22 citations · 22 across the 3 of their papers we have counts for
5 papers · 1 filter
Online Learning with Improving Agents: Multiclass, Budgeted Agents and Bandit Learners
Sajad Ashkezari, Shai Ben-David
We investigate the recently introduced model of learning with improvements, where agents are allowed to make small changes to their feature values to be warranted a more desirable…
Active learning from positive and unlabeled examples
Farnam Mansouri, Sandra Zilles, Shai Ben-David
Learning from positive and unlabeled data (PU learning) is a weakly supervised variant of binary classification in which the learner receives labels only for (some) positively labe…
A Novel Data-Dependent Learning Paradigm for Large Hypothesis Classes
Alireza F. Pour, Shai Ben-David
We address the general task of learning with a set of candidate models that is too large to have a uniform convergence of empirical estimates to true losses. While the common appro…
Learning from positive and unlabeled examples -Finite size sample bounds
Farnam Mansouri, Shai Ben-David
PU (Positive Unlabeled) learning is a variant of supervised classification learning in which the only labels revealed to the learner are of positively labeled instances. PU learnin…
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy
To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around potential trade-offs is often informal and mi…