{"list":"other","total":61,"rows":[{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1705.08500","title":"Selective Classification for Deep Neural Networks","created":"2017-05-23","cited_by_count":852},{"id":"1912.13213","title":"Online Learning: A Modern Introduction Using Convex Optimization","created":"2019-12-31","cited_by_count":417},{"id":"2006.01862","title":"Consistent Estimators for Learning to Defer to an Expert","created":"2020-06-02","cited_by_count":271},{"id":"1908.07086","title":"Human uncertainty makes classification more robust","created":"2019-08-19","cited_by_count":235},{"id":"1711.06664","title":"Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer","created":"2017-11-17","cited_by_count":233},{"id":"2304.07288","title":"Cross-Entropy Loss Functions: Theoretical Analysis and Applications","created":"2023-04-14","cited_by_count":210},{"id":"1402.0555","title":"Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits","created":"2014-02-04","cited_by_count":201},{"id":"2102.13004","title":"Towards Unbiased and Accurate Deferral to Multiple Experts","created":"2021-02-25","cited_by_count":72},{"id":"2301.06197","title":"Who Should Predict? Exact Algorithms For Learning to Defer to Humans","created":"2023-01-15","cited_by_count":70},{"id":"2210.16955","title":"Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles","created":"2022-10-30","cited_by_count":60},{"id":"2104.09658","title":"Calibration and Consistency of Adversarial Surrogate Losses","created":"2021-04-19","cited_by_count":47},{"id":"2207.09584","title":"Sample Efficient Learning of Predictors that Complement Humans","created":"2022-07-19","cited_by_count":46},{"id":"1803.09349","title":"Logistic Regression: The Importance of Being Improper","created":"2018-03-25","cited_by_count":41},{"id":"2109.14591","title":"Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration","created":"2021-09-29","cited_by_count":40},{"id":"2206.07948","title":"Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts","created":"2022-06-16","cited_by_count":40},{"id":"2310.14774","title":"Principled Approaches for Learning to Defer with Multiple Experts","created":"2023-10-23","cited_by_count":38},{"id":"2310.14772","title":"Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms","created":"2023-10-23","cited_by_count":36},{"id":"2301.09044","title":"Learning to Reject with a Fixed Predictor: Application to Decontextualization","created":"2023-01-22","cited_by_count":32},{"id":"2310.14770","title":"Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention","created":"2023-10-23","cited_by_count":32},{"id":"2403.19494","title":"Regression with Multi-Expert Deferral","created":"2024-03-28","cited_by_count":29},{"id":"1703.03478","title":"Online Learning with Abstention","created":"2017-03-09","cited_by_count":28},{"id":"2307.02035","title":"Ranking with Abstention","created":"2023-07-05","cited_by_count":27},{"id":"2403.02683","title":"Learning to Defer to a Population: A Meta-Learning Approach","created":"2024-03-05","cited_by_count":27},{"id":"2311.01106","title":"In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer","created":"2023-11-02","cited_by_count":26},{"id":"2407.13732","title":"Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer","created":"2024-07-18","cited_by_count":26},{"id":"2304.07306","title":"Learning to Defer with Limited Expert Predictions","created":"2023-04-14","cited_by_count":25},{"id":"1302.1043","title":"The price of bandit information in multiclass online classification","created":"2013-02-05","cited_by_count":24},{"id":"2405.05968","title":"A Universal Growth Rate for Learning with Smooth Surrogate Losses","created":"2024-05-09","cited_by_count":24},{"id":"2407.07140","title":"Cardinality-Aware Set Prediction and Top-$k$ Classification","created":"2024-07-09","cited_by_count":23},{"id":"2407.13746","title":"Multi-Label Learning with Stronger Consistency Guarantees","created":"2024-07-18","cited_by_count":22},{"id":"2403.19480","title":"$H$-Consistency