2024 "out-of-distribution generalization" Papers
36 papers found
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Zhihai Wang, Lei Chen, Jie Wang et al.
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil et al.
A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
Baohong Li, Haoxuan Li, Anpeng Wu et al.
Amend to Alignment: Decoupled Prompt Tuning for Mitigating Spurious Correlation in Vision-Language Models
Jie ZHANG, Xiaosong Ma, Song Guo et al.
Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
Xueyang Tang, Song Guo, Jingcai Guo et al.
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
Leo Klarner, Tim G. J. Rudner, Garrett Morris et al.
CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection
Lin Zhu, Yifeng Yang, Qinying Gu et al.
Debiasing Multimodal Sarcasm Detection with Contrastive Learning
Mengzhao Jia, Can Xie, Liqiang Jing
Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach
Ziliang Chen, Yongsen Zheng, Zhao-Rong Lai et al.
Discovering Environments with XRM
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim et al.
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
Xilong Zhao, Siyuan Bian, Yaoyun Zhang et al.
Empowering Graph Invariance Learning with Deep Spurious Infomax
Tianjun Yao, Yongqiang Chen, Zhenhao Chen et al.
Ensemble Pruning for Out-of-distribution Generalization
Fengchun Qiao, Xi Peng
Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize
Tianren Zhang, Chujie Zhao, Guanyu Chen et al.
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
Fan Wu, Jinling Gao, Lanqing Hong et al.
Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization
Tianrui Jia, Haoyang Li, Cheng Yang et al.
Graph Structure Extrapolation for Out-of-Distribution Generalization
Xiner Li, Shurui Gui, Youzhi Luo et al.
Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
Da Wang, Lin Li, Wei Wei et al.
Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
Yufan LIAO, Qi Wu, Xing Yan
Invariant Risk Minimization Is A Total Variation Model
Zhao-Rong Lai, Weiwen Wang
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
Learning Visual Abstract Reasoning through Dual-Stream Networks
Kai Zhao, Chang Xu, Bailu Si
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui et al.
MonoTTA: Fully Test-Time Adaptation for Monocular 3D Object Detection
Hongbin Lin, Yifan Zhang, SHUAICHENG NIU et al.
MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting
Wanlin Cai, Yuxuan Liang, Xianggen Liu et al.
NOVUM: Neural Object Volumes for Robust Object Classification
Artur Jesslen, Guofeng Zhang, Angtian Wang et al.
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
Lin Li, Yifei Wang, Chawin Sitawarin et al.
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
Yuanzhao Zhai, Yiying Li, Zijian Gao et al.
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras et al.
Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
Hao Wu, Huiyuan Wang, kun wang et al.
RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction
Yemin Yu, Luotian Yuan, Ying WEI et al.
Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data
Nikita Tsoy, Nikola Konstantinov
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong et al.
Training-free Video Temporal Grounding using Large-scale Pre-trained Models
Minghang Zheng, Xinhao Cai, Qingchao Chen et al.
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot
Zixuan Wang, Stanley Wei, Daniel Hsu et al.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang et al.