{"total":355,"rows":[{"id":"2107.09044","title":"Just Train Twice: Improving Group Robustness without Training Group Information","created":"2021-07-19","cited_by_count":523,"area":null},{"id":"2108.13624","title":"Towards Out-Of-Distribution Generalization: A Survey","created":"2021-08-31","cited_by_count":436,"area":null},{"id":"2305.10429","title":"DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining","created":"2023-05-17","cited_by_count":399,"area":null},{"id":"2204.02937","title":"Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations","created":"2022-04-06","cited_by_count":381,"area":null},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2104.09937","title":"Gradient Matching for Domain Generalization","created":"2021-04-20","cited_by_count":258,"area":null},{"id":"2203.10020","title":"Challenges and Strategies in Cross-Cultural NLP","created":"2022-03-18","cited_by_count":218,"area":null},{"id":"2105.04054","title":"Societal Biases in Language Generation: Progress and Challenges","created":"2021-05-10","cited_by_count":202,"area":null},{"id":"2308.14921","title":"Gender bias and stereotypes in Large Language Models","created":"2023-08-28","cited_by_count":187,"area":null},{"id":"2203.01517","title":"Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations","created":"2022-03-03","cited_by_count":172,"area":null},{"id":"2108.12084","title":"Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies","created":"2021-08-27","cited_by_count":128,"area":null},{"id":"2207.07068","title":"Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey","created":"2022-07-14","cited_by_count":125,"area":null},{"id":"2202.02950","title":"Jury Learning: Integrating Dissenting Voices into Machine Learning Models","created":"2022-02-07","cited_by_count":118,"area":null},{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"area":null},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2111.13839","title":"Towards Principled Disentanglement for Domain Generalization","created":"2021-11-27","cited_by_count":91,"area":null},{"id":"2211.13972","title":"Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?","created":"2022-11-25","cited_by_count":91,"area":null},{"id":"2206.14754","title":"Distilling Model Failures as Directions in Latent Space","created":"2022-06-29","cited_by_count":82,"area":null},{"id":"2106.07171","title":"Examining and Combating Spurious Features under Distribution Shift","created":"2021-06-14","cited_by_count":65,"area":null},{"id":"2107.00758","title":"The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models","created":"2021-07-01","cited_by_count":63,"area":null},{"id":"2207.07180","title":"Contrastive Adapters for Foundation Model Group Robustness","created":"2022-07-14","cited_by_count":62,"area":null},{"id":"2209.03942","title":"Data Feedback Loops: Model-driven Amplification of Dataset Biases","created":"2022-09-08","cited_by_count":60,"area":null},{"id":"2110.03095","title":"Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective","created":"2021-10-06","cited_by_count":57,"area":null},{"id":"2106.06142","title":"DORO: Distributional and Outlier Robust Optimization","created":"2021-06-11","cited_by_count":56,"area":null},{"id":"2210.12530","title":"LMPriors: Pre-Trained Language Models as Task-Specific Priors","created":"2022-10-22","cited_by_count":56,"area":null},{"id":"2403.07857","title":"Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias","created":"2024-03-12","cited_by_count":55,"area":null},{"id":"2201.12323","title":"Describing Differences between Text Distributions with Natural Language","created":"2022-01-28","cited_by_count":54,"area":null},{"id":"2309.08534","title":"Towards Last-layer Retraining for Group Robustness with Fewer Annotations","created":"2023-09-15","cited_by_count":54,"area":null},{"id":"2203.07228","title":"FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing","created":"2022-03-14","cited_by_count":49,"area":null},{"id":"2110.14222","title":"Sample Selection for Fair and Robust Training","created":"2021-10-27","cited_by_count":48,"area":null},{"id":"2104.14537","title":"Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features","created":"2021-04-29","cited_by_count":45,"area":null},{"id":"2109.06141","title":"On Tilted Losses in Machine Learning: Theory and Applications","created":"2021-09-13","cited_by_count":43,"area":null},{"id":"2303.05033","title":"Out-of-distribution Detection with Implicit Outlier