7 papers
Surprises in Proper Positive-Only Learning
Shai Ben-David, Farnam Mansouri, Anay Mehrotra +1
Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive region of an unknown target concept, bu…
Learning with Multiple Correct Answers -- Regret Bounds under Different Feedback Models
Alireza F. Pour, Farnam Mansouri, Shai Ben-David
We study the problem of learning with multiple correct answers, where each instance admits a set of valid labels. We primarily focus on the online setup, where in each round the le…
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…
Learning Half-Spaces from Perturbed Contrastive Examples
Aryan Alavi Razavi Ravari, Farnam Mansouri, Yuxin Chen +3
We study learning under a two-step contrastive example oracle, as introduced by Mansouri et. al. (2025), where each queried (or sampled) labeled example is paired with an additiona…
The Computational Complexity of Almost Stable Clustering with Penalties
Kamyar Khodamoradi, Farnam Mansouri, Sandra Zilles
We investigate the complexity of stable (or perturbation-resilient) instances of and clustering problems in metrics with smal…
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…