Image de couverture de livre

Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Slavutsky, Yuli, Benjamini, Yuval
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

Predicting Classification Accuracy When Adding New Unobserved Classes
par: Slavutsky, Yuli, et autres
Publié: (2020)

Robust Representation Learning through Explicit Environment Modeling
par: Slavutsky, Yuli, et autres
Publié: (2026)

Quantifying Uncertainty in the Presence of Distribution Shifts
par: Slavutsky, Yuli, et autres
Publié: (2025)

Variational Learning of Disentangled Representations
par: Slavutsky, Yuli, et autres
Publié: (2025)

Input Adaptive Bayesian Model Averaging
par: Slavutsky, Yuli, et autres
Publié: (2025)

CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
par: Carlin, Lee, et autres
Publié: (2025)

Neural Generalized Mixed-Effects Models
par: Slavutsky, Yuli, et autres
Publié: (2026)

Confident Feature Ranking
par: Neuhof, Bitya, et autres
Publié: (2023)

Learning Clinical Representations Under Systematic Distribution Shift
par: Zhang, Yuanyun, et autres
Publié: (2026)

Towards Robust Zero-Shot Reinforcement Learning
par: Zheng, Kexin, et autres
Publié: (2025)

Zero-Shot Object-Centric Representation Learning
par: Didolkar, Aniket, et autres
Publié: (2024)

TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning
par: Bagatella, Marco, et autres
Publié: (2025)

CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts
par: Jia, Shunbo, et autres
Publié: (2025)

Learning Divergence Fields for Shift-Robust Graph Representations
par: Wu, Qitian, et autres
Publié: (2024)

Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
par: Xue, Yihao, et autres
Publié: (2023)

How Do the Architecture and Optimizer Affect Representation Learning? On the Training Dynamics of Representations in Deep Neural Networks
par: Sharon, Yuval, et autres
Publié: (2024)

Robust Invariant Representation Learning by Distribution Extrapolation
par: Yoshida, Kotaro, et autres
Publié: (2025)

On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot Learning
par: Tavares, Tiago, et autres
Publié: (2024)

Learning Optimal Classification Trees Robust to Distribution Shifts
par: Justin, Nathan, et autres
Publié: (2023)

Few-Shot Inspired Generative Zero-Shot Learning
par: Shohag, Md Shakil Ahamed, et autres
Publié: (2025)

Few-Shot Class Incremental Learning via Robust Transformer Approach
par: Paeedeh, Naeem, et autres
Publié: (2024)

Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting
par: Liu, Anxian, et autres
Publié: (2025)

On Zero-Shot Reinforcement Learning
par: Jeen, Scott
Publié: (2025)

CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer
par: Yilmaz, Rüveyda, et autres
Publié: (2025)

Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
par: Najafi, Amir, et autres
Publié: (2024)

Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications
par: Flovik, Vegard
Publié: (2024)

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
par: Thaker, Pratiksha, et autres
Publié: (2023)

Zero-Shot Learning for Obsolescence Risk Forecasting
par: Saad, Elie, et autres
Publié: (2025)

Robust Visual Representation Learning with Multi-modal Prior Knowledge for Image Classification Under Distribution Shift
par: Zhou, Hongkuan, et autres
Publié: (2024)

Zero-Shot Off-Policy Learning
par: Asadulaev, Arip, et autres
Publié: (2026)

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
par: Guo, Yongxin, et autres
Publié: (2023)

AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion
par: Li, Chenqi, et autres
Publié: (2024)

A Minimalist Prompt for Zero-Shot Policy Learning
par: Song, Meng, et autres
Publié: (2024)

Tackling the Zero-Shot Reinforcement Learning Loss Directly
par: Ollivier, Yann
Publié: (2025)

A Unified Framework for Zero-Shot Reinforcement Learning
par: Di Ventura, Jacopo, et autres
Publié: (2025)

Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach
par: Wang, Haoxuan, et autres
Publié: (2020)

Few-Shot Learning with Class Imbalance
par: Ochal, Mateusz, et autres
Publié: (2021)

Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
par: Ding, Pengfei, et autres
Publié: (2024)

Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions
par: Demir, Samet, et autres
Publié: (2025)

Approximate Bayesian Class-Conditional Models under Continuous Representation Shift
par: Lee, Thomas L., et autres
Publié: (2023)