Transformation","created":"2023-03-09","cited_by_count":43,"area":null},{"id":"2305.06969","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","created":"2023-05-11","cited_by_count":41,"area":null},{"id":"2102.04257","title":"Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities","created":"2021-02-03","cited_by_count":40,"area":null},{"id":"2111.14581","title":"Learning Fair Classifiers with Partially Annotated Group Labels","created":"2021-11-29","cited_by_count":40,"area":null},{"id":"2306.16635","title":"Improving Fairness in Deepfake Detection","created":"2023-06-29","cited_by_count":40,"area":null},{"id":"2110.12459","title":"Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis","created":"2021-10-24","cited_by_count":39,"area":null},{"id":"2210.12529","title":"On-Demand Sampling: Learning Optimally from Multiple Distributions","created":"2022-10-22","cited_by_count":39,"area":null},{"id":"2111.14923","title":"Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models","created":"2021-11-29","cited_by_count":36,"area":null},{"id":"2207.08977","title":"Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift","created":"2022-07-18","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":"2209.10222","title":"Fairness Reprogramming","created":"2022-09-21","cited_by_count":33,"area":null},{"id":"2210.10837","title":"On Learning Fairness and Accuracy on Multiple Subgroups","created":"2022-10-19","cited_by_count":32,"area":null},{"id":"2103.03399","title":"Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data","created":"2021-03-05","cited_by_count":31,"area":null},{"id":"2202.05436","title":"Minimax Regret Optimization for Robust Machine Learning under Distribution Shift","created":"2022-02-11","cited_by_count":31,"area":null},{"id":"2202.07241","title":"Learning to Solve Routing Problems via Distributionally Robust Optimization","created":"2022-02-15","cited_by_count":31,"area":null},{"id":"2201.12293","title":"Understanding Why Generalized Reweighting Does Not Improve Over ERM","created":"2022-01-28","cited_by_count":29,"area":null},{"id":"2303.13087","title":"Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization","created":"2023-03-23","cited_by_count":29,"area":null},{"id":"2304.10153","title":"On the Independence of Association Bias and Empirical Fairness in Language Models","created":"2023-04-20","cited_by_count":29,"area":null},{"id":"2110.06282","title":"The Rich Get Richer: Disparate Impact of Semi-Supervised Learning","created":"2021-10-12","cited_by_count":28,"area":null},{"id":"2306.08158","title":"Sociodemographic Bias in Language Models: A Survey and Forward Path","created":"2023-06-13","cited_by_count":28,"area":null},{"id":"2406.00783","title":"AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark","created":"2024-06-02","cited_by_count":28,"area":null},{"id":"2202.13547","title":"RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network","created":"2022-02-28","cited_by_count":27,"area":null},{"id":"2304.09362","title":"Long-Term Fairness with Unknown Dynamics","created":"2023-04-19","cited_by_count":27,"area":null},{"id":"2406.08641","title":"ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets","created":"2024-06-12","cited_by_count":27,"area":null},{"id":"2209.10015","title":"Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics","created":"2022-09-20","cited_by_count":26,"area":null},{"id":"2110.02619","title":"Focus on the Common Good: Group Distributional Robustness Follows","created":"2021-10-06","cited_by_count":25,"area":null},{"id":"2110.09253","title":"A Sociotechnical View of Algorithmic Fairness","created":"2021-09-27","cited_by_count":24,"area":null},{"id":"2106.05964","title":"Fair Classification with Adversarial Perturbations","created":"2021-06-10","cited_by_count":23,"area":null},{"id":"2106.06620","title":"Robust Representation Learning via Perceptual Similarity Metrics","created":"2021-06-11","cited_by_count":23,"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":"2210.13043","title":"Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data","created":"2022-10-24","cited_by_count":22,"area":null},{"id":"2212.10563","title":"BLIND: Bias Removal With No Demographics","created":"2022-12-20","cited_by_count":22,"area":null},{"id":"2103.15947","title":"Federated Learning with Taskonomy for Non-IID Data","created":"2021-03-29","cited_by_count":21,"area":null},{"id":"2109.04999","title":"Fairness without the sensitive attribute via Causal Variational Autoencoder","created":"2021-09-10","cited_by_count":21,"area":null},{"id":"2204.02567","title":"FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons","created":"2022-04-06","cited_by_count":21,"area":null},{"id":"2409.08466","title":"Explaining