45 citations · 202 across the 24 of their papers we have counts for
30 papers
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.…
Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey
Keerthi Vasan G. C., Stephen Sheng, Tucker Jones +2
Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test…
An Unsupervised Hunt for Gravitational Lenses
Stephen Sheng, Keerthi Vasan G. C, Chi Po Choi +2
Strong gravitational lenses allow us to peer into the farthest reaches of space by bending the light from a background object around a massive object in the foreground. Unfortunate…
Exponential Family Trend Filtering on Lattices
Veeranjaneyulu Sadhanala, Robert Bassett, James Sharpnack +1
Trend filtering is a modern approach to nonparametric regression that is more adaptive to local smoothness than splines or similar basis procedures. Existing analyses of trend filt…
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
Qin Ding, Yue Kang, Yi-Wei Liu +3
The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online adverti…
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Stochastic linear contextual bandit algorithms have substantial applications in practice, such as recommender systems, online advertising, clinical trials, etc. Recent works show t…