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ORCIDJoan Bruna
Joan Bruna Estrach
Person information
- affiliation: New York University, Courant Institute, NY, USA
- affiliation: University of California, Berkeley, Department of Statistics, CA, USA
- affiliation: École Polytechnique, Palaiseau, France
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2020 – today
- 2026
[j14]Yossi Arjevani, Joan Bruna, Joe Kileel, Elzbieta Polak, Matthew Trager:
Geometry and Optimization of Shallow Polynomial Networks. SIAM J. Appl. Algebra Geom. 10(2): 174-209 (2026)- [j13]Laura Balzano
, Joan Bruna Estrach, Gitta Kutyniok, Robert Nowak, Jong Chul Ye:
Guest Editorial for Part 1 of the Special Issue on the Mathematics of Deep Learning [From the Guest Editors]. IEEE Signal Process. Mag. 43(2): 6-8 (2026) - [i110]Qi Liu, Laure Zanna, Joan Bruna:
Towards Infinitely Long Neural Simulations: Self-Refining Neural Surrogate Models for Dynamical Systems. CoRR abs/2603.17750 (2026) - [i109]Shauli Ravfogel, Gilad Yehudai, Joan Bruna, Alberto Bietti:
Geometric Factual Recall in Transformers. CoRR abs/2605.12426 (2026) - [i108]Margalit Glasgow, Joan Bruna:
Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks. CoRR abs/2605.22010 (2026) - [i107]Maya Bechler-Speicher, Gilad Yehudai, Gil Harari, Clayton Sanford, Amir Globerson, Joan Bruna:
Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers. CoRR abs/2605.22471 (2026) - 2025
- [j12]Karl Otness, Laure Zanna, Joan Bruna:
Data-driven multiscale modeling for correcting dynamical systems. Mach. Learn. Sci. Technol. 6(4): 45072 (2025) - [c77]Margalit Glasgow, Denny Wu, Joan Bruna:
Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon. COLT 2025: 2461-2539 - [c76]Lei Chen, Joan Bruna, Alberto Bietti:
Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers. ICLR 2025 - [c75]Noah Amsel, Gilad Yehudai, Joan Bruna:
Quality over Quantity in Attention Layers: When Adding More Heads Hurts. ICLR 2025 - [c74]Christian Pedersen, Laure Zanna, Joan Bruna:
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos. ICML 2025 - [i106]Yossi Arjevani, Joan Bruna, Joe Kileel, Elzbieta Polak, Matthew Trager:
Geometry and Optimization of Shallow Polynomial Networks. CoRR abs/2501.06074 (2025) - [i105]Gilad Yehudai, Noah Amsel, Joan Bruna:
Compositional Reasoning with Transformers, RNNs, and Chain of Thought. CoRR abs/2503.01544 (2025) - [i104]Christian Pedersen, Laure Zanna, Joan Bruna:
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos. CoRR abs/2503.18731 (2025) - [i103]Joan Bruna, Daniel Hsu:
Survey on Algorithms for multi-index models. CoRR abs/2504.05426 (2025) - [i102]Margalit Glasgow, Denny Wu, Joan Bruna:
Propagation of Chaos in One-hidden-layer Neural Networks beyond Logarithmic Time. CoRR abs/2504.13110 (2025) - [i101]Alex Damian, Jason D. Lee, Joan Bruna:
The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models. CoRR abs/2506.05500 (2025) - [i100]Hyunsu Kim, Jonggeon Park, Joan Bruna, Hongseok Yang, Juho Lee:
Axial Neural Networks for Dimension-Free Foundation Models. CoRR abs/2510.13665 (2025) - [i99]Shauli Ravfogel, Gilad Yehudai, Tal Linzen, Joan Bruna, Alberto Bietti:
Emergence of Linear Truth Encodings in Language Models. CoRR abs/2510.15804 (2025) - [i98]Chirag Modi, Jiequn Han, Eric Vanden-Eijnden, Joan Bruna:
Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants. CoRR abs/2512.10857 (2025) - 2024
- [j11]Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden:
Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. 496: 112588 (2024) - [c73]Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). COLT 2024: 1262 - [c72]Aaron Zweig, Joan Bruna:
Symmetric Single Index Learning. ICLR 2024 - [c71]Joan Bruna, Jiequn Han:
