2 code implementations • 11 Apr 2024 • Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Chase Lipton
This phenomenon is especially prominent in high-noise settings.
1 code implementation • NeurIPS 2021 • Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton
Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.
1 code implementation • 8 Feb 2014 • Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy
As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive.
no code implementations • 27 Apr 2019 • Mohammad Taha Bahadori, Zachary Chase Lipton
We postulate that fine temporal detail, e. g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data.
no code implementations • 21 Sep 2022 • Audrey Huang, Liu Leqi, Zachary Chase Lipton, Kamyar Azizzadenesheli
To mitigate these problems, we incorporate model-based estimation to develop the first doubly robust (DR) estimator for the CDF of returns in MDPs.
no code implementations • NeurIPS 2023 • Saurabh Garg, Amrith Setlur, Zachary Chase Lipton, Sivaraman Balakrishnan, Virginia Smith, aditi raghunathan
Self-training and contrastive learning have emerged as leading techniques for incorporating unlabeled data, both under distribution shift (unsupervised domain adaptation) and when it is absent (semi-supervised learning).