10 citations · 40 across the 27 of their papers we have counts for
9 papers · 1 filter
Data value estimation on private gradients
Zijian Zhou, Xinyi Xu, Daniela Rus +1
For gradient-based machine learning (ML) methods commonly adopted in practice such as stochastic gradient descent, the de facto differential privacy (DP) technique is perturbing th…
ABNet: Attention BarrierNet for Safe and Scalable Robot Learning
Wei Xiao, Tsun-Hsuan Wang, Daniela Rus
Safe learning is central to AI-enabled robots where a single failure may lead to catastrophic results. Barrier-based method is one of the dominant approaches for safe robot learnin…
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Tim Seyde, Peter Werner, Wilko Schwarting +2
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks. The underlying coarse action s…
Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels
Zahra Babaiee, Peyman M. Kiasari, Daniela Rus +1
Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures, that surpass the performance of classical CNNs, by a considerable sc…
Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks
Sadhana Lolla, Iaroslav Elistratov, Alejandro Perez +3
The modern pervasiveness of large-scale deep neural networks (NNs) is driven by their extraordinary performance on complex problems but is also plagued by their sudden, unexpected,…
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
Wei Xiao, Tsun-Hsuan Wang, Chuang Gan +1
Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications.…