Provable Posterior Sampling with Denoising Oracles via Tilted Transport. NeurIPS 2024 - [c70]Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen:
Stochastic Optimal Control Matching. NeurIPS 2024 - [d4]Dhruv Balwada, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Berner, Venkatramani Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitch Bushuk, William E. Chapman, Alex Connolly, Julie Deshayes, Carlos Fernandez-Granda, Pierre Gentine, Anastasiia Gorbunova, Will Gregory, Shubham Gupta, Arthur Guillaumin, Marika M. Holland, J. Emmanuel Johnsson, Julien Le Sommer, Zhiwei Li, Nora Loose, Feiyu Lu, Paul O'Gorman, Pavel Perezhogin, Brandon Reichl, Andrew Slavin Ross, Aakash Sane, Sara Shamekh, Tarun Verma, Janni Yuval, Lorenzo Zampieri, Cheng Zhang, Laure Zanna:
m2lines/L96_demo: v1.0.1. Version v1.0.1. Zenodo, 2024 [all versions]
- [d3]Dhruv Balwada, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Brenner, Venkatramani Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitch Bushuk, William E. Chapman, Alex Connolly, Julie Deshayes, Carlos Fernandez-Granda, Pierre Gentine, Anastasiia Gorbunova, Will Gregory, Arthur Guillaumin, Shubham Gupta, Marika M. Holland, J. Emmanuel Johnsson, Julien Le Sommer, Zhiwei Li, Nora Loose, Feiyu Lu, Paul O'Gorman, Pavel Perezhogin, Brandon Reichl, Andrew Slavin Ross, Aakash Sane, Sara Shamekh, Tarun Verma, Janni Yuval, Lorenzo Zampieri, Cheng Zhang, Laure Zanna:
m2lines/L96_demo: Final version for JOSE. Version v1.0.2. Zenodo, 2024 [all versions] - [d2]Dhruv Balwada, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Berner, Venkatramani Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitch Bushuk, William E. Chapman, Alex Connolly, Julie Deshayes, Carlos Fernandez-Granda, Pierre Gentine, Anastasiia Gorbunova, Will Gregory, Arthur Guillaumin, Shubham Gupta, Johanna Goldman, Marika M. Holland, J. Emmanuel Johnsson, Julien Le Sommer, Ziwei Li, Nora Loose, Feiyu Lu, Paul O'Gorman, Pavel Perezhogin, Brandon Reichl, Andrew Slavin Ross, Aakash Sane, Sara Shamekh, Tarun Verma, Janni Yuval, Lorenzo Zampieri, Cheng Zhang, Laure Zanna:
m2lines/L96_demo: Final version for JOSE with DOIs. Version v1.0.3. Zenodo, 2024 [all versions] - [d1]Dhruv Balwada, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Berner, Venkatramani Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitch Bushuk, William E. Chapman, Alex Connolly, Julie Deshayes, Carlos Fernandez-Granda, Pierre Gentine, Anastasiia Gorbunova, Will Gregory, Arthur P. Guillaumin, Shubham Gupta, Marika M. Holland, J. Emmanuel Johnsson, Julien Le Sommer, Ziwei Li, Nora Loose, Feiyu Lu, Paul O'Gorman, Pavel Perezhogin, Brandon Reichl, Andrew Slavin Ross, Aakash Sane, Sara Shamekh, Tarun Verma, Janni Yuval, Lorenzo Zampieri, Cheng Zhang, Laure Zanna:
Learning Machine Learning with Lorenz-96. Version 4. Zenodo, 2024 [all versions] - [i97]Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna:
Computational-Statistical Gaps in Gaussian Single-Index Models. CoRR abs/2403.05529 (2024) - [i96]Lei Chen, Joan Bruna, Alberto Bietti:
How Truncating Weights Improves Reasoning in Language Models. CoRR abs/2406.03068 (2024) - [i95]Joan Bruna, Jiequn Han:
Posterior Sampling with Denoising Oracles via Tilted Transport. CoRR abs/2407.00745 (2024) - [i94]Noah Amsel, Gilad Yehudai, Joan Bruna:
On the Benefits of Rank in Attention Layers. CoRR abs/2407.16153 (2024) - 2023
- [c69]Lei Chen, Joan Bruna:
Beyond the Edge of Stability via Two-step Gradient Updates. ICML 2023: 4330-4391 - [c68]Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat:
Conditionally Strongly Log-Concave Generative Models. ICML 2023: 12224-12251 - [c67]David Brandfonbrener, Ofir Nachum, Joan Bruna:
