:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Azar, Eyar, Nadler, Boaz
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2409.03335
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robustness of OLS to sample removals: Theoretical analysis and implications
by: Azar, Eyar, et al.
Published: (2025)

Distributed Sparse Linear Regression under Communication Constraints
by: Fonseca, Rodney, et al.
Published: (2023)

Semi-Supervised Crowd Counting from Unlabeled Data
by: Duan, Haoran, et al.
Published: (2021)

RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees
by: Laufer, Eilon Vaknin, et al.
Published: (2025)

Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
by: Zhou, Xuanning, et al.
Published: (2025)

AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
by: Wu, Zhiyu, et al.
Published: (2024)

Unlabeled Data Can Provably Enhance In-Context Learning of Transformers
by: Liu, Renpu, et al.
Published: (2026)

Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model
by: He, Rundong, et al.
Published: (2025)

Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data
by: Nakayama, Shogo, et al.
Published: (2026)

How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
by: Du, Xuefeng, et al.
Published: (2024)

A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion
by: Liu, Xiaoqian, et al.
Published: (2023)

On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation
by: Yu, Bing, et al.
Published: (2020)

High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
by: Yin, Yuxuan, et al.
Published: (2023)

CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
by: Park, Byoungjun, et al.
Published: (2025)

InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
by: Huang, Zhe, et al.
Published: (2024)

When and How Unlabeled Data Provably Improve In-Context Learning
by: Li, Yingcong, et al.
Published: (2025)

Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning
by: Gan, Kai, et al.
Published: (2024)

FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
by: Qiu, Xinchi, et al.
Published: (2024)

Bridged Clustering: Semi-Supervised Sparse Bridging
by: Ye, Patrick Peixuan, et al.
Published: (2025)

SpectralNet: Spectral Clustering using Deep Neural Networks
by: Shaham, Uri, et al.
Published: (2018)

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
by: Liu, Siyan, et al.
Published: (2024)

On Provable Benefits of Muon in Federated Learning
by: Zhang, Xinwen, et al.
Published: (2025)

Hypergraph Transformer for Semi-Supervised Classification
by: Liu, Zexi, et al.
Published: (2023)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
by: Kato, Masahiro
Published: (2025)

Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification
by: Grivas, Andreas, et al.
Published: (2023)

Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
by: Kögler, Kevin, et al.
Published: (2024)

Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
by: Roy, Shuvendu, et al.
Published: (2023)

Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees
by: Du, Ally Yalei, et al.
Published: (2025)

Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning
by: Zhang, Liyi, et al.
Published: (2024)

Graph Semi-Supervised Learning for Point Classification on Data Manifolds
by: Netto, Caio F. Deberaldini, et al.
Published: (2025)

Provable Benefits of Sinusoidal Activation for Modular Addition
by: Huang, Tianlong, et al.
Published: (2025)

Geometric Imbalance in Semi-Supervised Node Classification
by: Yan, Liang, et al.
Published: (2023)

SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
by: Xu, Yang, et al.
Published: (2023)

ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data
by: Barbaux, Melvin, et al.
Published: (2025)

Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum
by: Rajaraman, Nived, et al.
Published: (2026)

Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
by: Elbatel, Marawan, et al.
Published: (2024)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)

Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training
by: Bu, Dake, et al.
Published: (2025)

Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data
by: Mori, Junki, et al.
Published: (2024)

Provable Benefit of Cutout and CutMix for Feature Learning
by: Oh, Junsoo, et al.
Published: (2024)