60 citations · 290 across the 33 of their papers we have counts for
35 papers
An Analysis of Self-supervised Pre-training with Dependent Samples
Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe
Self-supervised learning relies on so-called data augmentations of unlabeled datapoints --- for example, masking random pixels in an image --- that should leave the…
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees
Joe Suk, Samory Kpotufe
We study outlier (a.k.a., anomaly) detection for single-pass non-stationary streaming data. In the well-studied offline or batch outlier detection problem, traditional methods such…
Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
Mohammadreza M. Kalan, Yuyang Deng, Eitan J. Neugut +1
We consider the problem of transfer learning in Neyman-Pearson classification, where the objective is to minimize the error w.r.t. a distribution , subject to the constraint t…
Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning
Yuyang Deng, Samory Kpotufe
Theoretical works on supervised transfer learning (STL) -- where the learner has access to labeled samples from both source and target distributions -- have for the most part focus…
Distributionally-Constrained Adversaries in Online Learning
Moïse Blanchard, Samory Kpotufe
There has been much recent interest in understanding the continuum from adversarial to stochastic settings in online learning, with various frameworks including smoothed settings p…