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. NeurIPS 2023 - [c66]Vignesh Kothapalli, Tom Tirer, Joan Bruna:
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks. NeurIPS 2023 - [c65]Aaron Zweig, Loucas Pillaud-Vivien, Joan Bruna:
On Single-Index Models beyond Gaussian Data. NeurIPS 2023 - [i93]Karl Otness, Laure Zanna, Joan Bruna:
Data-driven multiscale modeling of subgrid parameterizations in climate models. CoRR abs/2303.17496 (2023) - [i92]David Brandfonbrener, Ofir Nachum, Joan Bruna:
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation. CoRR abs/2305.16985 (2023) - [i91]Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat:
Conditionally Strongly Log-Concave Generative Models. CoRR abs/2306.00181 (2023) - [i90]Vignesh Kothapalli, Tom Tirer, Joan Bruna:
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks. CoRR abs/2307.01951 (2023) - [i89]Joan Bruna, Loucas Pillaud-Vivien, Aaron Zweig:
On Single Index Models beyond Gaussian Data. CoRR abs/2307.15804 (2023) - [i88]Aaron Zweig, Joan Bruna:
Symmetric Single Index Learning. CoRR abs/2310.02117 (2023) - [i87]Alberto Bietti, Joan Bruna, Loucas Pillaud-Vivien:
On Learning Gaussian Multi-index Models with Gradient Flow. CoRR abs/2310.19793 (2023) - [i86]Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen:
Stochastic Optimal Control Matching. CoRR abs/2312.02027 (2023) - 2022
- [j10]Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna:
Depth separation beyond radial functions. J. Mach. Learn. Res. 23: 122:1-122:56 (2022) - [j9]Stefanos Zafeiriou, Michael M. Bronstein, Taco Cohen, Oriol Vinyals, Le Song, Jure Leskovec, Pietro Liò, Joan Bruna, Marco Gori:
Guest Editorial: Non-Euclidean Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(2): 723-726 (2022) - [j8]Pau Batlle, Joan Bruna, Carlos Fernandez-Granda, Victor M. Preciado:
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks. SIAM J. Control. Optim. 60(2): S274-S293 (2022) - [c64]Ilias Zadik, Min Jae Song, Alexander S. Wein, Joan Bruna:
Lattice-Based Methods Surpass Sum-of-Squares in Clustering. COLT 2022: 1247-1248 - [c63]Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, Or Litany:
Neural Fields as Learnable Kernels for 3D Reconstruction. CVPR 2022: 18479-18489 - [c62]Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok:
Cartoon Explanations of Image Classifiers. ECCV (12) 2022: 443-458 - [c61]Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna:
On feature learning in neural networks with global convergence guarantees. ICLR 2022 - [c60]Tom Tirer, Joan Bruna:
Extended Unconstrained Features Model for Exploring Deep Neural Collapse. ICML 2022: 21478-21505 - [c59]Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning single-index models with shallow neural networks. NeurIPS 2022 - [c58]David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? NeurIPS 2022 - [c57]Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon:
On Non-Linear operators for Geometric Deep Learning. NeurIPS 2022 - [c56]Aaron Zweig, Joan Bruna:
Exponential Separations in Symmetric Neural Networks. NeurIPS 2022 - [i85]Tom Tirer, Joan Bruna:
Extended Unconstrained Features Model for Exploring Deep Neural Collapse. CoRR abs/2202.08087 (2022) - [i84]Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden:
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR abs/2203.01360 (2022) - [i83]Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna:
On Feature Learning in Neural Networks with Global Convergence Guarantees. CoRR abs/2204.10782 (2022) - [i82]David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna:
When does return-conditioned supervised learning work for offline reinforcement learning? CoRR abs/2206.01079 (2022) - [i81]Aaron Zweig, Joan Bruna:
