Compositional and Contract-Based Verification for Autonomous Driving on Road Networks. - Lucas Liebenwein; Wilko Schwarting; Cristian Ioan Vasile; Jonathan A. DeCastro; Javier Alonso-Mora; Sertac Karaman; Daniela Rus (ISRR, 2017)

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

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)

Counterexample-Guided Safety Contracts for Autonomous Driving. - Jonathan A. DeCastro; Lucas Liebenwein; Cristian Ioan Vasile; Russ Tedrake; Sertac Karaman; Daniela Rus (WAFR, 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)

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

Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts. - Björn Lütjens; Lucas Liebenwein; Katharina Kramer (CoRR, 2019)

Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space. - Wilko Schwarting; Tim Seyde; Igor Gilitschenski; Lucas Liebenwein; Ryan Sander; Sertac Karaman; Daniela Rus (CoRL, 2020)

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)

Sparse Flows: Pruning Continuous-depth Models. - Lucas Liebenwein; Ramin M. Hasani; Alexander Amini; Daniela Rus (NeurIPS, 2021)

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. - Lucas Liebenwein; Alaa Maalouf; Dan Feldman; Daniela Rus (NeurIPS, 2021)

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

Closed-form Continuous-Depth Models. - Ramin M. Hasani; Mathias Lechner; Alexander Amini; Lucas Liebenwein; Max Tschaikowski; Gerald Teschl; Daniela Rus (CoRR, 2021)

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. - Lucas Liebenwein; Alaa Maalouf; Oren Gal; Dan Feldman; Daniela Rus (CoRR, 2021)

Efficient Deep Learning: From Theory to Practice. - Lucas Liebenwein (2021)

End-to-End Sensitivity-Based Filter Pruning. - Zahra Babaiee; Lucas Liebenwein; Ramin M. Hasani; Daniela Rus; Radu Grosu (CoRR, 2022)

Pruning by Active Attention Manipulation. - Zahra Babaiee; Lucas Liebenwein; Ramin M. Hasani; Daniela Rus; Radu Grosu (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)

Closed-form continuous-time neural networks. - Ramin M. Hasani; Mathias Lechner; Alexander Amini; Lucas Liebenwein; Aaron Ray; Max Tschaikowski; Gerald Teschl; Daniela Rus (Nat. Mac. Intell., 2022)

Publisher Correction: Closed-form continuous-time neural networks. - Ramin M. Hasani; Mathias Lechner; Alexander Amini; Lucas Liebenwein; Aaron Ray; Max Tschaikowski; Gerald Teschl; Daniela Rus (Nat. Mac. Intell., 2022)