Datasets in Words: Statistical Models with Natural Language Parameters","created":"2024-09-13","cited_by_count":21,"area":null},{"id":"2102.06764","title":"Technical Challenges for Training Fair Neural Networks","created":"2021-02-12","cited_by_count":20,"area":null},{"id":"2108.02391","title":"Adapting to Function Difficulty and Growth Conditions in Private Optimization","created":"2021-08-05","cited_by_count":20,"area":null},{"id":"2205.05770","title":"De-biasing \"bias\" measurement","created":"2022-05-11","cited_by_count":20,"area":null},{"id":"2206.02617","title":"Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent","created":"2022-06-06","cited_by_count":20,"area":null},{"id":"2209.07592","title":"Explicit Tradeoffs between Adversarial and Natural Distributional Robustness","created":"2022-09-15","cited_by_count":20,"area":null},{"id":"2303.00442","title":"Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization","created":"2023-03-01","cited_by_count":20,"area":null},{"id":"2501.00961","title":"Uncovering Memorization Effect in the Presence of Spurious Correlations","created":"2025-01-01","cited_by_count":20,"area":null},{"id":"2106.05127","title":"Deep Clustering based Fair Outlier Detection","created":"2021-06-09","cited_by_count":19,"area":null},{"id":"2107.13098","title":"A Tale Of Two Long Tails","created":"2021-07-27","cited_by_count":19,"area":null},{"id":"2102.06004","title":"Fairness-Aware PAC Learning from Corrupted Data","created":"2021-02-11","cited_by_count":18,"area":null},{"id":"2109.04020","title":"Distributionally Robust Multilingual Machine Translation","created":"2021-09-09","cited_by_count":18,"area":null},{"id":"2206.04101","title":"What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective","created":"2022-06-08","cited_by_count":18,"area":null},{"id":"2207.10991","title":"Algorithmic Fairness in Business Analytics: Directions for Research and Practice","created":"2022-07-22","cited_by_count":18,"area":null},{"id":"2102.12013","title":"Understanding and Mitigating Accuracy Disparity in Regression","created":"2021-02-24","cited_by_count":17,"area":null},{"id":"2109.06074","title":"On Language Models for Creoles","created":"2021-09-13","cited_by_count":17,"area":null},{"id":"2111.03015","title":"Modeling Techniques for Machine Learning Fairness: A Survey","created":"2021-11-04","cited_by_count":17,"area":null},{"id":"2204.06340","title":"Distributionally Robust Models with Parametric Likelihood Ratios","created":"2022-04-13","cited_by_count":17,"area":null},{"id":"2211.12703","title":"Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation","created":"2022-11-23","cited_by_count":17,"area":null},{"id":"2211.15231","title":"Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers","created":"2022-11-28","cited_by_count":17,"area":null},{"id":"2212.13669","title":"Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond","created":"2022-12-28","cited_by_count":17,"area":null},{"id":"2302.03629","title":"Ethical Considerations for Responsible Data Curation","created":"2023-02-07","cited_by_count":17,"area":null},{"id":"2305.04699","title":"Runtime Monitoring of Dynamic Fairness Properties","created":"2023-05-08","cited_by_count":17,"area":null},{"id":"2207.11727","title":"Can we achieve robustness from data alone?","created":"2022-07-24","cited_by_count":16,"area":null},{"id":"2311.05573","title":"Outlier-Robust Wasserstein DRO","created":"2023-11-09","cited_by_count":16,"area":null},{"id":"2312.02592","title":"FRAPPE: A Group Fairness Framework for Post-Processing Everything","created":"2023-12-05","cited_by_count":16,"area":null},{"id":"2107.09947","title":"Preventing dataset shift from breaking machine-learning biomarkers","created":"2021-07-21","cited_by_count":15,"area":null},{"id":"2211.10508","title":"Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics","created":"2022-11-18","cited_by_count":15,"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":"2108.05152","title":"Estimation of Fair Ranking Metrics with Incomplete Judgments","created":"2021-08-11","cited_by_count":14,"area":null},{"id":"2203.13225","title":"Distributionally Robust Optimization via Ball Oracle Acceleration","created":"2022-03-24","cited_by_count":14,"area":null},{"id":"2306.14670","title":"Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition","created":"2023-06-26","cited_by_count":14,"area":null},{"id":"2409.10538","title":"Fairness in Survival Analysis with Distributionally Robust Optimization","created":"2024-08-31","cited_by_count":14,"area":null}],"next_cursor":"100"}