Exponential Separations in Symmetric Neural Networks. CoRR abs/2206.01266 (2022) - [i80]Lei Chen, Joan Bruna:
On Gradient Descent Convergence beyond the Edge of Stability. CoRR abs/2206.04172 (2022) - [i79]Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon:
On Non-Linear operators for Geometric Deep Learning. CoRR abs/2207.03485 (2022) - [i78]Aaron Zweig, Joan Bruna:
Towards Antisymmetric Neural Ansatz Separation. CoRR abs/2208.03264 (2022) - [i77]Alberto Bietti, Joan Bruna, Clayton Sanford, Min Jae Song:
Learning Single-Index Models with Shallow Neural Networks. CoRR abs/2210.15651 (2022) - [i76]Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna:
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks. CoRR abs/2210.16286 (2022) - 2021
- [c55]Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg:
A Permutation-Equivariant Neural Network Architecture For Auction Design. AAAI 2021: 5664-5672 - [c54]Francis Williams, Matthew Trager, Joan Bruna, Denis Zorin:
Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks. CVPR 2021: 9949-9958 - [c53]Lei Chen, Zhengdao Chen, Joan Bruna:
Learning the Relevant Substructures for Tasks on Graph Data. ICASSP 2021: 8528-8532 - [c52]Lei Chen, Zhengdao Chen, Joan Bruna:
On Graph Neural Networks versus Graph-Augmented MLPs. ICLR 2021 - [c51]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline Contextual Bandits with Overparameterized Models. ICML 2021: 1049-1058 - [c50]Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. ICML 2021: 2771-2782 - [c49]Aaron Zweig, Joan Bruna:
A Functional Perspective on Learning Symmetric Functions with Neural Networks. ICML 2021: 13023-13032 - [c48]Tom Tirer, Joan Bruna, Raja Giryes:
Kernel-Based Smoothness Analysis of Residual Networks. MSML 2021: 921-954 - [c47]David Brandfonbrener, Will Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. NeurIPS 2021: 4933-4946 - [c46]Alberto Bietti, Luca Venturi, Joan Bruna:
On the Sample Complexity of Learning under Geometric Stability. NeurIPS 2021: 18673-18684 - [c45]Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin:
An Extensible Benchmark Suite for Learning to Simulate Physical Systems. NeurIPS Datasets and Benchmarks 2021 - [c44]Min Jae Song, Ilias Zadik, Joan Bruna:
On the Cryptographic Hardness of Learning Single Periodic Neurons. NeurIPS 2021: 29602-29615 - [c43]Joan Bruna, Oded Regev, Min Jae Song, Yi Tang:
Continuous LWE. STOC 2021: 694-707 - [e1]Joan Bruna, Jan S. Hesthaven, Lenka Zdeborová:
Mathematical and Scientific Machine Learning, 16-19 August 2021, Virtual Conference / Lausanne, Switzerland. Proceedings of Machine Learning Research 145, PMLR 2021 [contents] - [i75]Cinjon Resnick, Or Litany, Cosmas Heiß, Hugo Larochelle, Joan Bruna, Kyunghyun Cho:
Self-Supervised Equivariant Scene Synthesis from Video. CoRR abs/2102.00863 (2021) - [i74]Luca Venturi, Samy Jelassi, Tristan Ozuch, Joan Bruna:
Depth separation beyond radial functions. CoRR abs/2102.01621 (2021) - [i73]Yossi Arjevani, Joan Bruna, Michael Field, Joe Kileel, Matthew Trager, Francis Williams:
Symmetry Breaking in Symmetric Tensor Decomposition. CoRR abs/2103.06234 (2021) - [i72]Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR abs/2104.07531 (2021) - [i71]Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic:
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR abs/2104.13478 (2021) - [i70]Alberto Bietti, Luca Venturi, Joan Bruna:
On the Sample Complexity of Learning with Geometric Stability. CoRR abs/2106.07148 (2021) - [i69]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Offline RL Without Off-Policy Evaluation. CoRR abs/2106.08909 (2021) - [i68]Min Jae Song, Ilias Zadik, Joan Bruna:
