Joan Bruna

Affiliations:
  • University of California, Berkeley, Department of Statistics, CA, USA
  • New York University, Courant Institute, NY, USA
  • École Polytechnique, Palaiseau, France


According to our database1, Joan Bruna authored at least 109 papers between 2010 and 2022.

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Bibliography

2022
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks.
SIAM J. Control. Optim., 2022

Guest Editorial: Non-Euclidean Machine Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Towards Antisymmetric Neural Ansatz Separation.
CoRR, 2022

On Non-Linear operators for Geometric Deep Learning.
CoRR, 2022

On Gradient Descent Convergence beyond the Edge of Stability.
CoRR, 2022

Exponential Separations in Symmetric Neural Networks.
CoRR, 2022

When does return-conditioned supervised learning work for offline reinforcement learning?
CoRR, 2022

Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations.
CoRR, 2022

Simultaneous Transport Evolution for Minimax Equilibria on Measures.
CoRR, 2022

Extended Unconstrained Features Model for Exploring Deep Neural Collapse.
Proceedings of the International Conference on Machine Learning, 2022

On feature learning in neural networks with global convergence guarantees.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Lattice-Based Methods Surpass Sum-of-Squares in Clustering.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Quantile Filtered Imitation Learning.
CoRR, 2021

Neural Fields as Learnable Kernels for 3D Reconstruction.
CoRR, 2021

Multi-fidelity Stability for Graph Representation Learning.
CoRR, 2021

Cartoon Explanations of Image Classifiers.
CoRR, 2021

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks.
CoRR, 2021

On the Sample Complexity of Learning with Geometric Stability.
CoRR, 2021

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.
CoRR, 2021

Symmetry Breaking in Symmetric Tensor Decomposition.
CoRR, 2021

Depth separation beyond radial functions.
CoRR, 2021

Self-Supervised Equivariant Scene Synthesis from Video.
CoRR, 2021

On the Cryptographic Hardness of Learning Single Periodic Neurons.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Extensible Benchmark Suite for Learning to Simulate Physical Systems.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Offline RL Without Off-Policy Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Sample Complexity of Learning under Geometric Stability.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Kernel-Based Smoothness Analysis of Residual Networks.
Proceedings of the Mathematical and Scientific Machine Learning, 2021

A Functional Perspective on Learning Symmetric Functions with Neural Networks.
Proceedings of the 38th International Conference on Machine Learning, 2021

On Energy-Based Models with Overparametrized Shallow Neural Networks.
Proceedings of the 38th International Conference on Machine Learning, 2021

Offline Contextual Bandits with Overparameterized Models.
Proceedings of the 38th International Conference on Machine Learning, 2021

On Graph Neural Networks versus Graph-Augmented MLPs.
Proceedings of the 9th International Conference on Learning Representations, 2021

Learning the Relevant Substructures for Tasks on Graph Data.
Proceedings of the IEEE International Conference on Acoustics, 2021

Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

A Permutation-Equivariant Neural Network Architecture For Auction Design.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Stability Properties of Graph Neural Networks.
IEEE Trans. Signal Process., 2020

Kymatio: Scattering Transforms in Python.
J. Mach. Learn. Res., 2020

Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science.
J. Math. Imaging Vis., 2020

Continuous LWE.
Electron. Colloquium Comput. Complex., 2020

Learned Equivariant Rendering without Transformation Supervision.
CoRR, 2020

Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems.
CoRR, 2020

In-Distribution Interpretability for Challenging Modalities.
CoRR, 2020

Overfitting and Optimization in Offline Policy Learning.
CoRR, 2020

On Sparsity in Overparametrised Shallow ReLU Networks.
CoRR, 2020

Provably Efficient Third-Person Imitation from Offline Observation.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

A mean-field analysis of two-player zero-sum games.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Dynamical Central Limit Theorem for Shallow Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Can Graph Neural Networks Count Substructures?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Rate-Distortion Framework for Explaining Black-Box Model Decisions.
Proceedings of the xxAI - Beyond Explainable AI, 2020

Extra-gradient with player sampling for faster convergence in n-player games.
Proceedings of the 37th International Conference on Machine Learning, 2020

Pure and Spurious Critical Points: a Geometric Study of Linear Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

