Poster "distribution shift" Papers
44 papers found
Conference
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.
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.
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
Deep Kernel Relative Test for Machine-generated Text Detection
Yiliao Song, Zhenqiao Yuan, Shuhai Zhang et al.
Epistemic Uncertainty for Generated Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu 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.
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.
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.
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
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.
Ensemble Pruning for Out-of-distribution Generalization
Fengchun Qiao, Xi Peng
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.
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.
Performative Prediction with Bandit Feedback: Learning through Reparameterization
Yatong Chen, Wei Tang, Chien-Ju Ho et al.
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Zining Chen, Weiqiu Wang, Zhicheng Zhao et al.
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
Amrith Setlur, Saurabh Garg, Virginia Smith et al.
Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
Dapeng Hu, Jian Liang, Xinchao Wang et al.
Reducing Balancing Error for Causal Inference via Optimal Transport
Yuguang Yan, Hao Zhou, Zeqin Yang et al.
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
Shenghua Wan, Ziyuan Chen, Le Gan et al.
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
Yu Yongcan, Lijun Sheng, Ran He et al.
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Daehee Park, Jaeseok Jeong, Sung-Hoon Yoon et al.
TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules
Weijieying Ren, Xiaoting Li, Huiyuan Chen et al.
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen et al.
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
Sepanta Zeighami, Cyrus Shahabi