Copertina

Predicting Classification Accuracy When Adding New Unobserved Classes

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Slavutsky, Yuli, Benjamini, Yuval
Natura:	Preprint
Pubblicazione:	2020
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations
di: Slavutsky, Yuli, et al.
Pubblicazione: (2023)

Confident Feature Ranking
di: Neuhof, Bitya, et al.
Pubblicazione: (2023)

Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status
di: Lee, Samuel, et al.
Pubblicazione: (2024)

Robust Representation Learning through Explicit Environment Modeling
di: Slavutsky, Yuli, et al.
Pubblicazione: (2026)

Quantifying Uncertainty in the Presence of Distribution Shifts
di: Slavutsky, Yuli, et al.
Pubblicazione: (2025)

CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models
di: Wagner, Eitan, et al.
Pubblicazione: (2024)

Adaptive Reinforcement Learning for Unobservable Random Delays
di: Wikman, John, et al.
Pubblicazione: (2025)

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
di: Yang, Yuqin, et al.
Pubblicazione: (2024)

Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
di: Shao, Daqian, et al.
Pubblicazione: (2025)

Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders
di: Du, Xiaojing, et al.
Pubblicazione: (2025)

Adding Conditional Control to Diffusion Models with Reinforcement Learning
di: Zhao, Yulai, et al.
Pubblicazione: (2024)

Input Adaptive Bayesian Model Averaging
di: Slavutsky, Yuli, et al.
Pubblicazione: (2025)

Aggregating Concepts of Fairness and Accuracy in Prediction Algorithms
di: Kinney, David
Pubblicazione: (2025)

A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions
di: Shi, Jie, et al.
Pubblicazione: (2026)

CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
di: Carlin, Lee, et al.
Pubblicazione: (2025)

Demystifying the Optimal Fair Classifier in Multi-Class Classification
di: Zhang, Li, et al.
Pubblicazione: (2026)

NC-NCD: Novel Class Discovery for Node Classification
di: Hou, Yue, et al.
Pubblicazione: (2024)

Multi-Scenario Highway Lane-Change Intention Prediction: A Physics-Informed AI Framework for Three-Class Classification
di: Shi, Jiazhao, et al.
Pubblicazione: (2025)

CoRPO: Adding a Correctness Bias to GRPO Improves Generalization
di: Garg, Anisha, et al.
Pubblicazione: (2025)

A Class-Aware Representation Refinement Framework for Graph Classification
di: Xu, Jiaxing, et al.
Pubblicazione: (2022)

Prediction Error-based Classification for Class-Incremental Learning
di: Zając, Michał, et al.
Pubblicazione: (2023)

Know When to Abstain: Optimal Selective Classification with Likelihood Ratios
di: Heng, Alvin, et al.
Pubblicazione: (2025)

Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost
di: Maier, Jannis, et al.
Pubblicazione: (2024)

On the Correlation between Individual Fairness and Predictive Accuracy in Probabilistic Models
di: Antonucci, Alessandro, et al.
Pubblicazione: (2025)

Neural Generalized Mixed-Effects Models
di: Slavutsky, Yuli, et al.
Pubblicazione: (2026)

Focus on Likely Classes for Test-Time Prediction
di: Schneider, Johannes
Pubblicazione: (2025)

A Joint Topology-Data Fusion Graph Network for Robust Traffic Speed Prediction with Data Anomalism
di: Jiang, Ruiyuan, et al.
Pubblicazione: (2025)

Prediction Accuracy & Reliability: Classification and Object Localization under Distribution Shift
di: Diet, Fabian, et al.
Pubblicazione: (2024)

Variational Learning of Disentangled Representations
di: Slavutsky, Yuli, et al.
Pubblicazione: (2025)

VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification
di: Hu, Yulan, et al.
Pubblicazione: (2023)

Mini-Batch Class Composition Bias in Link Prediction
di: Maguire, Kieran, et al.
Pubblicazione: (2026)

When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes
di: Yehudai, Asaf, et al.
Pubblicazione: (2024)

Few-shot Class-incremental Learning for Classification and Object Detection: A Survey
di: Zhang, Jinghua, et al.
Pubblicazione: (2023)

BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
di: Wang, Qian, et al.
Pubblicazione: (2024)

Balancing Fairness and Accuracy in Data-Restricted Binary Classification
di: Lazri, Zachary McBride, et al.
Pubblicazione: (2024)

Conformal Prediction of Classifiers with Many Classes based on Noisy Labels
di: Penso, Coby, et al.
Pubblicazione: (2025)

Rare Class Prediction Model for Smart Industry in Semiconductor Manufacturing
di: Farrag, Abdelrahman, et al.
Pubblicazione: (2024)

When Shallow Wins: Silent Failures and the Depth-Accuracy Paradox in Latent Reasoning
di: Sahoo, Subramanyam, et al.
Pubblicazione: (2026)

When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
di: Xia, Riting, et al.
Pubblicazione: (2025)

When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
di: Schäfer, Lukas, et al.
Pubblicazione: (2026)