Peng Cui

Error Slice Discovery via Manifold Compactness AAAI 2026 Generating Risky Samples with Conformity Constraints via Diffusion Models AAAI 2026 COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts CVPR 2025 Going Beyond Static: Understanding Shifts with Time-Series Attribution ICLR 2025 Grammar Control in Dialogue Response Generation for Language Learning Chatbots NAACL 2025 Improving Accuracy and Calibration via Differentiated Deep Mutual Learning CVPR 2025 Investigating the Zone of Proximal Development of Language Models for In-Context Learning NAACL 2025 ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction ICCV 2025 On the Out-Of-Distribution Generalization of Large Multimodal Models CVPR 2025 Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph ICML 2025 Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study ICLR 2025 Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift NeurIPS 2024 Debiased Collaborative Filtering with Kernel-Based Causal Balancing ICLR 2024 Domain-wise Data Acquisition to Improve Performance under Distribution Shift ICML 2024 Enhancing Distributional Stability among Sub-populations AISTATS 2024 Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications ICML 2024 How to Engage your Readers? Generating Guiding Questions to Promote Active Reading ACL 2024 Rethinking the Evaluation Protocol of Domain Generalization CVPR 2024 Stability Evaluation through Distributional Perturbation Analysis ICML 2024 Adaptive and Personalized Exercise Generation for Online Language Learning ACL 2023 Competing for Shareable Arms in Multi-Player Multi-Armed Bandits ICML 2023 Covariate-Shift Generalization via Random Sample Weighting AAAI 2023 Factual Observation Based Heterogeneity Learning for Counterfactual Prediction CLeaR 2023 Flatness-Aware Minimization for Domain Generalization ICCV 2023 Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization CVPR 2023 Learning Sample Difficulty from Pre-trained Models for Reliable Prediction NeurIPS 2023 Measure the Predictive Heterogeneity ICLR 2023 NICO++: Towards Better Benchmarking for Domain Generalization CVPR 2023 On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets NeurIPS 2023 Propensity Matters: Measuring and Enhancing Balancing for Recommendation ICML 2023 Provably Invariant Learning without Domain Information ICML 2023 Stable Learning via Sparse Variable Independence AAAI 2023 Towards Accelerated Model Training via Bayesian Data Selection NeurIPS 2023 Adversarial Eigen Attack on Black-Box Models CVPR 2022 A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization ICML 2022 Confidence-based Reliable Learning under Dual Noises NeurIPS 2022 Counterfactual Prediction for Outcome-Oriented Treatments ICML 2022 Distributionally Robust Optimization with Data Geometry NeurIPS 2022 Model Agnostic Sample Reweighting for Out-of-Distribution Learning ICML 2022 Product Ranking for Revenue Maximization with Multiple Purchases NeurIPS 2022 Towards Unsupervised Domain Generalization CVPR 2022 ZIN: When and How to Learn Invariance Without Environment Partition? NeurIPS 2022 Deep Stable Learning for Out-of-Distribution Generalization CVPR 2021 Heterogeneous Risk Minimization ICML 2021 Integrated Latent Heterogeneity and Invariance Learning in Kernel Space NeurIPS 2021 Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation AAAI 2021 Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents NAACL 2021 Stable Adversarial Learning under Distributional Shifts AAAI 2021 Topic-Guided Abstractive Multi-Document Summarization EMNLP 2021 A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models AAAI 2020 Calibrated Reliable Regression using Maximum Mean Discrepancy NeurIPS 2020 Counterfactual Prediction for Bundle Treatment NeurIPS 2020 Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks COLING 2020 Learning to Select Base Classes for Few-Shot Classification CVPR 2020 Rule-Guided Compositional Representation Learning on Knowledge Graphs AAAI 2020 Stable Learning via Sample Reweighting AAAI 2020 Stable Prediction with Model Misspecification and Agnostic Distribution Shift AAAI 2020 Disentangled Graph Convolutional Networks ICML 2019 Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation IJCAI 2019 Incorporating Network Embedding into Markov Random Field for Better Community Detection AAAI 2019 Learning Disentangled Representations for Recommendation NeurIPS 2019 Learning to Learn Image Classifiers With Visual Analogy CVPR 2019 Power-law Distribution Aware Trust Prediction IJCAI 2018 Deep Multimodal Hashing with Orthogonal Regularization IJCAI 2015