Image de couverture de livre

Corruptions of Supervised Learning Problems: Typology and Mitigations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Iacovissi, Laura, Lu, Nan, Williamson, Robert C.
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Geometry and Stability of Supervised Learning Problems
par: Mémoli, Facundo, et autres
Publié: (2024)

Self-Supervised Learning with Gaussian Processes
par: Duan, Yunshan, et autres
Publié: (2025)

The Rhetoric of Machine Learning
par: Williamson, Robert C.
Publié: (2026)

A Typology for Exploring the Mitigation of Shortcut Behavior
par: Friedrich, Felix, et autres
Publié: (2022)

Insights From Insurance for Fair Machine Learning
par: Fröhlich, Christian, et autres
Publié: (2023)

Understanding and Mitigating Dataset Corruption in LLM Steering
par: Anderson, Cullen, et autres
Publié: (2026)

Forecast Evaluation and the Relationship of Regret and Calibration
par: Derr, Rabanus, et autres
Publié: (2024)

The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World
par: Höltgen, Benedikt, et autres
Publié: (2024)

On Corruption-Robustness in Performative Reinforcement Learning
par: Pollatos, Vasilis, et autres
Publié: (2025)

Whole-Genome Phenotype Prediction with Machine Learning: Open Problems in Bacterial Genomics
par: James, Tamsin, et autres
Publié: (2025)

Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning
par: Xiao, Qian, et autres
Publié: (2024)

Can Language Models Learn Typologically Implausible Languages?
par: Xu, Tianyang, et autres
Publié: (2025)

Formalising causal inference as prediction on a target population
par: Höltgen, Benedikt, et autres
Publié: (2024)

Scoring Rules and Calibration for Imprecise Probabilities
par: Fröhlich, Christian, et autres
Publié: (2024)

Risk Measures and Upper Probabilities: Coherence and Stratification
par: Fröhlich, Christian, et autres
Publié: (2022)

Sparse Offline Reinforcement Learning with Corruption Robustness
par: Tran, Nam Phuong, et autres
Publié: (2025)

Feature-Wise Mixing for Mitigating Contextual Bias in Predictive Supervised Learning
par: Tomar, Yash Vardhan
Publié: (2025)

Three Types of Calibration with Properties and their Semantic and Formal Relationships
par: Derr, Rabanus, et autres
Publié: (2025)

Learning with Monotone Adversarial Corruptions
par: Larsen, Kasper Green, et autres
Publié: (2026)

Provably Mitigating Corruption, Overoptimization, and Verbosity Simultaneously in Offline and Online RLHF/DPO Alignment
par: Chen, Ziyi, et autres
Publié: (2025)

Robust Distribution Learning with Local and Global Adversarial Corruptions
par: Nietert, Sloan, et autres
Publié: (2024)

Robust Reinforcement Learning from Corrupted Human Feedback
par: Bukharin, Alexander, et autres
Publié: (2024)

Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels
par: Lu, Yangdi, et autres
Publié: (2024)

Corrupted Learning Dynamics in Games
par: Tsuchiya, Taira, et autres
Publié: (2024)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation
par: Ye, Chenlu, et autres
Publié: (2023)

A Model Selection Approach for Corruption Robust Reinforcement Learning
par: Wei, Chen-Yu, et autres
Publié: (2021)

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
par: Kim, Suyeon, et autres
Publié: (2024)

Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
par: Wołczyk, Maciej, et autres
Publié: (2024)

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
par: Ye, Chenlu, et autres
Publié: (2024)

An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm
par: Pacheco, Armando J. Cabrera, et autres
Publié: (2024)

Sparse Robust Classification via the Kernel Mean
par: van Rooyen, Brendan, et autres
Publié: (2015)

Weakly Supervised Label Learning Flows
par: Lu, You, et autres
Publié: (2023)

Corruption Robust Offline Reinforcement Learning with Human Feedback
par: Mandal, Debmalya, et autres
Publié: (2024)

Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics
par: Samanta, Akash, et autres
Publié: (2025)

Corruption-Robust Lipschitz Contextual Search
par: Zuo, Shiliang
Publié: (2023)

Cascading Bandits Robust to Adversarial Corruptions
par: Xie, Jize, et autres
Publié: (2025)

Robust Bayesian Optimisation with Unbounded Corruptions
par: Ezzerg, Abdelhamid, et autres
Publié: (2025)

Subliminal Corruption: Mechanisms, Thresholds, and Interpretability
par: Vir, Reya, et autres
Publié: (2025)

Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
par: Ghaffari, Fatemeh, et autres
Publié: (2025)

Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
par: Chen, Yankai, et autres
Publié: (2026)