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Poster
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Tue 9:00
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Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
Tim Pearce · Jong-Hyeon Jeong · yichen jia · Jun Zhu
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Poster
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Tue 9:00
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Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free
Alexander Meinke · Julian Bitterwolf · Matthias Hein
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Poster
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Tue 9:00
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Learning (Very) Simple Generative Models Is Hard
Sitan Chen · Jerry Li · Yuanzhi Li
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Poster
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Thu 14:00
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Semantic uncertainty intervals for disentangled latent spaces
Swami Sankaranarayanan · Anastasios Angelopoulos · Stephen Bates · Yaniv Romano · Phillip Isola
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Poster
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Wed 9:00
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Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Kien Do · Thai Hung Le · Dung Nguyen · Dang Nguyen · HARIPRIYA HARIKUMAR · Truyen Tran · Santu Rana · Svetha Venkatesh
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Workshop
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Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance
Aengus Lynch · Jean Kaddour · Ricardo Silva
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Workshop
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Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance
Aengus Lynch · Jean Kaddour · Ricardo Silva
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Poster
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Thu 9:00
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Invariance Learning based on Label Hierarchy
Shoji Toyota · Kenji Fukumizu
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Poster
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Tue 14:00
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Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator
Lior Danon · Dan Garber
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Poster
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Distributed Learning of Finite Gaussian Mixtures
Qiong Zhang · Jiahua Chen
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Poster
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Thu 9:00
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Acceleration in Distributed Sparse Regression
Marie Maros · Gesualdo Scutari
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Poster
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Thu 14:00
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DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery
Marie Maros · Gesualdo Scutari
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