Guarantees for Regression","created":"2024-03-28","cited_by_count":21},{"id":"2001.10623","title":"Fast Rates for Online Prediction with Abstention","created":"2020-01-28","cited_by_count":19},{"id":"2407.13722","title":"Enhanced $H$-Consistency Bounds","created":"2024-07-18","cited_by_count":18},{"id":"2410.15729","title":"A Two-Stage Learning-to-Defer Approach for Multi-Task Learning","created":"2024-10-21","cited_by_count":17},{"id":"2003.08109","title":"Efficient improper learning for online logistic regression","created":"2020-03-18","cited_by_count":16},{"id":"2502.01027","title":"Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees","created":"2025-02-03","cited_by_count":16},{"id":"2506.20650","title":"Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer","created":"2025-06-25","cited_by_count":16},{"id":"2410.15761","title":"Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees","created":"2024-10-21","cited_by_count":15},{"id":"2502.10381","title":"Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data","created":"2025-02-14","cited_by_count":15},{"id":"2106.03596","title":"Beyond Bandit Feedback in Online Multiclass Classification","created":"2021-06-07","cited_by_count":14},{"id":"2203.08653","title":"Counterfactual Inference of Second Opinions","created":"2022-03-16","cited_by_count":14},{"id":"2405.18902","title":"A Causal Framework for Evaluating Deferring Systems","created":"2024-05-29","cited_by_count":13},{"id":"2510.26706","title":"Budgeted Multiple-Expert Deferral","created":"2025-10-30","cited_by_count":13},{"id":"2110.03960","title":"Mixability made efficient: Fast online multiclass logistic regression","created":"2021-10-08","cited_by_count":12},{"id":"2110.03020","title":"Efficient Methods for Online Multiclass Logistic Regression","created":"2021-10-06","cited_by_count":10},{"id":"2007.12618","title":"Exploiting the Surrogate Gap in Online Multiclass Classification","created":"2020-07-24","cited_by_count":9},{"id":"2512.23947","title":"Improved Balanced Classification with Theoretically Grounded Loss Functions","created":"2025-12-30","cited_by_count":9},{"id":"2512.22886","title":"Theory and Algorithms for Learning with Multi-Class Abstention and Multi-Expert Deferral","created":"2025-12-28","cited_by_count":8},{"id":"2504.12988","title":"Why Ask One When You Can Ask $k$? Learning-to-Defer to the Top-$k$ Experts","created":"2025-04-17","cited_by_count":7},{"id":"2511.15816","title":"Beyond Tsybakov: Model Margin Noise and $\\mathcal{H}$-Consistency Bounds","created":"2025-11-19","cited_by_count":7},{"id":"1702.07958","title":"Efficient Online Bandit Multiclass Learning with $\\tilde{O}(\\sqrt{T})$ Regret","created":"2017-02-25","cited_by_count":6},{"id":"2512.23133","title":"Principled Algorithms for Optimizing Generalized Metrics in Binary Classification","created":"2025-12-29","cited_by_count":6},{"id":"2601.22538","title":"Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models","created":"2026-01-30","cited_by_count":6},{"id":"2409.20489","title":"Online Decision Deferral under Budget Constraints","created":"2024-09-30","cited_by_count":5},{"id":"2510.10988","title":"Adversarial Robustness in One-Stage Learning-to-Defer","created":"2025-10-13","cited_by_count":5},{"id":"2604.09414","title":"Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer","created":"2026-04-10","cited_by_count":5},{"id":"2602.17554","title":"A Theoretical Framework for Modular Learning of Robust Generative Models","created":"2026-02-19","cited_by_count":4},{"id":"2604.27723","title":"Optimized Deferral for Imbalanced Settings","created":"2026-04-30","cited_by_count":2},{"id":"2604.27742","title":"Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction","created":"2026-04-30","cited_by_count":2},{"id":"2605.02435","title":"Generalized Distributional Alignment Games for Unbiased Answer-Level Fine-Tuning","created":"2026-05-04","cited_by_count":2}],"next_cursor":null}