6 papers
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…
Distance-based Learning of Hypertrees
Shaun Fallat, Kamyar Khodamoradi, David Kirkpatrick +3
We study the problem of learning hypergraphs with shortest-path queries (SP-queries), and present the first provably optimal online algorithm for a broad and natural class of hyper…
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…
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…
Common Benchmarks Undervalue the Generalization Power of Programmatic Policies
Amirhossein Rajabpour, Kiarash Aghakasiri, Sandra Zilles +1
Algorithms for learning programmatic representations for sequential decision-making problems are often evaluated on out-of-distribution (OOD) problems, with the common conclusion t…