18 citations · 18 across the 9 of their papers we have counts for
13 papers
Query Feedback and Teaching for Stable and Certifiable Language Generation
Farnam Mansouri, Alireza F. Pour, Grigoris Velegkas +1
Language generation in the limit studies whether a learner observing an enumeration of an unknown target language can eventually generate only valid, previously unseen elements. We…
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…
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…
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…
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…
Preference-Based Batch and Sequential Teaching
Farnam Mansouri, Yuxin Chen, Ara Vartanian +2
Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest t…
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Rati Devidze, Farnam Mansouri, Luis Haug +2
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical a…
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Farnam Mansouri, Yuxin Chen, Ara Vartanian +2
Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest t…
ChOracle: A Unified Statistical Framework for Churn Prediction
Ali Khodadadi, Seyed Abbas Hosseini, Ehsan Pajouheshgar +2
User churn is an important issue in online services that threatens the health and profitability of services. Most of the previous works on churn prediction convert the problem into…