18 citations · 18 across the 7 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Formal Models of Active Learning from Contrastive Examples
Farnam Mansouri, Hans U. Simon, Adish Singla +2
Machine learning can greatly benefit from providing learning algorithms with pairs of contrastive training examples -- typically pairs of instances that differ only slightly, yet h…
A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimension
Farnam Mansouri, Sandra Zilles
This paper presents a construction of a proper and stable labelled sample compression scheme of size $O(\VCD^2)$ for any finite concept class, where $\VCD$ denotes the Vapnik-Cherv…