On the Cryptographic Hardness of Learning Single Periodic Neurons. CoRR abs/2106.10744 (2021) - [i67]Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié, Joan Bruna, Eric Vanden-Eijnden:
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR abs/2107.05134 (2021) - [i66]Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin:
An Extensible Benchmark Suite for Learning to Simulate Physical Systems. CoRR abs/2108.07799 (2021) - [i65]Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok:
Cartoon Explanations of Image Classifiers. CoRR abs/2110.03485 (2021) - [i64]Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok:
A Rate-Distortion Framework for Explaining Black-box Model Decisions. CoRR abs/2110.08252 (2021) - [i63]Yihan He, Joan Bruna:
Multi-fidelity Stability for Graph Representation Learning. CoRR abs/2111.12865 (2021) - [i62]Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, Or Litany:
Neural Fields as Learnable Kernels for 3D Reconstruction. CoRR abs/2111.13674 (2021) - [i61]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Quantile Filtered Imitation Learning. CoRR abs/2112.00950 (2021) - [i60]Ilias Zadik, Min Jae Song, Alexander S. Wein, Joan Bruna:
Lattice-Based Methods Surpass Sum-of-Squares in Clustering. CoRR abs/2112.03898 (2021) - 2020
- [j7]Joan Bruna, Eldad Haber, Gitta Kutyniok, Thomas Pock, René Vidal:
Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science. J. Math. Imaging Vis. 62(3): 277-278 (2020) - [j6]Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg:
Kymatio: Scattering Transforms in Python. J. Mach. Learn. Res. 21: 60:1-60:6 (2020) - [j5]Fernando Gama, Joan Bruna, Alejandro Ribeiro:
Stability Properties of Graph Neural Networks. IEEE Trans. Signal Process. 68: 5680-5695 (2020) - [c42]Fernando Gama, Alejandro Ribeiro, Joan Bruna:
Stability of Graph Neural Networks to Relative Perturbations. ICASSP 2020: 9070-9074 - [c41]David Brandfonbrener, Joan Bruna:
Geometric Insights into the Convergence of Nonlinear TD Learning. ICLR 2020 - [c40]Matthew Trager, Kathlén Kohn, Joan Bruna:
Pure and Spurious Critical Points: a Geometric Study of Linear Networks. ICLR 2020 - [c39]Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok:
A Rate-Distortion Framework for Explaining Black-Box Model Decisions. xxAI@ICML 2020: 91-115 - [c38]Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna:
Extra-gradient with player sampling for faster convergence in n-player games. ICML 2020: 4736-4745 - [c37]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. NeurIPS 2020 - [c36]Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna:
Can Graph Neural Networks Count Substructures? NeurIPS 2020 - [c35]Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden:
A Dynamical Central Limit Theorem for Shallow Neural Networks. NeurIPS 2020 - [c34]Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna:
A mean-field analysis of two-player zero-sum games. NeurIPS 2020 - [c33]Aaron Zweig, Joan Bruna:
Provably Efficient Third-Person Imitation from Offline Observation. UAI 2020: 1228-1237 - [i59]Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna:
Can graph neural networks count substructures? CoRR abs/2002.04025 (2020) - [i58]Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna:
A mean-field analysis of two-player zero-sum games. CoRR abs/2002.06277 (2020) - [i57]Aaron Zweig, Joan Bruna:
Provably Efficient Third-Person Imitation from Offline Observation. CoRR abs/2002.12446 (2020) - [i56]Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg:
A Permutation-Equivariant Neural Network Architecture For Auction Design. CoRR abs/2003.01497 (2020) - [i55]Joan Bruna, Oded Regev, Min Jae Song, Yi Tang:
Continuous LWE. CoRR abs/2005.09595 (2020) - [i54]Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gürbüzbalaban, Stefanie Jegelka, Hongzhou Lin:
