40 citations · 117 across the 33 of their papers we have counts for
38 papers
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…
Breaking the Gradient Barrier: Unveiling Large Language Models for Strategic Classification
Xinpeng Lv, Yunxin Mao, Haoxuan Li +9
Strategic classification~(SC) explores how individuals or entities modify their features strategically to achieve favorable classification outcomes. However, existing SC methods, w…
Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference
Zichen Wang, Haoyang Hong, Chuanhao Li +3
In multi-armed bandits with network interference (MABNI), the action taken by one node can influence the rewards of others, creating complex interdependence. While existing researc…
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
Danni Yang, Zhikang Chen, Sen Cui +6
Federated continual learning (FCL) has garnered increasing attention for its ability to support distributed computation in environments with evolving data distributions. However, t…
Large Language Models are Demonstration Pre-Selectors for Themselves
Jiarui Jin, Yuwei Wu, Haoxuan Li +6
In-context learning (ICL) with large language models (LLMs) delivers strong few-shot performance by choosing few-shot demonstrations from the entire training data. However, existin…
Curious Causality-Seeking Agents Learn Meta Causal World
Zhiyu Zhao, Haoxuan Li, Haifeng Zhang +4
When building a world model, a common assumption is that the environment has a single, unchanging underlying causal rule, like applying Newton's laws to every situation. In reality…
Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Xiangning Yu, Zhuohan Wang, Linyi Yang +5
Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamen…
Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
Tianjun Yao, Haoxuan Li, Yongqiang Chen +4
Graph Neural Networks (GNNs) often encounter significant performance degradation under distribution shifts between training and test data, hindering their applicability in real-wor…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…
OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain
Wenzhen Yue, Yong Liu, Haoxuan Li +5
This paper presents , a -based multivariate time series forecasting model that operates in an rthogonally transformed domain. Recent…
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…
IPAD: Inverse Prompt for AI Detection - A Robust and Interpretable LLM-Generated Text Detector
Zheng Chen, Yushi Feng, Jisheng Dang +5
Large Language Models (LLMs) have attained human-level fluency in text generation, which complicates the distinguishing between human-written and LLM-generated texts. This increase…
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…
Learning Counterfactual Outcomes Under Rank Preservation
Peng Wu, Haoxuan Li, Chunyuan Zheng +5
Counterfactual inference aims to estimate the counterfactual outcome at the individual level given knowledge of an observed treatment and the factual outcome, with broad applicatio…
Towards Understanding Extrapolation: a Causal Lens
Lingjing Kong, Guangyi Chen, Petar Stojanov +3
Canonical work handling distribution shifts typically necessitates an entire target distribution that lands inside the training distribution. However, practical scenarios often inv…
Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition
Haoyu Xie, Haoxuan Li, Chunyuan Zheng +4
Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR t…
CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds
Lei Wang, Jianxun Lian, Yi Huang +5
Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, a…
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…
Debiased Recommendation with Noisy Feedback
Haoxuan Li, Chunyuan Zheng, Wenjie Wang +3
Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased l…
Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation
Honglei Zhang, Haoxuan Li, Jundong Chen +6
Federated recommendation aims to collect global knowledge by aggregating local models from massive devices, to provide recommendations while ensuring privacy. Current methods mainl…
On the Maximal Local Disparity of Fairness-Aware Classifiers
Jinqiu Jin, Haoxuan Li, Fuli Feng
Fairness has become a crucial aspect in the development of trustworthy machine learning algorithms. Current fairness metrics to measure the violation of demographic parity have the…
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation
Min Zhang, Haoxuan Li, Fei Wu +1
Out-of-distribution (OOD) problems in few-shot classification (FSC) occur when novel classes sampled from testing distributions differ from base classes drawn from training distrib…
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
Haoxuan Li, Chunyuan Zheng, Sihao Ding +4
Selection bias in recommender system arises from the recommendation process of system filtering and the interactive process of user selection. Many previous studies have focused on…
Debiased Collaborative Filtering with Kernel-Based Causal Balancing
Haoxuan Li, Chunyuan Zheng, Yanghao Xiao +4
Debiased collaborative filtering aims to learn an unbiased prediction model by removing different biases in observational datasets. To solve this problem, one of the simple and eff…