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