223 citations · 389 across the 45 of their papers we have counts for
8 papers · 2 filters
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
Neehal Tumma, Mathias Lechner, Noel Loo +2
Developing autonomous agents that can interact with changing environments is an open challenge in machine learning. Robustness is particularly important in these settings as agents…
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.…
On the Size and Approximation Error of Distilled Sets
Alaa Maalouf, Murad Tukan, Noel Loo +3
Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset. Despite s…
AutoCoreset: An Automatic Practical Coreset Construction Framework
Alaa Maalouf, Murad Tukan, Vladimir Braverman +1
A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries. Coresets became prevalent in machine learni…
Provable Data Subset Selection For Efficient Neural Network Training
Murad Tukan, Samson Zhou, Alaa Maalouf +3
Radial basis function neural networks (\emph{RBFNN}) are {well-known} for their capability to approximate any continuous function on a closed bounded set with arbitrary precision g…
Dataset Distillation with Convexified Implicit Gradients
Noel Loo, Ramin Hasani, Mathias Lechner +1
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this…
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Noel Loo, Ramin Hasani, Mathias Lechner +2
Modern deep learning requires large volumes of data, which could contain sensitive or private information that cannot be leaked. Recent work has shown for homogeneous neural networ…