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method. CoRR abs/2006.06733 (2020) - [i53]Jaume de Dios, Joan Bruna:
On Sparsity in Overparametrised Shallow ReLU Networks. CoRR abs/2006.10225 (2020) - [i52]Francis Williams, Matthew Trager, Joan Bruna, Denis Zorin:
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks. CoRR abs/2006.13782 (2020) - [i51]David Brandfonbrener, William F. Whitney, Rajesh Ranganath, Joan Bruna:
Overfitting and Optimization in Offline Policy Learning. CoRR abs/2006.15368 (2020) - [i50]Cosmas Heiß, Ron Levie, Cinjon Resnick, Gitta Kutyniok, Joan Bruna:
In-Distribution Interpretability for Challenging Modalities. CoRR abs/2007.00758 (2020) - [i49]Donsub Rim, Luca Venturi, Joan Bruna, Benjamin Peherstorfer:
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems. CoRR abs/2007.13977 (2020) - [i48]Aaron Zweig, Joan Bruna:
A Functional Perspective on Learning Symmetric Functions with Neural Networks. CoRR abs/2008.06952 (2020) - [i47]Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden:
A Dynamical Central Limit Theorem for Shallow Neural Networks. CoRR abs/2008.09623 (2020) - [i46]Tom Tirer, Joan Bruna, Raja Giryes:
Kernel-Based Smoothness Analysis of Residual Networks. CoRR abs/2009.10008 (2020) - [i45]Lei Chen, Zhengdao Chen, Joan Bruna:
On Graph Neural Networks versus Graph-Augmented MLPs. CoRR abs/2010.15116 (2020) - [i44]Pau Batlle, Joan Bruna, Carlos Fernandez-Granda, Victor M. Preciado:
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks. CoRR abs/2011.01998 (2020) - [i43]Cinjon Resnick, Or Litany, Hugo Larochelle, Joan Bruna, Kyunghyun Cho:
Learned Equivariant Rendering without Transformation Supervision. CoRR abs/2011.05787 (2020) - [i42]Joan Bruna, Oded Regev, Min Jae Song, Yi Tang:
Continuous LWE. Electron. Colloquium Comput. Complex. TR20: Article TR20-080 (2020)
2010 – 2019
- 2019
- [j4]Luca Venturi, Afonso S. Bandeira, Joan Bruna:
Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes. J. Mach. Learn. Res. 20: 133:1-133:34 (2019) - [c32]Francis Williams, Teseo Schneider, Cláudio T. Silva, Denis Zorin, Joan Bruna, Daniele Panozzo:
Deep Geometric Prior for Surface Reconstruction. CVPR 2019: 10130-10139 - [c31]Zhengdao Chen, Lisha Li, Joan Bruna:
Supervised Community Detection with Line Graph Neural Networks. ICLR (Poster) 2019 - [c30]Fernando Gama, Alejandro Ribeiro, Joan Bruna:
Diffusion Scattering Transforms on Graphs. ICLR (Poster) 2019 - [c29]Thomas Frerix, Joan Bruna:
Approximating Orthogonal Matrices with Effective Givens Factorization. ICML 2019: 1993-2001 - [c28]Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden:
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. ICML 2019: 5508-5517 - [c27]Fernando Gama, Alejandro Ribeiro, Joan Bruna:
Stability of Graph Scattering Transforms. NeurIPS 2019: 8036-8046 - [c26]Francis Williams, Matthew Trager, Daniele Panozzo, Cláudio T. Silva, Denis Zorin, Joan Bruna:
Gradient Dynamics of Shallow Univariate ReLU Networks. NeurIPS 2019: 8376-8385 - [c25]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna:
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. NeurIPS 2019: 9330-9340 - [c24]Joe Kileel, Matthew Trager, Joan Bruna:
On the Expressive Power of Deep Polynomial Neural Networks. NeurIPS 2019: 10310-10319 - [c23]Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna:
On the equivalence between graph isomorphism testing and function approximation with GNNs. NeurIPS 2019: 15868-15876 - [i41]Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden:
Global convergence of neuron birth-death dynamics. CoRR abs/1902.01843 (2019) - [i40]Jihun Oh, Kyunghyun Cho, Joan Bruna:
