{"total":189,"rows":[{"id":"2204.02610","title":"Efficient Test-Time Model Adaptation without Forgetting","created":"2022-04-06","cited_by_count":469,"area":null},{"id":"2302.12400","title":"Towards Stable Test-Time Adaptation in Dynamic Wild World","created":"2023-02-24","cited_by_count":415,"area":null},{"id":"2302.12254","title":"Change is Hard: A Closer Look at Subpopulation Shift","created":"2023-02-23","cited_by_count":120,"area":null},{"id":"2211.14238","title":"Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time","created":"2022-11-25","cited_by_count":90,"area":null},{"id":"2202.06523","title":"MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts","created":"2022-02-14","cited_by_count":81,"area":null},{"id":"2404.01650","title":"Test-Time Model Adaptation with Only Forward Passes","created":"2024-04-02","cited_by_count":74,"area":null},{"id":"2305.00650","title":"Discover and Cure: Concept-aware Mitigation of Spurious Correlation","created":"2023-05-01","cited_by_count":71,"area":null},{"id":"2307.04722","title":"Advances and Challenges in Meta-Learning: A Technical Review","created":"2023-07-10","cited_by_count":65,"area":null},{"id":"2210.05775","title":"C-Mixup: Improving Generalization in Regression","created":"2022-10-11","cited_by_count":57,"area":null},{"id":"2211.14946","title":"Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models","created":"2022-11-27","cited_by_count":57,"area":null},{"id":"2201.09637","title":"DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations","created":"2022-01-24","cited_by_count":54,"area":null},{"id":"2209.00613","title":"ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets","created":"2022-09-01","cited_by_count":44,"area":null},{"id":"2405.02363","title":"LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model","created":"2024-05-03","cited_by_count":44,"area":null},{"id":"2302.06232","title":"Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data","created":"2023-02-13","cited_by_count":43,"area":null},{"id":"2303.05470","title":"Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases","created":"2023-03-09","cited_by_count":41,"area":null},{"id":"2402.12715","title":"The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning","created":"2024-02-20","cited_by_count":41,"area":null},{"id":"2310.03182","title":"Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models","created":"2023-10-04","cited_by_count":40,"area":null},{"id":"2206.07766","title":"Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization","created":"2022-06-15","cited_by_count":38,"area":null},{"id":"2206.07837","title":"Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization","created":"2022-06-15","cited_by_count":34,"area":null},{"id":"2209.08928","title":"UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup","created":"2022-09-19","cited_by_count":34,"area":null},{"id":"2212.01433","title":"Avoiding spurious correlations via logit correction","created":"2022-12-02","cited_by_count":32,"area":null},{"id":"2211.02291","title":"SelecMix: Debiased Learning by Contradicting-pair Sampling","created":"2022-11-04","cited_by_count":29,"area":null},{"id":"2206.14502","title":"RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness","created":"2022-06-29","cited_by_count":27,"area":null},{"id":"2309.16748","title":"Discovering environments with XRM","created":"2023-09-28","cited_by_count":26,"area":null},{"id":"2411.10634","title":"Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data","created":"2024-11-15","cited_by_count":25,"area":null},{"id":"2302.11861","title":"Out-of-Domain Robustness via Targeted Augmentations","created":"2023-02-23","cited_by_count":24,"area":null},{"id":"2210.11075","title":"Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise","created":"2022-10-20","cited_by_count":22,"area":null},{"id":"2305.06024","title":"A Survey on the Robustness of Computer Vision Models against Common Corruptions","created":"2023-05-10","cited_by_count":21,"area":null},{"id":"2403.11491","title":"Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting","created":"2024-03-18","cited_by_count":20,"area":null},{"id":"2403.05026","title":"Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts","created":"2024-03-08","cited_by_count":19,"area":null},{"id":"2412.07684","title":"The Pitfalls of Memorization: When Memorization Hurts Generalization","created":"2024-12-10","cited_by_count":19,"area":null},{"id":"2210.01302","title":"Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation","created":"2022-10-04","cited_by_count":18,"area":null},{"id":"2303.04379","title":"HappyMap: A Generalized Multi-calibration Method","created":"2023-03-08","cited_by_count":18,"area":null},{"id":"2402.18919","title":"Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation","created":"2024-02-29","cited_by_count":18,"area":null},{"id":"2308.11158","title":"Domain