output
20052026
most citedStatistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates

108 citations

105 papers

cs.LG2026

Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation

Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +9

Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approac…

cs.AI2026

FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors

Hongxu Ma, Han Zhou, Chenghou Jin +5

Watch time has emerged as a pivotal metric for optimizing deep user engagement in short-video recommender systems. However, current methods of watch time prediction (WTP) suffer fr…

cs.LG2026

Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents

Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +8

Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To address fairness concerns intrins…

cs.LG2026

Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark

Xu Yao, Siyuan Zhou, Zhenbo Wu +6

Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, la…

cs.LG2026

DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression

Hongxu Ma, Lin Wang, Chenghou Jin +6

Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision. T…

cs.LG2026

Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting

Shuang Liang, Chaochuan Hou, Xu Yao +4

While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-leve…