1 citations · 2 across the 5 of their papers we have counts for
7 papers
Constrained Nonconvex Stochastic Optimization with One Projection
Yuyang Deng, Mohammadreza M. Kalan, Eitan J. Neugut +1
Constrained nonconvex optimization has seen increasing application in modern machine learning, such as safe LLM alignment/finetuning, transfer learning and rank-constrained continu…
Classification with Abstention Under Class-Conditional Error Constraints
Mohammadreza M. Kalan, Yuyang Deng, Sanaz Hamidi
We study binary classification with abstention under separate class-conditional error constraints, with the objective of minimizing abstention while keeping both errors below presc…
Neyman-Pearson Classification under Both Null and Alternative Distributions Shift
Mohammadreza M. Kalan, Yuyang Deng, Eitan J. Neugut +1
We consider the problem of transfer learning in Neyman-Pearson classification, where the objective is to minimize the error w.r.t. a distribution , subject to the constraint t…
Concentration and excess risk bounds for imbalanced classification with synthetic oversampling
Touqeer Ahmad, Mohammadreza M. Kalan, François Portier +1
Synthetic oversampling of minority examples using SMOTE and its variants is a leading strategy for addressing imbalanced classification problems. Despite the success of this approa…
Transfer Neyman-Pearson Algorithm for Outlier Detection
Mohammadreza M. Kalan, Eitan J. Neugut, Samory Kpotufe
We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balance…
Distribution-Free Rates in Neyman-Pearson Classification
Mohammadreza M. Kalan, Samory Kpotufe
We consider the problem of Neyman-Pearson classification which models unbalanced classification settings where error w.r.t. a distribution is to be minimized subject to low e…
Tight Rates in Supervised Outlier Transfer Learning
Mohammadreza M. Kalan, Samory Kpotufe
A critical barrier to learning an accurate decision rule for outlier detection is the scarcity of outlier data. As such, practitioners often turn to the use of similar but imperfec…