2 citations · 2 across the 13 of their papers we have counts for
15 papers
Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems
Xin Xu, Ke Cheng, Hao Wang +11
Improving an agent through successive revisions requires balancing exploration and evaluation under a limited budget. A narrow search can miss useful sequences of edits, while full…
A Self-Triggered Agentic Push Recommendation System
Zhao-Yu Zhang, Qingying Chen, Chunyuan Zheng +9
Push notification is a critical recommendation scenario on large-scale platforms, allowing the system to proactively reach users outside the application to improve long-term re-eng…
Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
Xinpeng Lv, Yunxin Mao, Renzhe Xu +11
Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcomes. Existing SC frameworks t…
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
Xinpeng Lv, Yunxin Mao, Renzhe Xu +13
Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph…
Unbiased Reward Modeling from Implicit Feedback for LLM Alignment
Hao Wang, Haocheng Yang, Licheng Pan +7
Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback, which is costly to collect and difficu…
Deep Time-Series Forecasting in 10 Years: A Survey
Hao Wang, Licheng Pan, Qingsong Wen +12
Autocorrelation is a common property of time-series, where each observation is dependent on its predecessors. In deep time-series forecasting, it raises two central challenges: (1)…
CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
Hao Wang, Licheng Pan, Zhichao Chen +7
Despite the success of reinforcement learning from human feedback (RLHF) in aligning language models, current reward modeling heavily relies on experimental feedback data collected…
Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective
Zhichao Chen, Hao Wang, Fangyikang Wang +5
Diffusion models (DMs) have shown promise for Time-Series Data Imputation (TSDI); however, their performance remains inconsistent in complex scenarios. We attribute this to two pri…
Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
Zhichao Chen, Zhan Zhuang, Yunfei Teng +6
Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via intermediate domains. However, real i…
Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond
Fan Zhang, Haoxuan Li, Shengju Qian +9
Multimodal Large Language Models (MLLMs) have revolutionized numerous research fields, including computer vision and affective computing. As a pivotal challenge in this interdiscip…
Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
Hao Wang, Licheng Pan, Yuan Lu +7
The design of learning objectives is central to training time-series forecasting models. Existing learning objectives such as mean squared error mostly treat each future step as an…
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
Hao Wang, Licheng Pan, Yuan Lu +7
Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approac…
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
Hao Wang, Licheng Pan, Zhichao Chen +5
Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared error, but this study reveals two crit…
Empowering LLMs with Logical Reasoning: A Comprehensive Survey
Fengxiang Cheng, Haoxuan Li, Fenrong Liu +3
Large language models (LLMs) have achieved remarkable successes on various tasks. However, recent studies have found that there are still significant challenges to the logical reas…
Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation
Hao Wang, Zhichao Chen, Zhaoran Liu +3
Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizi…