
Dual Training of EnergyBased Models with Overparametrized Shallow Neural Networks
Energybased models (EBMs) are generative models that are usually traine...
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On the Cryptographic Hardness of Learning Single Periodic Neurons
We show a simple reduction which demonstrates the cryptographic hardness...
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Offline RL Without OffPolicy Evaluation
Most prior approaches to offline reinforcement learning (RL) have taken ...
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On the Sample Complexity of Learning with Geometric Stability
Many supervised learning problems involve highdimensional data such as ...
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science...
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On EnergyBased Models with Overparametrized Shallow Neural Networks
Energybased models (EBMs) are a simple yet powerful framework for gener...
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Symmetry Breaking in Symmetric Tensor Decomposition
In this note, we consider the optimization problem associated with compu...
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Depth separation beyond radial functions
Highdimensional depth separation results for neural networks show that ...
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SelfSupervised Equivariant Scene Synthesis from Video
We propose a selfsupervised framework to learn scene representations fr...
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Learned Equivariant Rendering without Transformation Supervision
We propose a selfsupervised framework to learn scene representations fr...
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On Graph Neural Networks versus GraphAugmented MLPs
From the perspective of expressive power, this work compares multilayer...
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KernelBased Smoothness Analysis of Residual Networks
A major factor in the success of deep neural networks is the use of soph...
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A Dynamical Central Limit Theorem for Shallow Neural Networks
Recent theoretical work has characterized the dynamics of wide shallow n...
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A Functional Perspective on Learning Symmetric Functions with Neural Networks
Symmetric functions, which take as input an unordered, fixedsize set, a...
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Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Classical reduced models are lowrank approximations using a fixed basis...
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InDistribution Interpretability for Challenging Modalities
It is widely recognized that the predictions of deep neural networks are...
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Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
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Neural Splines: Fitting 3D Surfaces with InfinitelyWide Neural Networks
We present Neural Splines, a technique for 3D surface reconstruction tha...
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On Sparsity in Overparametrised Shallow ReLU Networks
The analysis of neural network training beyond their linearization regim...
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IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
We introduce a framework for designing primal methods under the decentra...
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Continuous LWE
We introduce a continuous analogue of the Learning with Errors (LWE) pro...
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A PermutationEquivariant Neural Network Architecture For Auction Design
Designing an incentive compatible auction that maximizes expected revenu...
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Provably Efficient ThirdPerson Imitation from Offline Observation
Domain adaptation in imitation learning represents an essential step tow...
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A meanfield analysis of twoplayer zerosum games
Finding Nash equilibria in twoplayer zerosum continuous games is a cen...
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Can graph neural networks count substructures?
The ability to detect and count certain substructures in graphs is impor...
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Probing the State of the Art: A Critical Look at Visual Representation Evaluation
Selfsupervised research improved greatly over the past half decade, wit...
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Stability of Graph Neural Networks to Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying...
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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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Gradient Dynamics of Shallow Univariate ReLU Networks
We present a theoretical and empirical study of the gradient dynamics of...
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Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Despite the phenomenal success of deep neural networks in a broad range ...
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Stability of Graph Scattering Transforms
Scattering transforms are nontrainable deep convolutional architectures...
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On the equivalence between graph isomorphism testing and function approximation with GNNs
Graph neural networks (GNNs) have achieved lots of success on graphstru...
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Extragradient with player sampling for provable fast convergence in nplayer games
Datadriven model training is increasingly relying on finding Nash equil...
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On the Expressive Power of Deep Polynomial Neural Networks
We study deep neural networks with polynomial activations, particularly ...
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On the Expected Dynamics of Nonlinear TD Learning
While there are convergence guarantees for temporal difference (TD) lear...
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Stability Properties of Graph Neural Networks
Data stemming from networks exhibit an irregular support, whereby each d...
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Advancing GraphSAGE with A DataDriven Node Sampling
As an efficient and scalable graph neural network, GraphSAGE has enabled...
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Global convergence of neuron birthdeath dynamics
Neural networks with a large number of parameters admit a meanfield des...
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Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
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Deep Geometric Prior for Surface Reconstruction
The reconstruction of a discrete surface from a point cloud is a fundame...
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Pommerman: A MultiAgent Playground
We present Pommerman, a multiagent environment based on the classic con...
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Graph Neural Networks for IceCube Signal Classification
Tasks involving the analysis of geometric (graph and manifoldstructure...
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Planning with Arithmetic and Geometric Attributes
A desirable property of an intelligent agent is its ability to understan...
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Backplay: "Man muss immer umkehren"
A longstanding problem in model free reinforcement learning (RL) is tha...
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Diffusion Scattering Transforms on Graphs
Stability is a key aspect of data analysis. In many applications, the na...
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Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys
Neural networks provide a rich class of highdimensional, nonconvex opt...
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Multiscale Sparse Microcanonical Models
We study density estimation of stationary processes defined over an infi...
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Mathematics of Deep Learning
Recently there has been a dramatic increase in the performance of recogn...
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FewShot Learning with Graph Neural Networks
We propose to study the problem of fewshot learning with the prism of i...
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A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks
Many inverse problems are formulated as optimization problems over certa...
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