Rus, Daniela

85 publications

ICML 2025 ABNet: Adaptive Explicit-Barrier Net for Safe and Scalable Robot Learning Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Daniela Rus

ICLRW 2025 Active Human Feedback Collection via Neural Contextual Dueling Bandits Arun Verma, Xiaoqiang Lin, Zhongxiang Dai, Daniela Rus, Bryan Kian Hsiang Low

NeurIPS 2025 Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples Shiva Sreeram, Alaa Maalouf, Pratyusha Sharma, Daniela Rus

CoRL 2025 Improving Efficiency of Sampling-Based Motion Planning via Message-Passing Monte Carlo Makram Chahine, T. Konstantin Rusch, Zach J Patterson, Daniela Rus

ICLRW 2025 Improving Efficiency of Sampling-Based Motion Planning via Message-Passing Monte Carlo Makram Chahine, T. Konstantin Rusch, Zach J Patterson, Daniela Rus

ICLRW 2025 Low Stein Discrepancy via Message-Passing Monte Carlo Nathan Kirk, T. Konstantin Rusch, Jakob Zech, Daniela Rus

ICLR 2025 Oscillatory State-Space Models T. Konstantin Rusch, Daniela Rus

ICLR 2025 ReGen: Generative Robot Simulation via Inverse Design Phat Tan Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Erfan Aasi, Andrew Silva, Guy Rosman, Sertac Karaman, Daniela Rus

ICLR 2025 SafeDiffuser: Safe Planning with Diffusion Probabilistic Models Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Ramin Hasani, Mathias Lechner, Daniela Rus

AAAI 2025 The Master Key Filters Hypothesis: Deep Filters Are General Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu

NeurIPS 2025 The Quest for Universal Master Key Filters in DS-CNNs Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu

ICML 2025 Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu

NeurIPS 2024 DETAIL: Task DEmonsTration Attribution for Interpretable In-Context Learning Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low

ICMLW 2024 DETAIL: Task DEmonsTration Attribution for Interpretable In-Context Learning Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low

CoRL 2024 Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks Alex Quach, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus

ICMLW 2024 Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus

L4DC 2024 Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus

ICML 2024 LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan, Wojciech Matusik

NeurIPSW 2024 LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan, Wojciech Matusik

ICML 2024 Large Scale Dataset Distillation with Domain Shift Noel Loo, Alaa Maalouf, Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus

ICLR 2024 Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control Neehal Tumma, Mathias Lechner, Noel Loo, Ramin Hasani, Daniela Rus

ICMLW 2024 Message-Passing Monte Carlo: Generating Low-Discrepancy Point Sets via Graph Neural Networks T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus

WACV 2024 Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu

NeurIPSW 2024 The Master Key Filters Hypothesis: Deep Filters Are General Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu

ICLR 2024 Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation Noel Loo, Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus

ICLR 2024 Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu

NeurIPSW 2024 We Need Far Fewer Unique Filters than We Thought Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu

ICMLW 2023 Adversarial Training in Continuous-Time Models and Irregularly Sampled Time-Series Alvin Li, Mathias Lechner, Alexander Amini, Daniela Rus

ICML 2023 AutoCoreset: An Automatic Practical Coreset Construction Framework Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus

ICML 2023 Dataset Distillation with Convexified Implicit Gradients Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus

NeurIPS 2023 DiffuseBot: Breeding Soft Robots with Physics-Augmented Generative Diffusion Models Tsun-Hsuan Johnson Wang, Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim, Andrew Spielberg, Josh Tenenbaum, Chuang Gan, Daniela Rus

CoRL 2023 Dynamic Multi-Team Racing: Competitive Driving on 1/10-Th Scale Vehicles via Learning in Simulation Peter Werner, Tim Seyde, Paul Drews, Thomas Matrai Balch, Igor Gilitschenski, Wilko Schwarting, Guy Rosman, Sertac Karaman, Daniela Rus

NeurIPS 2023 Gigastep - One Billion Steps per Second Multi-Agent Reinforcement Learning Mathias Lechner, Lianhao Yin, Tim Seyde, Tsun-Hsuan Johnson Wang, Wei Xiao, Ramin Hasani, Joshua Rountree, Daniela Rus

L4DC 2023 Learning Stability Attention in Vision-Based End-to-End Driving Policies Tsun-Hsuan Wang, Wei Xiao, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus

NeurIPSW 2023 Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control Neehal Tumma, Mathias Lechner, Noel Loo, Ramin Hasani, Daniela Rus

ICLR 2023 Liquid Structural State-Space Models Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus

CoRL 2023 Measuring Interpretability of Neural Policies of Robots with Disentangled Representation Tsun-Hsuan Wang, Wei Xiao, Tim Seyde, Ramin Hasani, Daniela Rus

ICML 2023 On the Forward Invariance of Neural ODEs Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus

NeurIPS 2023 On the Size and Approximation Error of Distilled Datasets Alaa Maalouf, Murad Tukan, Noel Loo, Ramin Hasani, Mathias Lechner, Daniela Rus

ICML 2023 Provable Data Subset Selection for Efficient Neural Networks Training Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman

AAAI 2023 Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger, Daniela Rus

ICMLW 2023 Risk-Aware Image Generation by Estimating and Propagating Uncertainty Alejandro Perez, Iaroslav Elistratov, Fynn Schmitt-Ulms, Ege Demir, Sadhana Lolla, Elaheh Ahmadi, Daniela Rus, Alexander Amini

ICLR 2023 SoftZoo: A Soft Robot Co-Design Benchmark for Locomotion in Diverse Environments Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan

