propose a measure of class-imbalance which can be derived in a privacy-preserving way. Based on this measure, we design a computation-efficient client sampling strategy such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees. Experimental results show that Fed-CBS outperforms the status quo approaches in terms of test accuracy and the rate of convergence while achieving comparable or even better performance than the ideal setting where all the available clients participate in the FL training.

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v2026.09.13