Haoxuan Li

Orcid: 0000-0003-3620-3769

Affiliations:
  • Peking University, Beijing, China


According to our database1, Haoxuan Li authored at least 116 papers between 2022 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Causal Inference with Complex Treatments: A Survey.
ACM Comput. Surv., July, 2026

T-GINEE: A Tensor-Based Multilayer Graph Representation Learning.
CoRR, May, 2026

Beyond Rational Illusion: Behaviorally Realistic Strategic Classification.
CoRR, May, 2026

When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach.
CoRR, May, 2026

Optimal Transport for LLM Reward Modeling from Noisy Preference.
CoRR, May, 2026

From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation.
CoRR, April, 2026

Batch-Adaptive Doubly Robust Learning for Debiasing Post-Click Conversion Rate Prediction Under Sparse Data.
ACM Trans. Inf. Syst., March, 2026

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment.
CoRR, March, 2026

Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects.
CoRR, March, 2026

CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks.
CoRR, March, 2026

Invariant Causal Routing for Governing Social Norms in Online Market Economies.
CoRR, March, 2026

Observationally Informed Adaptive Causal Experimental Design.
CoRR, March, 2026

Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation.
ACM Trans. Inf. Syst., February, 2026

ProcMEM: Learning Reusable Procedural Memory from Experience via Non-Parametric PPO for LLM Agents.
CoRR, February, 2026

Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective.
CoRR, February, 2026

Rethinking the Flow-Based Gradual Domain Adaption: A Semi-Dual Optimal Transport Perspective.
CoRR, February, 2026

Deep Time-series Forecasting Needs Kernelized Moment Balancing.
CoRR, February, 2026

Pareto-optimal estimation and policy learning for balancing short-term and long-term outcomes.
Neural Networks, 2026

Uplift Modeling with Delayed Feedback: Identifiability and Algorithms.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Semantics-Preserving Adversarial Attacks on Event-Driven Stock Prediction Models.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Debiased Recommendation via Wasserstein Causal Balancing.
ACM Trans. Inf. Syst., November, 2025

Breaking the Gradient Barrier: Unveiling Large Language Models for Strategic Classification.
CoRR, November, 2025

Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond.
CoRR, November, 2025

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models.
CoRR, November, 2025

MTIR-SQL: Multi-turn Tool-Integrated Reasoning Reinforcement Learning for Text-to-SQL.
CoRR, October, 2025

DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment.
CoRR, October, 2025

Beyond Normality: Reliable A/B Testing with Non-Gaussian Data.
CoRR, October, 2025

A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators.
CoRR, October, 2025

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training.
CoRR, September, 2025

MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models.
CoRR, August, 2025

Mitigating Hidden Confounding by Progressive Confounder Imputation via Large Language Models.
CoRR, July, 2025

Curious Causality-Seeking Agents Learn Meta Causal World.
CoRR, June, 2025

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering.
CoRR, June, 2025

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition.
CoRR, June, 2025

Pruning Spurious Subgraphs for Graph Out-of-Distribtuion Generalization.
CoRR, June, 2025

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining.
CoRR, May, 2025

Mixture of Low Rank Adaptation with Partial Parameter Sharing for Time Series Forecasting.
CoRR, May, 2025

TransDF: Time-Series Forecasting Needs Transformed Label Alignment.
CoRR, May, 2025

OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain.
CoRR, May, 2025

A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confounding.
CoRR, January, 2025

Entire Space Counterfactual Learning for Reliable Content Recommendations.
IEEE Trans. Inf. Forensics Secur., 2025

Learning double balancing representation for heterogeneous dose-response curve estimation.
Neural Networks, 2025

Proactive Recommendation in Social Networks: Steering user interest with causal inference.
AI Open, 2025

CAP: Causal Air Quality Index Prediction Under Interference with Unmeasured Confounding.
Proceedings of the ACM on Web Conference 2025, 2025

Adaptive Structure Learning with Partial Parameter Sharing for Post-Click Conversion Rate Prediction.
Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

Curious Causality-Seeking Agents in Open-ended Worlds.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Learning Counterfactual Outcomes Under Rank Preservation.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarios.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds.
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

A Two-Stage Pretraining-Finetuning Framework for Treatment Effect Estimation with Unmeasured Confounding.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

Classifying Treatment Responders: Bounds and Algorithms.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

Mitigating Data Imbalance in Time Series Classification Based on Counterfactual Minority Samples Augmentation.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 2025

Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 2025

Empowering LLMs with Logical Reasoning: A Comprehensive Survey.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Active Treatment Effect Estimation via Limited Samples.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Large Language Models are Demonstration Pre-Selectors for Themselves.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Learning without Isolation: Pathway Protection for Continual Learning.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Optimal Transport for Time Series Imputation.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

FreDF: Learning to Forecast in the Frequency Domain.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Effective and Efficient Time-Varying Counterfactual Prediction with State-Space Models.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Unifying Within and Across: Intra-Modality Multi-View Fusion and Inter-Modality Alignment for Knowledge Graph Completion.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

Mitigating Spurious Correlations via Counterfactual Contrastive Learning.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Visual Representation Learning through Causal Intervention for Controllable Image Editing.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

Text-Driven Fashion Image Editing with Compositional Concept Learning and Counterfactual Abduction.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
FSMLP: Modelling Channel Dependencies With Simplex Theory Based Multi-Layer Perceptions In Frequency Domain.
CoRR, 2024

Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence.
CoRR, 2024

Causal Inference with Complex Treatments: A Survey.
CoRR, 2024

Proximity Matters: Local Proximity Preserved Balancing for Treatment Effect Estimation.
CoRR, 2024

Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow.
CoRR, 2024

Attaining Human's Desirable Outcomes in Human-AI Interaction via Structural Causal Games.
CoRR, 2024

Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects.
CoRR, 2024

FreDF: Learning to Forecast in Frequency Domain.
CoRR, 2024

TransFR: Transferable Federated Recommendation with Pre-trained Language Models.
CoRR, 2024

Towards Understanding Extrapolation: a Causal Lens.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Debiased Recommendation with Noisy Feedback.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Your Neighbor Matters: Towards Fair Decisions Under Networked Interference.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Learning Causal Relations from Subsampled Time Series with Two Time-Slices.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

On the Maximal Local Disparity of Fairness-Aware Classifiers.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Debiased Collaborative Filtering with Kernel-Based Causal Balancing.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Uncovering the Propensity Identification Problem in Debiased Recommendations.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series.
CoRR, 2023

ConvFormer: Revisiting Transformer for Sequential User Modeling.
CoRR, 2023

Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations.
Proceedings of the ACM Web Conference 2023, 2023

Causal Recommendation: Progresses and Future Directions.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction.
Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Optimal Transport for Treatment Effect Estimation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Pareto Invariant Representation Learning for Multimedia Recommendation.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Trustworthy Policy Learning under the Counterfactual No-Harm Criterion.
Proceedings of the International Conference on Machine Learning, 2023

Propensity Matters: Measuring and Enhancing Balancing for Recommendation.
Proceedings of the International Conference on Machine Learning, 2023

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation.
Proceedings of the IEEE International Conference on Data Mining, 2023

Multiple Robust Learning for Recommendation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random.
CoRR, 2022

Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random.
CoRR, 2022

Causal Analysis Framework for Recommendation.
CoRR, 2022

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022


  Loading...