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Peng Cui

64 papers · 2015–2026 · 13 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (13) 🌈 Renaissance Researcher (12) 🐝 Cross-Pollinator (10) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer πŸ† Grand Slam πŸ‘‘ Triple Crown 🀝 Dynamic Duo (17) πŸ”¬ Deep Specialist (20) πŸ† Keyword Champion (11) πŸš€ Conference Pioneer ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (258) ❓ The Questioner (2) πŸ’Ž Century Club (62) πŸ”₯ Unstoppable (8)

Conferences

ICML (12) NIPS (12) AAAI (11) CVPR (11) ICLR (4) IJCAI (3) NAACL (3) ACL (2) ICCV (2) AISTATS (1) CLEAR (1) COLING (1) EMNLP (1)

Top co-authors

Xingxuan Zhang (18) Renzhe Xu (18) Jiashuo Liu (15) Bo Li (14) Yue He (11) Hao Zou (10) Zheyan Shen (10) Han Yu (10) Jiayun Wu (6) Wenwu Zhu (6)

Research topics

Education (1)

Keywords

out-of-distribution generalization (11) domain generalization (11) distribution shift (10) causal inference (7) invariant learning (4) covariate shift (4) domain adaptation (4) representation learning (3) counterfactual prediction (3) sample reweighting (3) recommender system (3) stable learning (3) disentangled representation (3) distributional shift (3) graph neural network (3) graph embedding (2) empirical risk minimization (2) image classification (2) distributionally robust optimization (2) few-shot learning (2)

Papers

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