activity
20192026
most citedDouble Cross Validation for the Number of Factors in Approximate Factor Models

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2026

Unified Inference Framework for Single and Multi-Player Performative Prediction: Method and Asymptotic Optimality

Zhixian Zhang, Xiaotian Hou, Linjun Zhang

Performative prediction characterizes environments where predictive models alter the very data distributions they aim to forecast, triggering complex feedback loops. While prior re…

cs.AI2026

PieArena: Ranking and Profiling Language Agents in Realistic Negotiation Scenarios

Chris Zhu, Sasha Cui, Will Sanok Dufallo +4

We present an in-depth evaluation of LLMs' ability to negotiate, a central business task requiring strategic reasoning, theory of mind, and economic value creation. To do so, we in…

cs.LG2026

Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals

Zihan Dong, Zhixian Zhang, Yang Zhou +3

Evaluating mathematical reasoning in LLMs is constrained by limited benchmark sizes and inherent model stochasticity, yielding high-variance accuracy estimates and unstable ranking…

stat.ML2025

Bias-Corrected Data Synthesis for Imbalanced Learning

Pengfei Lyu, Zhengchi Ma, Linjun Zhang +1

Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic oversampling can reduce imbalance, but…

stat.ME2019★ 3 cited

Double Cross Validation for the Number of Factors in Approximate Factor Models

Xianli Zeng, Yingcun Xia, Linjun Zhang

Determining the number of factors is essential to factor analysis. In this paper, we propose {an efficient cross validation (CV)} method to determine the number of factors in appro…