Participatory route planning. - David Wilkie; Cenk Baykal; Ming C. Lin (SIGSPATIAL/GIS, 2014)

Interactive-rate motion planning for concentric tube robots. - Luis G. Torres; Cenk Baykal; Ron Alterovitz (ICRA, 2014)

Optimizing design parameters for sets of concentric tube robots using sampling-based motion planning. - Cenk Baykal; Luis G. Torres; Ron Alterovitz (IROS, 2015)

Persistent Surveillance of Events with Unknown Rate Statistics. - Cenk Baykal; Guy Rosman; Kyle Kotowick; Mark Donahue; Daniela Rus (WAFR, 2016)

Persistent surveillance of events with unknown, time-varying statistics. - Cenk Baykal; Guy Rosman; Sebastian Claici; Daniela Rus (ICRA, 2017)

Asymptotically Optimal Design of Piecewise Cylindrical Robots using Motion Planning. - Cenk Baykal; Ron Alterovitz (Robotics: Science and Systems, 2017)

Detection of AQM on Paths using Machine Learning Methods. - Cenk Baykal; Wilko Schwarting; Alex Wallar (CoRR, 2017)

Training Support Vector Machines using Coresets. - Cenk Baykal; Lucas Liebenwein; Wilko Schwarting (CoRR, 2017)

Kinematic Design Optimization of a Parallel Surgical Robot to Maximize Anatomical Visibility via Motion Planning. - Alan Kuntz; Chris Bowen; Cenk Baykal; Arthur W. Mahoney; Patrick L. Anderson; Fabien Maldonado; Robert J. Webster III; Ron Alterovitz (ICRA, 2018)

Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees. - Lucas Liebenwein; Cenk Baykal; Igor Gilitschenski; Sertac Karaman; Daniela Rus (Robotics: Science and Systems, 2018)

Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. - Cenk Baykal; Lucas Liebenwein; Igor Gilitschenski; Dan Feldman; Daniela Rus (ICLR (Poster), 2019)

Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRank. - Harry Lang; Cenk Baykal; Najib Abu Samra; Tony Tannous; Dan Feldman; Daniela Rus (TAMC, 2019)

Asymptotically optimal kinematic design of robots using motion planning. - Cenk Baykal; Chris Bowen; Ron Alterovitz (Auton. Robots, 2019)

SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks. - Cenk Baykal; Lucas Liebenwein; Igor Gilitschenski; Dan Feldman; Daniela Rus (CoRR, 2019)

Resilient Multi-Agent Consensus Using Wi-Fi Signals. - Stephanie Gil; Cenk Baykal; Daniela Rus (IEEE Control. Syst. Lett., 2019)

Provable Filter Pruning for Efficient Neural Networks. - Lucas Liebenwein; Cenk Baykal; Harry Lang; Dan Feldman; Daniela Rus (ICLR, 2020)

Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. - Lucas Liebenwein; Cenk Baykal; Brandon Carter 001; David Gifford 001; Daniela Rus (MLSys, 2021)

Low-Regret Active learning. - Cenk Baykal; Lucas Liebenwein; Dan Feldman; Daniela Rus (CoRR, 2021)

Graph Belief Propagation Networks. - Junteng Jia; Cenk Baykal; Vamsi K. Potluru; Austin R. Benson (CoRR, 2021)

On coresets for support vector machines. - Murad Tukan; Cenk Baykal; Dan Feldman; Daniela Rus (Theor. Comput. Sci., 2021)

Sampling-based Algorithms for Fast and Deployable AI. - Cenk Baykal (2021)

A Theoretical View on Sparsely Activated Networks. - Cenk Baykal; Nishanth Dikkala; Rina Panigrahy; Cyrus Rashtchian; Xin Wang 016 (NeurIPS, 2022)

Weighted Distillation with Unlabeled Examples. - Fotis Iliopoulos; Vasilis Kontonis; Cenk Baykal; Gaurav Menghani; Khoa Trinh; Erik Vee (NeurIPS, 2022)

Bandit Sampling for Multiplex Networks. - Cenk Baykal; Vamsi K. Potluru; Sameena Shah; Manuela M. Veloso (CoRR, 2022)

Sensitivity-Informed Provable Pruning of Neural Networks. - Cenk Baykal; Lucas Liebenwein; Igor Gilitschenski; Dan Feldman; Daniela Rus (SIAM J. Math. Data Sci., 2022)

Robust Active Distillation. - Cenk Baykal; Khoa Trinh; Fotis Iliopoulos; Gaurav Menghani; Erik Vee (ICLR, 2023)

Alternating Updates for Efficient Transformers. - Cenk Baykal; Dylan J. Cutler; Nishanth Dikkala; Nikhil Ghosh; Rina Panigrahy; Xin Wang 116 (NeurIPS, 2023)

SLaM: Student-Label Mixing for Distillation with Unlabeled Examples. - Vasilis Kontonis; Fotis Iliopoulos; Khoa Trinh; Cenk Baykal; Gaurav Menghani; Erik Vee (NeurIPS, 2023)

Alternating Updates for Efficient Transformers. - Cenk Baykal; Dylan J. Cutler; Nishanth Dikkala; Nikhil Ghosh; Rina Panigrahy; Xin Wang 016 (CoRR, 2023)

The Power of External Memory in Increasing Predictive Model Capacity. - Cenk Baykal; Dylan J. Cutler; Nishanth Dikkala; Nikhil Ghosh; Rina Panigrahy; Xin Wang 016 (CoRR, 2023)

SLaM: Student-Label Mixing for Semi-Supervised Knowledge Distillation. - Vasilis Kontonis; Fotis Iliopoulos; Khoa Trinh; Cenk Baykal; Gaurav Menghani; Erik Vee (CoRR, 2023)