:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cha, J., Lee, J., Cho, J., Shin, J.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2509.13218
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FOSSIL: Regret-Minimizing Curriculum Learning for Metadata-Free and Low-Data Mpox Diagnosis
by: Han, Sahng-Min, et al.
Published: (2025)

A weighted-likelihood framework for class imbalance in Bayesian prediction models
by: Lazic, Stanley E.
Published: (2025)

Distributionally robust minimization in meta-learning for system identification
by: Rufolo, Matteo, et al.
Published: (2025)

Regret minimization in Linear Bandits with offline data via extended D-optimal exploration
by: Vijayan, Sushant, et al.
Published: (2025)

Robust inference for risk heterogeneity under group imbalance
by: Xu, Mengqi, et al.
Published: (2026)

Why Alignment Must Precede Distillation: A Minimal Working Explanation
by: Cha, Sungmin, et al.
Published: (2025)

Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
by: Cha, Sungmin, et al.
Published: (2025)

Fake detection in imbalance dataset by Semi-supervised learning with GAN
by: Bordbar, Jinus, et al.
Published: (2022)

Regretful Decisions under Label Noise
by: Nagaraj, Sujay, et al.
Published: (2025)

On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
by: Maiti, Arnab, et al.
Published: (2023)

Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
by: Cha, Sungmin, et al.
Published: (2024)

Distributionally robust self-supervised learning for tabular data
by: Ghosh, Shantanu, et al.
Published: (2024)

TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design
by: Cho, Geonwoo, et al.
Published: (2025)

Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions
by: Park, Sungwoo, et al.
Published: (2024)

Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift
by: Shin, Bong Gyun, et al.
Published: (2026)

Laser interferometry as a robust neuromorphic platform for machine learning
by: Anteneh, Amanuel, et al.
Published: (2026)

Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
by: Cha, Sungmin, et al.
Published: (2023)

Hyperparameters in Continual Learning: A Reality Check
by: Cha, Sungmin, et al.
Published: (2024)

Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
by: Lee, Junghyun, et al.
Published: (2023)

Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers
by: Cha, Sungmin, et al.
Published: (2023)

Improved Regret of Linear Ensemble Sampling
by: Lee, Harin, et al.
Published: (2024)

Logarithmic Regret for Nonlinear Control
by: Wang, James, et al.
Published: (2025)

No-Regret Linear Bandits under Gap-Adjusted Misspecification
by: Liu, Chong, et al.
Published: (2025)

Fast weight programming and linear transformers: from machine learning to neurobiology
by: Irie, Kazuki, et al.
Published: (2025)

Robust variance-regularized risk minimization with concomitant scaling
by: Holland, Matthew J.
Published: (2023)

Regret Analysis of Repeated Delegated Choice
by: Hajiaghayi, MohammadTaghi, et al.
Published: (2023)

Conformalised imprecise inference for robust extrapolation under limited data
by: Chen, Yu, et al.
Published: (2026)

Regret of exploratory policy improvement and $q$-learning
by: Tang, Wenpin, et al.
Published: (2024)

MX+: Pushing the Limits of Microscaling Formats for Efficient Large Language Model Serving
by: Lee, Jungi, et al.
Published: (2025)

Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion
by: Shin, Jaeyong, et al.
Published: (2025)

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
by: Gouverneur, Amaury, et al.
Published: (2024)

NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
by: Kang, Honggu, et al.
Published: (2023)

Dynamic Regret Reduces to Kernelized Static Regret
by: Jacobsen, Andrew, et al.
Published: (2025)

CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning
by: Birkbeck, John, et al.
Published: (2024)

Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
by: Lee, Joongkyu, et al.
Published: (2024)

Consistency-Preserving Concept Erasure via Unsafe-Safe Pairing and Directional Fisher-weighted Adaptation
by: Kim, Yongwoo, et al.
Published: (2026)

Buffer replay enhances the robustness of multimodal learning under missing-modality
by: Zhu, Hongye, et al.
Published: (2025)

Queue Length Regret Bounds for Contextual Queueing Bandits
by: Bae, Seoungbin, et al.
Published: (2026)

The dynamic interplay between in-context and in-weight learning in humans and neural networks
by: Russin, Jacob, et al.
Published: (2024)

Forget Forgetting: Continual Learning in a World of Abundant Memory
by: Cho, Dongkyu, et al.
Published: (2025)