Fairness-Preserving Regularizer: Balancing Core and Spurious Features
Jiawei Feng, Ancong Wu, YuHan Yao, Wei-Shi Zheng
Identifying and Disentangling Spurious Features in Pretrained Image Representations
Rafayel Darbinyan, Hrayr Harutyunyan, Aram H. Markosyan, Hrant Khachatrian
Pruning for Better Domain Generalizability
Xinglong Sun
Temporal Consistency based Test Time Adaptation: Towards Fair and Personalized AI
Mohammadmahdi Honarmand, Onur Cezmi Mutlu, Saimourya Surabhi, Dennis Wall
Regularizing Model Gradients with Concepts to Improve Robustness to Spurious Correlations
Yiwei Yang, Anthony Zhe Liu, Robert Wolfe, Aylin Caliskan, Bill Howe
Regularizing Adversarial Imitation Learning Using Causal Invariance
Ivan Ovinnikov, Joachim M. Buhmann
Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, Aditi Raghunathan
Spurious Correlations and Where to Find Them
Gautam Sreekumar, Vishnu Boddeti
Why is SAM Robust to Label Noise?
Christina Baek, J Zico Kolter, Aditi Raghunathan
Confident feature ranking
Bitya Neuhof, Yuval Benjamini
Calibrated Propensities for Causal Effect Estimation
Shachi Deshpande, Volodymyr Kuleshov
Understanding the Detrimental Class-level Effects of Data Augmentation
Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson
Transportable Representations for Out-of-distribution Generalization
Amirkasra Jalaldoust, Elias Bareinboim
Feature Selection in the Presence of Monotone Batch Effects
Peng Dai, Sina Baharlouei, Meisam Razaviyayn, Sze-Chuan Suen
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Tassilo Wald, Constantin Ulrich, Fabian Isensee, Gregor Koehler, David Zimmerer, Michael Baumgartner, Klaus Maier-Hein
Complementing a Policy with a Different Observation Space
Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
Yuzhen Mao, Zhun Deng, Huaxiu Yao, Ting Ye, Kenji Kawaguchi, James Zou
Separating multimodal modeling from multidimensional modeling for multimodal learning
Divyam Madaan, Taro Makino, Sumit Chopra, Kyunghyun Cho
Do as your neighbors: Invariant learning through non-parametric neighbourhood matching
Andrei Liviu Nicolicioiu, Jerry Huang, Dhanya Sridhar, Aaron Courville
Leveraging sparse and shared feature activations for disentangled representation learning
Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello
Implications of Gaussian process kernel mismatch for out-of-distribution data
Beau Coker, Finale Doshi-Velez
Which Features are Learned by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen, Baharan Mirzasoleiman
Cross-Risk Minimization: Inferring Groups Information for Improved Generalization
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
Robustness through Loss Consistency Regularization
Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami
Learning Diverse Features in Vision Transformers for Improved Generalization
Armand Mihai Nicolicioiu, Andrei Liviu Nicolicioiu, Bogdan Alexe, Damien Teney
Saving a Split for Last-layer Retraining can Improve Group Robustness without Group Annotations
Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
Sharpness-Aware Minimization Enhances Feature Diversity
Jacob Mitchell Springer, Vaishnavh Nagarajan, Aditi Raghunathan
ERM++: An Improved Baseline for Domain Generalization
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Kate Saenko, Bryan A. Plummer
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu
Group Fairness with Uncertainty in Sensitive Attributes
Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti
Arbitrary Decisions are a Hidden Cost of Differentially Private Training
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flavio Calmon
Causal-structure Driven Augmentations for Text OOD Generalization
Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David Blei
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard Zemel
Prediction without Preclusion: Recourse Verification with Reachable Sets
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng, Berk Ustun
Removing Multiple Biases through the Lens of Multi-task Learning
Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, Suha Kwak
Learning Independent Causal Mechanisms
Sarah Mameche, David Kaltenpoth, Jilles Vreeken
Towards Fair Knowledge Distillation using Student Feedback
Abhinav Java, Surgan Jandial, Chirag Agarwal
A Cosine Similarity-based Method for Out-of-Distribution Detection
Hieu Ngoc Nguyen, Nguyen Hung-Quang, The-Anh Ta, Thanh Nguyen-Tang, Khoa D Doan, Hoang Thanh-Tung
Reviving Shift Equivariance in Vision Transformers
Peijian Ding, Davit Soselia, Thomas Armstrong, Jiahao Su, Furong Huang
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Jiaqi Zhang, Chandler Squires, Kristjan Greenewald, Akash Srivastava, Karthikeyan Shanmugam, Caroline Uhler
Towards A Scalable Solution for Compositional Multi-Group Fair Classification
James Atwood, Tina Tian, Ben Packer, Meghana Deodhar, Jilin Chen, Alex Beutel, Flavien Prost, Ahmad Beirami
Towards Modular Learning of Deep Causal Generative Models
Md Musfiqur Rahman, Murat Kocaoglu
