Image de couverture de livre

FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Li, Sai, Zhang, Linjun
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Personalizing black-box models for nonparametric regression with minimax optimality
par: Li, Sai, et autres
Publié: (2026)

Efficient machine unlearning with minimax optimality
par: Xie, Jingyi, et autres
Publié: (2026)

Locally minimax optimal confidence sets for the best model
par: Kim, Ilmun, et autres
Publié: (2025)

Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion
par: Sadhukhan, Tathagata, et autres
Publié: (2024)

Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness
par: Hou, Xiaotian, et autres
Publié: (2024)

Contrastive Network Representation Learning
par: Dong, Zihan, et autres
Publié: (2025)

Bias-Corrected Data Synthesis for Imbalanced Learning
par: Lyu, Pengfei, et autres
Publié: (2025)

Statistical Inference for Differentially Private Stochastic Gradient Descent
par: Xia, Xintao, et autres
Publié: (2025)

Doubly Robust Conditional Independence Testing with Generative Neural Networks
par: Zhang, Yi, et autres
Publié: (2024)

Unified Inference Framework for Single and Multi-Player Performative Prediction: Method and Asymptotic Optimality
par: Zhang, Zhixian, et autres
Publié: (2026)

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
par: Zhang, Zhe, et autres
Publié: (2024)

Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation
par: Zhang, Chenbin, et autres
Publié: (2025)

Federated fairness-aware classification under differential privacy
par: Xue, Gengyu, et autres
Publié: (2026)

Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
par: Cai, T. Tony, et autres
Publié: (2023)

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback
par: Zhong, Huiying, et autres
Publié: (2024)

Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks
par: Zhang, Lujing, et autres
Publié: (2024)

A Unified Combination Framework for Dependent Tests with Applications to Microbiome Association Studies
par: Yu, Xiufan, et autres
Publié: (2024)

MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment
par: Wang, Tianze, et autres
Publié: (2025)

A general framework for inference on algorithm-agnostic variable importance
par: Williamson, Brian D., et autres
Publié: (2020)

Approximately optimal domain adaptation with Fisher's Linear Discriminant
par: Helm, Hayden S., et autres
Publié: (2023)

Transfer Q-learning
par: Chen, Elynn, et autres
Publié: (2022)

Predictive variational inference: Learn the predictively optimal posterior distribution
par: Lai, Jinlin, et autres
Publié: (2024)

A non-parametric optimal design algorithm for population pharmacokinetics
par: Hovd, Markus, et autres
Publié: (2025)

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning
par: Zhou, Qin, et autres
Publié: (2026)

Minimax optimal adaptive structured transfer learning through semi-parametric domain-varying coefficient model
par: Chen, Hanxiao, et autres
Publié: (2026)

High-dimensional semi-supervised learning: in search for optimal inference of the mean
par: Zhang, Yuqian, et autres
Publié: (2019)

Causal inference using invariant prediction: identification and confidence intervals
par: Peters, Jonas, et autres
Publié: (2015)

New insights into Elo algorithm for practitioners and statisticians
par: Szczecinski, Leszek
Publié: (2026)

Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities
par: Li, Fengyi, et autres
Publié: (2024)

Pareto optimal proxy metrics
par: Zito, Alessandro, et autres
Publié: (2023)

The data augmentation algorithm
par: Roy, Vivekananda, et autres
Publié: (2024)

Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemia
par: Gachon, Erell, et autres
Publié: (2024)

Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
par: Sauer, Christina, et autres
Publié: (2024)

Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
par: Dong, Zihan, et autres
Publié: (2026)

Trunc-Opt vine building algorithms
par: Pfeifer, Dániel, et autres
Publié: (2025)

Online monotone density estimation and log-optimal calibration
par: Hore, Rohan, et autres
Publié: (2026)

Computing high-dimensional optimal transport by flow neural networks
par: Xu, Chen, et autres
Publié: (2023)

Towards minimax optimal algorithms for Active Simple Hypothesis Testing
par: Vijayan, Sushant
Publié: (2025)

MM-algorithms for traditional and convex NMF with Tweedie and Negative Binomial cost functions and empirical evaluation
par: James, Elisabeth Sommer, et autres
Publié: (2026)

A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions
par: Abry, Patrice, et autres
Publié: (2025)