Lucas Liebenwein

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Daniela RusAndrew (1956) and Erna Viterbi Professor of Computer Science, MITVerified email at csail.mit.edu
Cenk BaykalPh.D. Student, Massachusetts Institute of TechnologyVerified email at mit.edu
Dan FeldmanUniversity of HaifaVerified email at csail.mit.edu
Igor GilitschenskiAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Sertac KaramanAssociate Professor of Aeronautics and Astronautics, Massachusetts Institute of TechnologyVerified email at mit.edu
Wilko SchwartingMassachusetts Institute of TechnologyVerified email at mit.edu
Cristian-Ioan VasileLehigh UniversityVerified email at lehigh.edu
Jonathan DeCastroToyota Research InstituteVerified email at cornell.edu
Ramin HasaniMassachusetts Institute of TechnologyVerified email at mit.edu
Alexander AminiComputer Science, Massachusetts Institute of TechnologyVerified email at mit.edu
Russ TedrakeMIT (EECS, MechE, Aero/Astro) and Toyota Research InstituteVerified email at mit.edu
Brandon CarterMIT CSAILVerified email at csail.mit.edu
Ryan SanderMIT CSAILVerified email at mit.edu
Tim SeydeMITVerified email at mit.edu
Javier Alonso-MoraAssociate Professor, Delft University of TechnologyVerified email at tudelft.nl
Björn LütjensPhD Candidate, Massachusetts Institute of TechnologyVerified email at mit.edu
Mathias LechnerIST AustriaVerified email at ist.ac.at
Max TschaikowskiAssociate Professor, Aalborg UniversityVerified email at cs.aau.dk
Alaa MaaloufComputer Science Department, University of HaifaVerified email at campus.haifa.ac.il
Radu GrosuProfessor of Computer ScienceVerified email at tuwien.ac.at

Lucas Liebenwein

Massachusetts Institute of Technology

Verified email at mit.edu - Homepage

Machine Learning Neural Networks Deep Learning Artificial Intelligence Robotics


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Provable Filter Pruning for Efficient Neural Networks L Liebenwein, C Baykal, H Lang, D Feldman, D Rus International Conference on Learning Representations, 2020	64	2020
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds C Baykal, L Liebenwein, I Gilitschenski, D Feldman, D Rus International Conference on Learning Representations, 2019	57	2019
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy L Liebenwein, C Baykal, B Carter, D Gifford, D Rus Proceedings of Machine Learning and Systems (MLSys 2021), 2021	19	2021
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space W Schwarting, T Seyde, I Gilitschenski*, L Liebenwein, R Sander, ... Conference on Robot Learning (CoRL), 2020	17	2020
Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees L Liebenwein, C Baykal, I Gilitschenski, S Karaman, D Rus Robotics: Science and Systems XIV (RSS), 2018	17	2018
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks C Baykal, L Liebenwein, I Gilitschenski, D Feldman, D Rus arXiv preprint arXiv:1910.05422, 2019	14	2019
Compositional and Contract-based Verification for Autonomous Driving on Road Networks L Liebenwein, W Schwarting, CI Vasile, J DeCastro, J Alonso-Mora, ... Robotics Research: The 18th International Symposium ISRR, 2018	13	2018
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts B Lütjens, L Liebenwein, K Kramer arXiv preprint arXiv:1912.07850, 2019	12	2019
Counterexample-guided safety contracts for autonomous driving J DeCastro, L Liebenwein, CI Vasile, R Tedrake, S Karaman, D Rus International Workshop on the Algorithmic Foundations of Robotics, 2018	9	2018
Closed-form Continuous-Depth Models R Hasani, M Lechner, A Amini, L Liebenwein, M Tschaikowski, G Teschl, ... arXiv preprint arXiv:2106.13898, 2021	7	2021
Sparse flows: Pruning continuous-depth models L Liebenwein, R Hasani, A Amini, D Rus Advances in Neural Information Processing Systems 34, 2021	5	2021
Training Support Vector Machines using Coresets C Baykal, L Liebenwein, W Schwarting arXiv preprint arXiv:1708.03835, 2017	5	2017
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition L Liebenwein, A Maalouf, D Feldman, D Rus Advances in Neural Information Processing Systems 34, 2021	3	2021
Sensitivity-Informed Provable Pruning of Neural Networks C Baykal, L Liebenwein, I Gilitschenski, D Feldman, D Rus SIAM Journal on Mathematics of Data Science 4 (1), 26-45, 2022	2	2022
Low-Regret Active learning C Baykal, L Liebenwein, D Feldman, D Rus arXiv preprint arXiv:2104.02822, 2021	1	2021
End-to-End Sensitivity-Based Filter Pruning Z Babaiee, L Liebenwein, R Hasani, D Rus, R Grosu arXiv preprint arXiv:2204.07412, 2022		2022
Efficient Deep Learning: From Theory to Practice L Liebenwein Massachusetts Institute of Technology, 2021		2021
SYSTEM AND METHOD OF VALIDATION OF OPERATIONAL REGULATIONS TO AUTONOMOUSLY OPERATE A VEHICLE DURING TRAVEL J Decastro, L Liebenwein, C Vasile, RL Tedrake, S Karaman, D Rus US Patent App. 16/539,772, 2020		2020
Contract-based safety verification for autonomous driving L Liebenwein Massachusetts Institute of Technology, 2018		2018
Supplementary Material:“Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition” L Liebenwein, A Maalouf, D Feldman, D Rus

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