Geometric Insights into the Convergence of Nonlinear TD Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Stability of Graph Neural Networks to Relative Perturbations.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes.
J. Mach. Learn. Res., 2019

Probing the State of the Art: A Critical Look at Visual Representation Evaluation.
CoRR, 2019

Extra-gradient with player sampling for provable fast convergence in n-player games.
CoRR, 2019

On the Expected Dynamics of Nonlinear TD Learning.
CoRR, 2019

Advancing GraphSAGE with A Data-Driven Node Sampling.
CoRR, 2019

Global convergence of neuron birth-death dynamics.
CoRR, 2019

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Gradient Dynamics of Shallow Univariate ReLU Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Expressive Power of Deep Polynomial Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stability of Graph Scattering Transforms.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the equivalence between graph isomorphism testing and function approximation with GNNs.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically.
Proceedings of the 36th International Conference on Machine Learning, 2019

Approximating Orthogonal Matrices with Effective Givens Factorization.
Proceedings of the 36th International Conference on Machine Learning, 2019

Diffusion Scattering Transforms on Graphs.
Proceedings of the 7th International Conference on Learning Representations, 2019

Supervised Community Detection with Line Graph Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

Deep Geometric Prior for Surface Reconstruction.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Kymatio: Scattering Transforms in Python.
CoRR, 2018

Planning with Arithmetic and Geometric Attributes.
CoRR, 2018

Backplay: "Man muss immer umkehren".
CoRR, 2018

Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys.
CoRR, 2018

Graph Neural Networks for IceCube Signal Classification.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Few-Shot Learning with Graph Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Divide and Conquer Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Revised Note on Learning Quadratic Assignment with Graph Neural Networks.
Proceedings of the 2018 IEEE Data Science Workshop, 2018

Surface Networks.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Pommerman: A Multi-Agent Playground.
Proceedings of the Joint Proceedings of the AIIDE 2018 Workshops co-located with 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2018), 2018

2017
Geometric Deep Learning: Going beyond Euclidean data.
IEEE Signal Process. Mag., 2017

Mathematics of Deep Learning.
CoRR, 2017

A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.
CoRR, 2017

Surface Networks.
CoRR, 2017

Understanding Trainable Sparse Coding with Matrix Factorization.
Proceedings of the 5th International Conference on Learning Representations, 2017

Topology and Geometry of Half-Rectified Network Optimization.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
A Mathematical Motivation for Complex-Valued Convolutional Networks.
Neural Comput., 2016

Divide and Conquer with Neural Networks.
CoRR, 2016

Voice Conversion using Convolutional Neural Networks.
CoRR, 2016

Inverse Problems with Invariant Multiscale Statistics.
CoRR, 2016

Super-Resolution with Deep Convolutional Sufficient Statistics.
Proceedings of the 4th International Conference on Learning Representations, 2016

2015
Audio Source Separation with Discriminative Scattering Networks.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Deep Convolutional Networks on Graph-Structured Data.
CoRR, 2015

Unsupervised Feature Learning from Temporal Data.
Proceedings of the 3rd International Conference on Learning Representations, 2015

A theoretical argument for complex-valued convolutional networks.
CoRR, 2015

Unsupervised Learning of Spatiotemporally Coherent Metrics.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Source separation with scattering Non-Negative Matrix Factorization.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Intriguing properties of neural networks.
Proceedings of the 2nd International Conference on Learning Representations, 2014

Video (language) modeling: a baseline for generative models of natural videos.
CoRR, 2014

Spectral Networks and Locally Connected Networks on Graphs.
Proceedings of the 2nd International Conference on Learning Representations, 2014

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Signal recovery from Pooling Representations.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Scattering Representations for Recognition. (Representations en Scattering pour la Reconaissance).
PhD thesis, 2013

Invariant Scattering Convolution Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2013

Learning Stable Group Invariant Representations with Convolutional Networks
Proceedings of the 1st International Conference on Learning Representations, 2013

Blind Deconvolution with Re-weighted Sparsity Promotion.
CoRR, 2013

Audio Texture Synthesis with Scattering Moments.
CoRR, 2013

2011
Classification with invariant scattering representations.
Proceedings of the IEEE 10th Image, 2011

Classification with scattering operators.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010
Geometric models with co-occurrence groups.
Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010


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