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07/28/2023
On Single Index Models beyond Gaussian Data
Sparse high-dimensional functions have arisen as a rich framework to stu...
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07/04/2023
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
Graph neural networks (GNNs) have become increasingly popular for classi...
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05/31/2023
Conditionally Strongly Log-Concave Generative Models
There is a growing gap between the impressive results of deep image gene...
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05/26/2023
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
In recent years, domains such as natural language processing and image r...
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10/28/2022
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks
To understand the training dynamics of neural networks (NNs), prior stud...
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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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08/05/2022
Towards Antisymmetric Neural Ansatz Separation
We study separations between two fundamental models (or Ansätze) of anti...
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07/06/2022
On Non-Linear operators for Geometric Deep Learning
This work studies operators mapping vector and scalar fields defined ove...
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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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06/02/2022
Exponential Separations in Symmetric Neural Networks
In this work we demonstrate a novel separation between symmetric neural ...
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06/02/2022
When does return-conditioned supervised learning work for offline reinforcement learning?
Several recent works have proposed a class of algorithms for the offline...
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04/22/2022
On Feature Learning in Neural Networks with Global Convergence Guarantees
We study the optimization of wide neural networks (NNs) via gradient flo...
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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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02/16/2022
Extended Unconstrained Features Model for Exploring Deep Neural Collapse
The modern strategy for training deep neural networks for classification...
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02/14/2022
Simultaneous Transport Evolution for Minimax Equilibria on Measures
Min-max optimization problems arise in several key machine learning setu...
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12/07/2021
Lattice-Based Methods Surpass Sum-of-Squares in Clustering
Clustering is a fundamental primitive in unsupervised learning which giv...
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12/02/2021
Quantile Filtered Imitation Learning
We introduce quantile filtered imitation learning (QFIL), a novel policy...
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11/26/2021
Neural Fields as Learnable Kernels for 3D Reconstruction
We present Neural Kernel Fields: a novel method for reconstructing impli...
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11/25/2021
Multi-fidelity Stability for Graph Representation Learning
In the problem of structured prediction with graph representation learni...
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10/12/2021
A Rate-Distortion Framework for Explaining Black-box Model Decisions
We present the Rate-Distortion Explanation (RDE) framework, a mathematic...
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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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07/11/2021
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
Energy-based models (EBMs) are generative models that are usually traine...
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06/20/2021
On the Cryptographic Hardness of Learning Single Periodic Neurons
We show a simple reduction which demonstrates the cryptographic hardness...
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06/16/2021
Offline RL Without Off-Policy Evaluation
Most prior approaches to offline reinforcement learning (RL) have taken ...
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06/14/2021
On the Sample Complexity of Learning with Geometric Stability
Many supervised learning problems involve high-dimensional data such as ...
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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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04/15/2021
On Energy-Based Models with Overparametrized Shallow Neural Networks
Energy-based models (EBMs) are a simple yet powerful framework for gener...
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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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02/02/2021
Depth separation beyond radial functions
High-dimensional depth separation results for neural networks show that ...
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02/01/2021
Self-Supervised Equivariant Scene Synthesis from Video
We propose a self-supervised framework to learn scene representations fr...
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11/11/2020
Learned Equivariant Rendering without Transformation Supervision
We propose a self-supervised framework to learn scene representations fr...
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10/28/2020
On Graph Neural Networks versus Graph-Augmented MLPs
From the perspective of expressive power, this work compares multi-layer...
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09/21/2020
Kernel-Based Smoothness Analysis of Residual Networks
A major factor in the success of deep neural networks is the use of soph...
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08/21/2020
A Dynamical Central Limit Theorem for Shallow Neural Networks
Recent theoretical work has characterized the dynamics of wide shallow n...
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08/16/2020
A Functional Perspective on Learning Symmetric Functions with Neural Networks
Symmetric functions, which take as input an unordered, fixed-size set, a...
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07/28/2020
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Classical reduced models are low-rank approximations using a fixed basis...
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07/01/2020
In-Distribution Interpretability for Challenging Modalities
It is widely recognized that the predictions of deep neural networks are...
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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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06/18/2020
On Sparsity in Overparametrised Shallow ReLU Networks
The analysis of neural network training beyond their linearization regim...
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06/11/2020
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
We introduce a framework for designing primal methods under the decentra...
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05/19/2020
Continuous LWE
We introduce a continuous analogue of the Learning with Errors (LWE) pro...
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03/02/2020
A Permutation-Equivariant Neural Network Architecture For Auction Design
Designing an incentive compatible auction that maximizes expected revenu...
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02/27/2020
Provably Efficient Third-Person Imitation from Offline Observation
Domain adaptation in imitation learning represents an essential step tow...
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02/14/2020
A mean-field analysis of two-player zero-sum games
Finding Nash equilibria in two-player zero-sum continuous games is a cen...
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02/10/2020
Can graph neural networks count substructures?
The ability to detect and count certain substructures in graphs is impor...
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11/30/2019
Probing the State of the Art: A Critical Look at Visual Representation Evaluation
Self-supervised research improved greatly over the past half decade, wit...
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10/21/2019
Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
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10/03/2019
Pure and Spurious Critical Points: a Geometric Study of Linear Networks
The critical locus of the loss function of a neural network is determine...
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06/18/2019
