Image de couverture de livre

From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chang, Trenton, Wiens, Jenna
Format:	Preprint
Publié:	2024
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

Expectation Maximization Pseudo Labels
par: Xu, Moucheng, et autres
Publié: (2023)

Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence
par: Chen, Winston, et autres
Publié: (2025)

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
par: Kou, Zhiqiang, et autres
Publié: (2025)

A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs
par: Chang, Trenton, et autres
Publié: (2025)

Stochastic Approximation with Biased MCMC for Expectation Maximization
par: Gruffaz, Samuel, et autres
Publié: (2024)

Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability
par: Watanabe, Shotaro, et autres
Publié: (2022)

Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
par: Mai, Tan-Ha, et autres
Publié: (2026)

CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
par: Tian, Shiyu, et autres
Publié: (2023)

Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation
par: Chang, Trenton, et autres
Publié: (2024)

Learning with Selectively Labeled Data from Multiple Decision-makers
par: Chen, Jian, et autres
Publié: (2023)

Random Search Neural Networks for Efficient and Expressive Graph Learning
par: Ito, Michael, et autres
Publié: (2025)

Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making
par: He, Liu
Publié: (2026)

PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph
par: Zhang, Shengtao, et autres
Publié: (2025)

Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
par: Wang, Yingxu, et autres
Publié: (2025)

A Confidence-Variance Theory for Pseudo-Label Selection in Semi-Supervised Learning
par: Liu, Jinshi, et autres
Publié: (2026)

Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
par: Saravanan, Darshana, et autres
Publié: (2024)

Smooth Pseudo-Labeling
par: Karaliolios, Nikolaos, et autres
Publié: (2024)

Confident Sinkhorn Allocation for Pseudo-Labeling
par: Nguyen, Vu, et autres
Publié: (2022)

PLGC: Pseudo-Labeled Graph Condensation
par: Nandy, Jay, et autres
Publié: (2026)

Understanding GNNs and Homophily in Dynamic Node Classification
par: Ito, Michael, et autres
Publié: (2025)

De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data
par: Tenev, Nicholas
Publié: (2024)

Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective
par: Zapzalka, Dylan, et autres
Publié: (2025)

Learning from a Biased Sample
par: Sahoo, Roshni, et autres
Publié: (2022)

Curriculum Learning and Pseudo-Labeling Improve the Generalization of Multi-Label Arabic Dialect Identification Models
par: Mekky, Ali, et autres
Publié: (2026)

Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
par: Sun, Xueqing, et autres
Publié: (2025)

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
par: Romero, David W.
Publié: (2024)

Meta-Learning Neural Procedural Biases
par: Raymond, Christian, et autres
Publié: (2024)

Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases
par: Morishita, Gota
Publié: (2026)

Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
par: Gao, Wanfu, et autres
Publié: (2025)

Learning Laplacian Positional Encodings for Heterophilous Graphs
par: Ito, Michael, et autres
Publié: (2025)

SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
par: Azizi, Ilia, et autres
Publié: (2024)

Transformers Are Born Biased: Structural Inductive Biases at Random Initialization and Their Practical Consequences
par: Li, Siquan, et autres
Publié: (2026)

Learning Diffusion Priors from Observations by Expectation Maximization
par: Rozet, François, et autres
Publié: (2024)

Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
par: Qiao, Congyu, et autres
Publié: (2024)

Fuzzy Label: From Concept to Its Application in Label Learning
par: Luoa, Chenxi, et autres
Publié: (2025)

Fast Graph Generation via Autoregressive Noisy Filtration Modeling
par: Krimmel, Markus, et autres
Publié: (2025)

Language Models Need Inductive Biases to Count Inductively
par: Chang, Yingshan, et autres
Publié: (2024)

Robust Classification with Noisy Labels Based on Posterior Maximization
par: Novello, Nicola, et autres
Publié: (2025)

Measuring Model Performance in the Presence of an Intervention
par: Chen, Winston, et autres
Publié: (2025)

Regretful Decisions under Label Noise
par: Nagaraj, Sujay, et autres
Publié: (2025)