Generalization via Rationale Invariance","created":"2023-08-22","cited_by_count":16,"area":null},{"id":"2404.13815","title":"Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference","created":"2024-04-22","cited_by_count":15,"area":null},{"id":"2211.00184","title":"FL Games: A Federated Learning Framework for Distribution Shifts","created":"2022-10-31","cited_by_count":14,"area":null},{"id":"2302.02609","title":"Improving Domain Generalization with Domain Relations","created":"2023-02-06","cited_by_count":14,"area":null},{"id":"2302.03679","title":"How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?","created":"2023-02-07","cited_by_count":14,"area":null},{"id":"2302.11215","title":"Energy-Based Test Sample Adaptation for Domain Generalization","created":"2023-02-22","cited_by_count":14,"area":null},{"id":"2310.12803","title":"Data Augmentations for Improved (Large) Language Model Generalization","created":"2023-10-19","cited_by_count":14,"area":null},{"id":"2509.22412","title":"FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing","created":"2025-09-26","cited_by_count":14,"area":null},{"id":"2308.00994","title":"SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems","created":"2023-08-02","cited_by_count":13,"area":null},{"id":"2401.01764","title":"Understanding the Detrimental Class-level Effects of Data Augmentation","created":"2023-12-07","cited_by_count":13,"area":null},{"id":"2303.07527","title":"Domain Generalization via Nuclear Norm Regularization","created":"2023-03-13","cited_by_count":12,"area":null},{"id":"2405.05160","title":"Selective Classification Under Distribution Shifts","created":"2024-05-08","cited_by_count":12,"area":null},{"id":"2205.12753","title":"An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation","created":"2022-05-25","cited_by_count":11,"area":null},{"id":"2403.06392","title":"Towards Robust Out-of-Distribution Generalization Bounds via Sharpness","created":"2024-03-11","cited_by_count":11,"area":null},{"id":"2406.06384","title":"Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations","created":"2024-06-10","cited_by_count":11,"area":null},{"id":"2407.07311","title":"ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence","created":"2024-07-10","cited_by_count":11,"area":null},{"id":"2211.10370","title":"Invariant Learning via Diffusion Dreamed Distribution Shifts","created":"2022-11-18","cited_by_count":10,"area":null},{"id":"2306.08076","title":"Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization","created":"2023-06-13","cited_by_count":10,"area":null},{"id":"2403.02695","title":"Controllable Prompt Tuning For Balancing Group Distributional Robustness","created":"2024-03-05","cited_by_count":10,"area":null},{"id":"2403.03599","title":"Learning Invariant Representations of Graph Neural Networks via Cluster Generalization","created":"2024-03-06","cited_by_count":10,"area":null},{"id":"2405.03649","title":"Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation","created":"2024-05-06","cited_by_count":10,"area":null},{"id":"2302.02931","title":"Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts","created":"2023-02-06","cited_by_count":9,"area":null},{"id":"2407.14974","title":"Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations","created":"2024-07-20","cited_by_count":9,"area":null},{"id":"2305.16817","title":"Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup","created":"2023-05-26","cited_by_count":8,"area":null},{"id":"2305.18421","title":"HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts","created":"2023-05-28","cited_by_count":8,"area":null},{"id":"2312.14329","title":"Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective","created":"2023-12-21","cited_by_count":8,"area":null},{"id":"2401.14846","title":"Understanding Domain Generalization: A Noise Robustness Perspective","created":"2024-01-26","cited_by_count":8,"area":null},{"id":"2510.02818","title":"Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets","created":"2025-10-03","cited_by_count":8,"area":null},{"id":"2305.02995","title":"Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations","created":"2023-05-04","cited_by_count":7,"area":null},{"id":"2306.04527","title":"ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology","created":"2023-06-07","cited_by_count":7,"area":null},{"id":"2402.11039","title":"Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains","created":"2024-02-16","cited_by_count":7,"area":null},{"id":"2404.16307","title":"Boosting Model Resilience via Implicit Adversarial Data Augmentation","created":"2024-04-25","cited_by_count":7,"area":null},{"id":"2405.08586","title":"Cross-Domain