1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Fine-tuning Vision Classifiers On A Budget
Sunil Kumar, Ted Sandler, Paulina Varshavskaya
Fine-tuning modern computer vision models requires accurately labeled data for which the ground truth may not exist, but a set of multiple labels can be obtained from labelers of v…
cs.LG2022★ 1 cited
Boosted Off-Policy Learning
Ben London, Levi Lu, Ted Sandler +1
We propose the first boosting algorithm for off-policy learning from logged bandit feedback. Unlike existing boosting methods for supervised learning, our algorithm directly optimi…
cs.LG2018★ 1 cited
Bayesian Counterfactual Risk Minimization
Ben London, Ted Sandler
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization b…