154 citations · 861 across the 94 of their papers we have counts for
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Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh +3
Learning how to effectively control unknown dynamical systems is crucial for intelligent autonomous systems. This task becomes a significant challenge when the underlying dynamics…
Post-hoc Models for Performance Estimation of Machine Learning Inference
Xuechen Zhang, Samet Oymak, Jiasi Chen
Estimating how well a machine learning model performs during inference is critical in a variety of scenarios (for example, to quantify uncertainty, or to choose from a library of a…
Certainty Equivalent Quadratic Control for Markov Jump Systems
Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh +3
Real-world control applications often involve complex dynamics subject to abrupt changes or variations. Markov jump linear systems (MJS) provide a rich framework for modeling such…
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul +2
Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. However, most of these…
Generalization Guarantees for Neural Architecture Search with Train-Validation Split
Samet Oymak, Mingchen Li, Mahdi Soltanolkotabi
Neural Architecture Search (NAS) is a popular method for automatically designing optimized architectures for high-performance deep learning. In this approach, it is common to use b…
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Ganesh Ramachandra Kini, Orestis Paraskevas, Samet Oymak +1
The goal in label-imbalanced and group-sensitive classification is to optimize relevant metrics such as balanced error and equal opportunity. Classical methods, such as weighted cr…