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Classification

15,289 papers

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Papers

Content Moderation in Online Platforms: A Study of Annotation Methods for Inappropriate Language COLING 2024
Studying Reactions to Stereotypes in Teenagers: an Annotated Italian Dataset COLING 2024
Offensiveness, Hate, Emotion and GPT: Benchmarking GPT3.5 and GPT4 as Classifiers on Twitter-specific Datasets COLING 2024
Exploring Boundaries and Intensities in Offensive and Hate Speech: Unveiling the Complex Spectrum of Social Media Discourse COLING 2024
Introducing the Djinni Recruitment Dataset: A Corpus of Anonymized CVs and Job Postings COLING 2024
Introducing NER-UK 2.0: A Rich Corpus of Named Entities for Ukrainian COLING 2024
Multilingual Bias Detection and Mitigation for Indian Languages COLING 2024
Majority-of-Three: The Simplest Optimal Learner? COLT 2024
Regularization and Optimal Multiclass Learning COLT 2024
A Theory of Interpretable Approximations COLT 2024
Efficient Algorithms for Learning Monophonic Halfspaces in Graphs COLT 2024
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension COLT 2024
Learnability Gaps of Strategic Classification COLT 2024
Testable Learning of General Halfspaces with Adversarial Label Noise COLT 2024
On Computationally Efficient Multi-Class Calibration COLT 2024
The Star Number and Eluder Dimension: Elementary Observations About the Dimensions of Disagreement COLT 2024
List Sample Compression and Uniform Convergence COLT 2024
On the sample complexity of parameter estimation in logistic regression with normal design COLT 2024
Superconstant Inapproximability of Decision Tree Learning COLT 2024
Optimistic Rates for Learning from Label Proportions COLT 2024
Apple Tasting: Combinatorial Dimensions and Minimax Rates COLT 2024
Online Learning with Set-valued Feedback COLT 2024
Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss COLT 2024
Improved Hardness Results for Learning Intersections of Halfspaces COLT 2024
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency COLT 2024