Feature Augmentation for Domain Generalization","created":"2024-05-14","cited_by_count":7,"area":null},{"id":"2408.07832","title":"LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers","created":"2024-07-31","cited_by_count":7,"area":null},{"id":"2504.08010","title":"Self-Bootstrapping for Versatile Test-Time Adaptation","created":"2025-04-10","cited_by_count":7,"area":null},{"id":"2209.00652","title":"Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution","created":"2022-09-01","cited_by_count":6,"area":null},{"id":"2210.13533","title":"Sufficient Invariant Learning for Distribution Shift","created":"2022-10-24","cited_by_count":6,"area":null},{"id":"2211.05228","title":"FIXED: Frustratingly Easy Domain Generalization with Mixup","created":"2022-11-07","cited_by_count":6,"area":null},{"id":"2309.06717","title":"Bias Amplification Enhances Minority Group Performance","created":"2023-09-13","cited_by_count":6,"area":null},{"id":"2310.20427","title":"Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology","created":"2023-10-31","cited_by_count":6,"area":null},{"id":"2401.08464","title":"Enhancing Evolving Domain Generalization through Dynamic Latent Representations","created":"2024-01-16","cited_by_count":6,"area":null},{"id":"2403.07310","title":"How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance","created":"2024-03-12","cited_by_count":6,"area":null},{"id":"2404.18311","title":"Towards Incremental Learning in Large Language Models: A Critical Review","created":"2024-04-28","cited_by_count":6,"area":null},{"id":"2409.03303","title":"Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization","created":"2024-09-05","cited_by_count":6,"area":null},{"id":"2211.08722","title":"Learning with Noisy Labels over Imbalanced Subpopulations","created":"2022-11-16","cited_by_count":5,"area":null},{"id":"2306.11120","title":"Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts","created":"2023-06-19","cited_by_count":5,"area":null},{"id":"2309.10301","title":"Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms","created":"2023-09-19","cited_by_count":5,"area":null},{"id":"2311.01434","title":"Tailoring Mixup to Data for Calibration","created":"2023-11-02","cited_by_count":5,"area":null},{"id":"2311.15545","title":"Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction","created":"2023-11-27","cited_by_count":5,"area":null},{"id":"2402.08384","title":"Selective Learning: Towards Robust Calibration with Dynamic Regularization","created":"2024-02-13","cited_by_count":5,"area":null},{"id":"2403.13850","title":"Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance","created":"2024-03-18","cited_by_count":5,"area":null},{"id":"2404.19644","title":"MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation","created":"2024-04-30","cited_by_count":5,"area":null},{"id":"2411.12201","title":"Invariant Shape Representation Learning For Image Classification","created":"2024-11-19","cited_by_count":5,"area":null},{"id":"2505.04038","title":"Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning","created":"2025-05-07","cited_by_count":5,"area":null},{"id":"2505.15008","title":"Know When to Abstain: Optimal Selective Classification with Likelihood Ratios","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2206.12444","title":"Gated Domain Units for Multi-source Domain Generalization","created":"2022-06-24","cited_by_count":4,"area":null},{"id":"2310.04971","title":"Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift","created":"2023-10-08","cited_by_count":4,"area":null},{"id":"2310.08677","title":"GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts","created":"2023-10-12","cited_by_count":4,"area":null},{"id":"2407.02900","title":"Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2408.09570","title":"Say My Name: a Model's Bias Discovery Framework","created":"2024-08-18","cited_by_count":4,"area":null},{"id":"2503.13236","title":"Gradient Extrapolation for Debiased Representation Learning","created":"2025-03-17","cited_by_count":4,"area":null},{"id":"2210.03103","title":"Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!","created":"2022-10-06","cited_by_count":3,"area":null},{"id":"2210.14358","title":"Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations","created":"2022-10-25","cited_by_count":3,"area":null},{"id":"2212.05908","title":"Instance-Conditional Timescales of Decay for Non-Stationary Learning","created":"2022-12-12","cited_by_count":3,"area":null},{"id":"2302.02092","title":"Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics","created":"2023-02-04","cited_by_count":3,"area":null},{"id":"2305.14521","title":"Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings","created":"2023-05-23","cited_by_count":3,"area":null}],"next_cursor":"100"}