ICLR 2023 Solving Continuous Control via Q-Learning Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin Riedmiller, Daniela Rus, Markus Wulfmeier

NeurIPS 2022 ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment Joseph DelPreto, Chao Liu, Yiyue Luo, Michael Foshey, Yunzhu Li, Antonio Torralba, Wojciech Matusik, Daniela Rus

L4DC 2022 Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models Jose Luis Vazquez Espinoza, Alexander Liniger, Wilko Schwarting, Daniela Rus, Luc Van Gool

NeurIPS 2022 Efficient Dataset Distillation Using Random Feature Approximation Noel Loo, Ramin Hasani, Alexander Amini, Daniela Rus

NeurIPS 2022 Evolution of Neural Tangent Kernels Under Benign and Adversarial Training Noel Loo, Ramin Hasani, Alexander Amini, Daniela Rus

AAAI 2022 GoTube: Scalable Statistical Verification of Continuous-Depth Models Sophie A. Gruenbacher, Mathias Lechner, Ramin M. Hasani, Daniela Rus, Thomas A. Henzinger, Scott A. Smolka, Radu Grosu

L4DC 2022 Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

NeurIPSW 2022 PyHopper - A Plug-and-Play Hyperparameter Optimization Engine Mathias Lechner, Ramin Hasani, Sophie Neubauer, Philipp Neubauer, Daniela Rus

NeurIPS 2021 Causal Navigation by Continuous-Time Neural Networks Charles Vorbach, Ramin Hasani, Alexander Amini, Mathias Lechner, Daniela Rus

NeurIPS 2021 Compressing Neural Networks: Towards Determining the Optimal Layer-Wise Decomposition Lucas Liebenwein, Alaa Maalouf, Dan Feldman, Daniela Rus

ICLR 2021 Deep Learning Meets Projective Clustering Alaa Maalouf, Harry Lang, Daniela Rus, Dan Feldman

L4DC 2021 Feedback from Pixels: Output Regulation via Learning-Based Scene View Synthesis Murad Abu-Khalaf, Sertac Karaman, Daniela Rus

NeurIPS 2021 Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, Daniela Rus

CoRL 2021 Learning a Risk-Aware Trajectory Planner from Demonstrations Using Logic Monitor Xiao Li, Jonathan DeCastro, Cristian Ioan Vasile, Sertac Karaman, Daniela Rus

CoRL 2021 Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

AAAI 2021 Liquid Time-Constant Networks Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu

NeurIPSW 2021 Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

ICML 2021 On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu

NeurIPS 2021 Sparse Flows: Pruning Continuous-Depth Models Lucas Liebenwein, Ramin Hasani, Alexander Amini, Daniela Rus

CoRL 2021 Strength Through Diversity: Robust Behavior Learning via Mixture Policies Tim Seyde, Wilko Schwarting, Igor Gilitschenski, Markus Wulfmeier, Daniela Rus

NeurIPSW 2021 Strength Through Diversity: Robust Behavior Learning via Mixture Policies Tim Seyde, Wilko Schwarting, Igor Gilitschenski, Markus Wulfmeier, Daniela Rus

ICML 2021 The Logical Options Framework Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan Decastro, Micah Fry, Daniela Rus

ICML 2020 A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu

AAAI 2020 Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior Brandon Araki, Kiran Vodrahalli, Thomas Leech, Cristian Ioan Vasile, Mark Donahue, Daniela Rus

NeurIPS 2020 Deep Evidential Regression Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus

CoRL 2020 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

ICLR 2020 Deep Orientation Uncertainty Learning Based on a Bingham Loss Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus

CoRL 2020 Differentiable Logic Layer for Rule Guided Trajectory Prediction Xiao Li, Guy Rosman, Igor Gilitschenski, Jonathan DeCastro, Cristian-Ioan Vasile, Sertac Karaman, Daniela Rus

L4DC 2020 Learning to Plan via Deep Optimistic Value Exploration Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

ICML 2020 Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik

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

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

NeurIPS 2019 Learning-in-the-Loop Optimization: End-to-End Control and Co-Design of Soft Robots Through Learned Deep Latent Representations Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus

ICMLW 2019 Real-World Autonomous Vehicle Control Trained Entirely Within Data-Driven Simulation Alexander Amini, Igor Gilitschenski, Jacob Phillips, Julia Moseyko, Sertac Karaman, Daniela Rus

ICML 2017 Coresets for Vector Summarization with Applications to Network Graphs Dan Feldman, Sedat Ozer, Daniela Rus

NeurIPS 2016 Dimensionality Reduction of Massive Sparse Datasets Using Coresets Dan Feldman, Mikhail Volkov, Daniela Rus

CVPR 2016 Information-Driven Adaptive Structured-Light Scanners Guy Rosman, Daniela Rus, John W. Fisher Iii

NeurIPS 2014 Coresets for K-Segmentation of Streaming Data Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher Iii, Daniela Rus

AAAI 2012 Parsing Outdoor Scenes from Streamed 3D Laser Data Using Online Clustering and Incremental Belief Updates Rudolph Triebel, Rohan Paul, Daniela Rus, Paul M. Newman

IJCAI 2003 Self-Reconfiguring Robots: Successes and Challenges Daniela Rus

AAAI 1997 The Dartmouth Mobile Robot: SK William Garner, Gregory Friedland, Artyom Lifshits, Daniela Rus, Keith Kotay, Jon Howell

IJCAI 1997 The Self-Organizing Desk Daniela Rus, Peter de Santis