:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xie, Renchunzi, Odonnat, Ambroise, Feofanov, Vasilii, Deng, Weijian, Zhang, Jianfeng, An, Bo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.18979
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
by: Xie, Renchunzi, et al.
Published: (2024)

Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
by: Odonnat, Ambroise, et al.
Published: (2023)

Layer by layer, module by module: Choose both for optimal OOD probing of ViT
by: Odonnat, Ambroise, et al.
Published: (2026)

Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
by: Ilbert, Romain, et al.
Published: (2024)

SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
by: Ilbert, Romain, et al.
Published: (2024)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
by: Wen, Songkang, et al.
Published: (2024)

Clustering Head: A Visual Case Study of the Training Dynamics in Transformers
by: Odonnat, Ambroise, et al.
Published: (2024)

Optimal Self-Consistency for Efficient Reasoning with Large Language Models
by: Feng, Austin, et al.
Published: (2025)

Easing Optimization Paths: a Circuit Perspective
by: Odonnat, Ambroise, et al.
Published: (2025)

MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
by: Feofanov, Vasilii, et al.
Published: (2026)

Provable Benefits of In-Tool Learning for Large Language Models
by: Houliston, Sam, et al.
Published: (2025)

Vision Transformer Finetuning Benefits from Non-Smooth Components
by: Odonnat, Ambroise, et al.
Published: (2026)

Leveraging Generic Time Series Foundation Models for EEG Classification
by: Gnassounou, Théo, et al.
Published: (2025)

UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification
by: Moakher, Yessin, et al.
Published: (2026)

User-friendly Foundation Model Adapters for Multivariate Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2024)

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
by: Lalou, Yanis, et al.
Published: (2024)

Large Language Models as Markov Chains
by: Zekri, Oussama, et al.
Published: (2024)

Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2025)

Minimum-Norm Interpolation Under Covariate Shift
by: Mallinar, Neil, et al.
Published: (2024)

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
by: Benechehab, Abdelhakim, et al.
Published: (2025)

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers
by: Roschmann, Simon, et al.
Published: (2025)

Self-Training: A Survey
by: Amini, Massih-Reza, et al.
Published: (2022)

Minimax Optimal Estimation of Stability Under Distribution Shift
by: Namkoong, Hongseok, et al.
Published: (2022)

ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
by: Peng, Bo, et al.
Published: (2024)

LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
by: Roger, Alexis, et al.
Published: (2026)

Causal Imitation Learning Under Measurement Error and Distribution Shift
by: Bo, Shi, et al.
Published: (2026)

Zero-shot Model-based Reinforcement Learning using Large Language Models
by: Benechehab, Abdelhakim, et al.
Published: (2024)

MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm
by: Fan, Xiao, et al.
Published: (2025)

Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
by: Wilkins-Reeves, Steven, et al.
Published: (2026)

Online GNN Evaluation Under Test-time Graph Distribution Shifts
by: Zheng, Xin, et al.
Published: (2024)

Distribution Estimation under the Infinity Norm
by: Kontorovich, Aryeh, et al.
Published: (2024)

Learning Clinical Representations Under Systematic Distribution Shift
by: Zhang, Yuanyun, et al.
Published: (2026)

Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
by: Li, Yi, et al.
Published: (2024)

Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning
by: Kim, Dae-Won, et al.
Published: (2024)

Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
by: Tsyganov, Askar, et al.
Published: (2025)

Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift
by: Amoukou, Salim I., et al.
Published: (2026)

Confidence and Dispersity as Signals: Unsupervised Model Evaluation and Ranking
by: Deng, Weijian, et al.
Published: (2025)

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
by: Sun, Yanru, et al.
Published: (2024)

ASAP: Unsupervised Post-training with Label Distribution Shift Adaptive Learning Rate
by: Park, Heewon, et al.
Published: (2025)