Lucas Liebenwein

Publication Activity (10 Years)

Years Active: 2017-2022
Publications (10 Years): 25

Top Topics

Generalization Bounds

Autonomous Driving

Pruning Methods

Top Venues

Nat. Mac. Intell.

SIAM J. Math. Data Sci.

Publications

Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus
Publisher Correction: Closed-form continuous-time neural networks. Nat. Mac. Intell. 4 (12) (2022)
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
Sensitivity-Informed Provable Pruning of Neural Networks. SIAM J. Math. Data Sci. 4 (1) (2022)
Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu
Pruning by Active Attention Manipulation. CoRR (2022)
Zahra Babaiee, Lucas Liebenwein, Ramin M. Hasani, Daniela Rus, Radu Grosu
End-to-End Sensitivity-Based Filter Pruning. CoRR (2022)
Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus
Closed-form continuous-time neural networks. Nat. Mac. Intell. 4 (11) (2022)
Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus
Sparse Flows: Pruning Continuous-depth Models. CoRR (2021)
Cenk Baykal, Lucas Liebenwein, Dan Feldman, Daniela Rus
Low-Regret Active learning. CoRR (2021)
Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. MLSys (2021)
Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy. CoRR (2021)
Ramin M. Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Max Tschaikowski, Gerald Teschl, Daniela Rus
Closed-form Continuous-Depth Models. CoRR (2021)
Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. NeurIPS (2021)
Lucas Liebenwein, Alaa Maalouf, Oren Gal, Dan Feldman, Daniela Rus
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition. CoRR (2021)
Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space. CoRR (2021)
Lucas Liebenwein, Ramin M. Hasani, Alexander Amini, Daniela Rus
Sparse Flows: Pruning Continuous-depth Models. NeurIPS (2021)
Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space. CoRL (2020)
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus
Provable Filter Pruning for Efficient Neural Networks. ICLR (2020)
Lucas Liebenwein, Cenk Baykal, Harry Lang, Dan Feldman, Daniela Rus
Provable Filter Pruning for Efficient Neural Networks. CoRR (2019)
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. ICLR (Poster) (2019)
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks. CoRR (2019)
Björn Lütjens, Lucas Liebenwein, Katharina Kramer
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts. CoRR (2019)
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds. CoRR (2018)
Lucas Liebenwein, Cenk Baykal, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees. Robotics: Science and Systems (2018)
Jonathan A. DeCastro, Lucas Liebenwein, Cristian Ioan Vasile, Russ Tedrake, Sertac Karaman, Daniela Rus
Counterexample-Guided Safety Contracts for Autonomous Driving. WAFR (2018)
Cenk Baykal, Lucas Liebenwein, Wilko Schwarting
Training Support Vector Machines using Coresets. CoRR (2017)
Lucas Liebenwein, Wilko Schwarting, Cristian Ioan Vasile, Jonathan A. DeCastro, Javier Alonso-Mora, Sertac Karaman, Daniela Rus
Compositional and Contract-Based Verification for Autonomous Driving on Road Networks. ISRR (2017)