20 citations · 30 across the 8 of their papers we have counts for
10 papers
When Does Dense Retrieval Need Asymmetric Geometry? A Bias-Variance Theory of Shared and Dual Projections
Maojun Sun, Yancheng Yuan, Jian Huang +1
Dense retrieval powers retrieval-augmented generation, semantic search, and question answering, yet the theoretical basis for choosing between shared and dual query-document projec…
VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
Hongfei Wu, Ruijian Han, Yancheng Yuan
Imbalanced classification remains a pervasive challenge in machine learning, particularly when minority samples are too scarce to provide a robust discriminative boundary. In such…
DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
Maojun Sun, Yue Wu, Yifei Xie +5
Large Language Model (LLM) agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistica…
Think Dense, Not Long: Dynamic Decoupled Conditional Advantage for Efficient Reasoning
Keqin Peng, Yuanxin Ouyang, Xuebo Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) can elicit strong multi-step reasoning, yet it often encourages overly verbose traces. Moreover, naive length penalties in gro…
DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems
Maojun Sun, Yifei Xie, Yue Wu +5
Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, whi…
Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation
Yu Zhong, Zihao Zhang, Rui Zhang +9
Vision-and-Language Navigation (VLN) requires an agent to dynamically explore complex 3D environments following human instructions. Recent research underscores the potential of har…
Learning Guarantee of Reward Modeling Using Deep Neural Networks
Yuanhang Luo, Yeheng Ge, Ruijian Han +1
In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep re…
A Survey on Large Language Model-based Agents for Statistics and Data Science
Maojun Sun, Ruijian Han, Binyan Jiang +4
In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis pa…
LAMBDA: A Large Model Based Data Agent
Maojun Sun, Ruijian Han, Binyan Jiang +4
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is des…
Adaptive debiased SGD in high-dimensional GLMs with streaming data
Ruijian Han, Lan Luo, Yuanhang Luo +2
Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This paper in…