237 citations · 470 across the 14 of their papers we have counts for
6 papers · 1 filter
RLSbench: Domain Adaptation Under Relaxed Label Shift
Saurabh Garg, Nick Erickson, James Sharpnack +3
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.…
Time-Varying Propensity Score to Bridge the Gap between the Past and Present
Rasool Fakoor, Jonas Mueller, Zachary C. Lipton +2
Real-world deployment of machine learning models is challenging because data evolves over time. While no model can work when data evolves in an arbitrary fashion, if there is some…
Faster Deep Reinforcement Learning with Slower Online Network
Kavosh Asadi, Rasool Fakoor, Omer Gottesman +3
Deep reinforcement learning algorithms often use two networks for value function optimization: an online network, and a target network that tracks the online network with some dela…
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
Xingjian Shi, Jonas Mueller, Nick Erickson +2
We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well. Here we assembl…
Mixture Proportion Estimation and PU Learning: A Modern Approach
Saurabh Garg, Yifan Wu, Alex Smola +2
Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifie…
Dive into Deep Learning
Aston Zhang, Zachary C. Lipton, Mu Li +1
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter not…