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Xiaoyu Liu, Jiaxin Yuan, Bang An, Yuancheng Xu, Yifan Yang, Furong Huang
Weighted Risk Invariance for Density-Aware Domain Generalization
Gina Wong, Joshua Gleason, Rama Chellappa, Yoav Wald, Anqi Liu
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman
Adversarial Data Augmentations for Out-of-Distribution Generalization
Simon Zhang, Ryan Peter DeMilt, Kun Jin, Cathy Honghui Xia
Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar
Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness
Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline Reinforcement Learning
Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Youfang Lin, Shou cheng Song, Han Wang
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Kumar Ravikumar
Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection
Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent
Neuro-Causal Factor Analysis
Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
Deep Neural Networks Extrapolate Cautiously (Most of the Time)
Katie Kang, Amrith Setlur, Claire Tomlin, Sergey Levine
Approximate Causal Effect Identification under Weak Confounding
Ziwei Jiang, Lai Wei, Murat Kocaoglu
Large Dimensional Change Point Detection with FWER Control as Automatic Stopping
Jiacheng Zou, Yang Fan, Markus Pelger
Robust Learning with Progressive Data Expansion Against Spurious Correlation
Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu
Towards Understanding Feature Learning in Out-of-Distribution Generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, James Cheng
Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie, Reihaneh Zohrabi, Mahdi Amiri, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
Uncertainty-Guided Online Test-Time Adaptation via Meta-Learning
Kyubyung Chae, Taesup Ki
Stabilizing GNN for Fairness via Lipschitz Bounds
Yaning Jia, Chunhui Zhan
SAFE: Stable Feature Extraction without Environment Labels
Aayush Mishra, Anqi Liu
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato
Contextual Vision Transformers for Robust Representation Learning
Yujia Bao, Theofanis Karaletsos
Learning Counterfactually Invariant Predictors
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus
Concept Algebra for Score-based Conditional Model
Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch
Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models
Shantanu Ghosh, Ke Yu, Forough Arabshahi, kayhan Batmanghelich
Invariant Causal Set Covering Machines
Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin
Replicable Reinforcement Learning
ERIC EATON, Marcel Hussing, Michael Kearns, Jessica Sorrell
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
Elan Rosenfeld, Saurabh Garg
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Wenhao Ding, Laixi Shi, Yuejie Chi, Ding Zhao
Shortcut Detection with Variational Autoencoders
Nicolas Michael Müller, Simon Roschmann, Shahbaz Farooque Khan, Philip Sperl, Konstantin Böttinger
Results on Counterfactual Invariance
Jake Fawkes, Robin J. Evans
The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-language Models
Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick haffner, Rong Ge, Zaiwei Zhang
Bridging the Domain Gap by Clustering-based Image-Text Graph Matching
Nokyung Park, Daewon Chae, Jeong Yong Shim, Sangpil Kim, Eun-Sol Kim, Jinkyu Kim
Group Robustness via Adaptive Class-Specific Scaling
Seonguk Seo, Bohyung Han
Improve Identity-Robustness for Face Models
Qi Qi, Shervin Ardeshir
Impact of Noise on Calibration and Generalisation of Neural Networks
Martin Ferianc, Ondrej Bohdal, Timothy Hospedales, Miguel R. D. Rodrigues
Bias-to-Text: Debiasing Unknown Visual Biases by Language Interpretation
Younghyun Kim, Sangwoo Mo, Minkyu Kim, Kyungmin Lee, Jaeho Lee, Jinwoo Shin
Studying Generalization on Memory-Based Methods in Continual Learning
Felipe del Rio, Julio Hurtado, Cristian Buc Calderon, Alvaro Soto, Vincenzo Lomonaco
Causal Dynamics Learning with Quantized Local Independence Discovery
Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee
Shortcut Learning Through the Lens of Training Dynamics
Nihal Murali, Aahlad Manas Puli, Ke Yu, Rajesh Ranganath, kayhan Batmanghelich
Optimization or Architecture: What Matters in Non-Linear Filtering?
Ido Greenberg, Netanel Yannay, Shie Mannor
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Junhyun Nam, Sangwoo Mo, Jaeho Lee, Jinwoo Shin
Where Does My Model Underperform?: A Human Evaluation of Slice Discovery Algorithms
Nari Johnson, Angel Cabrera, Gregory Plumb, Ameet Talwalkar
Antibody DomainBed: Towards robust predictions using invariant representations of biological sequences carrying complex distribution shifts
Natasa Tagasovska, Ji Won Park, Stephen Ra, Kyunghyun Cho
Provable domain adaptation using privileged information
Adam Breitholtz, Anton Matsson, Fredrik D. Johansson
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Ratsch, Guy Tennenholtz
ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry