"distribution shift" Papers
61 papers found • Page 1 of 2
Conference
Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning
Yixiu Mao, Yun Qu, Qi Wang et al.
AdaWM: Adaptive World Model based Planning for Autonomous Driving
Hang Wang, Xin Ye, Feng Tao et al.
ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
Xincheng Yao, Yan Luo, Zefeng Qian et al.
A Layer Selection Approach to Test Time Adaptation
Sabyasachi Sahoo, Mostafa ElAraby, Jonas Ngnawe et al.
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization
Xiran Wang, Jian Zhang, Lei Qi et al.
Bayesian Test-Time Adaptation for Vision-Language Models
Lihua Zhou, Mao Ye, Shuaifeng Li et al.
Boosting Test Performance with Importance Sampling--a Subpopulation Perspective
Hongyu Shen, Zhizhen Zhao
Bridging the Gap for Test-Time Multimodal Sentiment Analysis
Zirun Guo, Tao Jin, Wenlong Xu et al.
CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor
Han Ji, Yuqi Feng, Jiahao Fan et al.
Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization
Han Wang, Chao Ning
CUQDS: Conformal Uncertainty Quantification Under Distribution Shift for Trajectory Prediction
Huiqun Huang, Sihong He, Fei Miao
Deep Kernel Relative Test for Machine-generated Text Detection
Yiliao Song, Zhenqiao Yuan, Shuhai Zhang et al.
Early Concept Drift Detection via Prediction Uncertainty
Pengqian Lu, Jie Lu, Anjin Liu et al.
Epistemic Uncertainty for Generated Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu et al.
Error-quantified Conformal Inference for Time Series
Junxi Wu, Dongjian Hu, Yajie Bao et al.
Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection
Zining Chen, Xingshuang Luo, Weiqiu Wang et al.
Geometry of Long-Tailed Representation Learning: Rebalancing Features for Skewed Distributions
Lingjie Yi, Michael Yao, Weimin Lyu et al.
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws
Muhammed Ildiz, Halil Gozeten, Ege Taga et al.
Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning
Yaxin Hou, Bo Han, Yuheng Jia et al.
Knowledge Distillation of Uncertainty using Deep Latent Factor Model
Sehyun Park, Jongjin Lee, Yunseop Shin et al.
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees
Gautam Chandrasekaran, Adam Klivans, Lin Lin Lee et al.
Learning Robust Spectral Dynamics for Temporal Domain Generalization
En Yu, Jie Lu, Xiaoyu Yang et al.
Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
Tian Gao, Songtao Lu, Junkyu Lee et al.
MetaOOD: Automatic Selection of OOD Detection Models
Yuehan Qin, Yichi Zhang, Yi Nian et al.
Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift
Zhen Zhang, Xin Liu, Shaoli Wang et al.
Mixture of Online and Offline Experts for Non-Stationary Time Series
Zhilin Zhao, Longbing Cao, Yuanyu Wan
Noisy Test-Time Adaptation in Vision-Language Models
Chentao Cao, Zhun Zhong, (Andrew) Zhanke Zhou et al.
Optimizing importance weighting in the presence of sub-population shifts
Floris Holstege, Bram Wouters, Noud Giersbergen et al.
Processing and acquisition traces in visual encoders: What does CLIP know about your camera?
Ryan Ramos, Vladan Stojnić, Giorgos Kordopatis-Zilos et al.
Progressive Test Time Energy Adaptation for Medical Image Segmentation
Xiaoran Zhang, Byung-Woo Hong, Hyoungseob Park et al.
RTDiff: Reverse Trajectory Synthesis via Diffusion for Offline Reinforcement Learning
Qianlan Yang, Yu-Xiong Wang
Synchronizing Task Behavior: Aligning Multiple Tasks during Test-Time Training
Wooseong Jeong, Jegyeong Cho, Youngho Yoon et al.
Test-Time Ensemble via Linear Mode Connectivity: A Path to Better Adaptation
Byungjai Kim, Chanho Ahn, Wissam Baddar et al.
Wasserstein-Regularized Conformal Prediction under General Distribution Shift
Rui Xu, Chao Chen, Yue Sun et al.
A Noisy Elephant in the Room: Is Your Out-of-Distribution Detector Robust to Label Noise?
Galadrielle Humblot-Renaux, Sergio Escalera, Thomas B. Moeslund
Conformal Risk Control
Anastasios Angelopoulos, Stephen Bates, Adam Fisch et al.
Cross-domain Open-world Discovery
Shuo Wen, Maria Brbic
Distributionally Robust Loss for Long-Tailed Multi-Label Image Classification
Dekun Lin, Zhe Cui, Rui Chen et al.
Domain-wise Data Acquisition to Improve Performance under Distribution Shift
Yue He, Dongbai Li, Pengfei Tian et al.
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Shengwei An, Sheng-Yen Chou, Kaiyuan Zhang et al.
Ensemble Pruning for Out-of-distribution Generalization
Fengchun Qiao, Xi Peng
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
Zhonghang Li, Lianghao Xia, Yong Xu et al.
HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation
Tianpei Zou, Sanqing Qu, Zhijun Li et al.
Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving
Zhenghao Peng, Wenjie Luo, Yiren Lu et al.
Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
Da Wang, Lin Li, Wei Wei et al.
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin et al.
Modality Translation for Object Detection Adaptation without forgetting prior knowledge
Heitor Rapela Medeiros, Masih Aminbeidokhti, Fidel A Guerrero Pena et al.
Multi-Source Conformal Inference Under Distribution Shift
Yi Liu, Alexander Levis, Sharon-Lise Normand et al.
On Pretraining Data Diversity for Self-Supervised Learning
Hasan Abed El Kader Hammoud, Tuhin Das, Fabio Pizzati et al.
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
Lin Li, Yifei Wang, Chawin Sitawarin et al.