Results

Workshop		Forecasting labels under distribution-shift for machine-guided sequence design Lauren B Wheelock · Stephen Malina · Jeffrey Gerold · Sam Sinai
Workshop		Class-wise Domain Generalization: A Novel Framework for Evaluating Distributional Shift Sarath Sivaprasad · Akshay Goindani · Mario Fritz · Vineet Gandhi
Poster	Tue 14:00	Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition Yifan Zhang · Bryan Hooi · Lanqing Hong · Jiashi Feng
Poster	Thu 14:00	Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees Avinandan Bose · Arunesh Sinha · Tien Mai
Workshop		Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack Raksha Ramakrishna · György Dán
Workshop		Tailored Overlap for Learning Under Distribution Shift David Bruns-Smith · Alexander D'Amour · Avi Feller · Steve Yadlowsky
Workshop		Engineering Uncertainty Representations to Monitor Distribution Shifts Thomas Bonnier · Benjamin Bosch
Poster	Tue 14:00	Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift Christina Baek · Yiding Jiang · Aditi Raghunathan · J. Zico Kolter
Poster	Tue 14:00	Unsupervised Causal Generative Understanding of Images Titas Anciukevicius · Patrick Fox-Roberts · Edward Rosten · Paul Henderson
Poster	Thu 14:00	Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization Yibo Zeng · Henry Lam
Poster		RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection Yue Song · Nicu Sebe · Wei Wang