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10/27/2022
Learning Single-Index Models with Shallow Neural Networks
Single-index models are a class of functions given by an unknown univari...
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06/08/2022
On Gradient Descent Convergence beyond the Edge of Stability
Gradient Descent (GD) is a powerful workhorse of modern machine learning...
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03/02/2022
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations
Machine learning methods have been shown to give accurate predictions in...
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10/07/2021
Cartoon Explanations of Image Classifiers
We present CartoonX (Cartoon Explanation), a novel model-agnostic explan...
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04/27/2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science...
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03/10/2021
Symmetry Breaking in Symmetric Tensor Decomposition
In this note, we consider the optimization problem associated with compu...
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06/27/2020
Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
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06/24/2020
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
We present Neural Splines, a technique for 3D surface reconstruction tha...
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05/29/2019
On the Expected Dynamics of Nonlinear TD Learning
While there are convergence guarantees for temporal difference (TD) lear...
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09/19/2018
Pommerman: A Multi-Agent Playground
We present Pommerman, a multi-agent environment based on the classic con...
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07/18/2018
Backplay: "Man muss immer umkehren"
A long-standing problem in model free reinforcement learning (RL) is tha...
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11/24/2016
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a...
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12/21/2013