Advancing GraphSAGE with A Data-Driven Node Sampling. CoRR abs/1904.12935 (2019) - [i39]Fernando Gama, Joan Bruna, Alejandro Ribeiro:
Stability Properties of Graph Neural Networks. CoRR abs/1905.04497 (2019) - [i38]David Brandfonbrener, Joan Bruna:
On the Expected Dynamics of Nonlinear TD Learning. CoRR abs/1905.12185 (2019) - [i37]Joe Kileel, Matthew Trager, Joan Bruna:
On the Expressive Power of Deep Polynomial Neural Networks. CoRR abs/1905.12207 (2019) - [i36]Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna:
Extra-gradient with player sampling for provable fast convergence in n-player games. CoRR abs/1905.12363 (2019) - [i35]Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna:
On the equivalence between graph isomorphism testing and function approximation with GNNs. CoRR abs/1905.12560 (2019) - [i34]Fernando Gama, Joan Bruna, Alejandro Ribeiro:
Stability of Graph Scattering Transforms. CoRR abs/1906.04784 (2019) - [i33]Stéphane d'Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli:
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. CoRR abs/1906.06766 (2019) - [i32]Francis Williams, Matthew Trager, Cláudio T. Silva, Daniele Panozzo, Denis Zorin, Joan Bruna:
Gradient Dynamics of Shallow Univariate ReLU Networks. CoRR abs/1906.07842 (2019) - [i31]Matthew Trager, Kathlén Kohn, Joan Bruna:
Pure and Spurious Critical Points: a Geometric Study of Linear Networks. CoRR abs/1910.01671 (2019) - [i30]Fernando Gama, Joan Bruna, Alejandro Ribeiro:
Stability of Graph Neural Networks to Relative Perturbations. CoRR abs/1910.09655 (2019) - [i29]Cinjon Resnick, Zeping Zhan, Joan Bruna:
Probing the State of the Art: A Critical Look at Visual Representation Evaluation. CoRR abs/1912.00215 (2019) - 2018
- [c22]Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob N. Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna:
Pommerman: A Multi-Agent Playground. AIIDE Workshops 2018 - [c21]Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna:
Surface Networks. CVPR 2018: 2540-2548 - [c20]Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna:
Revised Note on Learning Quadratic Assignment with Graph Neural Networks. DSW 2018: 229-233 - [c19]Alex Nowak, David Folqué, Joan Bruna:
Divide and Conquer Networks. ICLR (Poster) 2018 - [c18]Victor Garcia Satorras, Joan Bruna Estrach:
Few-Shot Learning with Graph Neural Networks. ICLR (Poster) 2018 - [c17]Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna:
Graph Neural Networks for IceCube Signal Classification. ICMLA 2018: 386-391 - [i28]Luca Venturi, Afonso S. Bandeira, Joan Bruna:
Neural Networks with Finite Intrinsic Dimension have no Spurious Valleys. CoRR abs/1802.06384 (2018) - [i27]Fernando Gama, Alejandro Ribeiro, Joan Bruna:
Diffusion Scattering Transforms on Graphs. CoRR abs/1806.08829 (2018) - [i26]Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alex Peysakhovich, Kyunghyun Cho, Joan Bruna:
Backplay: "Man muss immer umkehren". CoRR abs/1807.06919 (2018) - [i25]David Folqué, Sainbayar Sukhbaatar, Arthur Szlam, Joan Bruna:
Planning with Arithmetic and Geometric Attributes. CoRR abs/1809.02031 (2018) - [i24]Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna:
Graph Neural Networks for IceCube Signal Classification. CoRR abs/1809.06166 (2018) - [i23]Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob N. Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna:
Pommerman: A Multi-Agent Playground. CoRR abs/1809.07124 (2018) - [i22]Francis Williams, Teseo Schneider, Cláudio T. Silva, Denis Zorin, Joan Bruna, Daniele Panozzo:
Deep Geometric Prior for Surface Reconstruction. CoRR abs/1811.10943 (2018) - [i21]Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg:
Kymatio: Scattering Transforms in Python. CoRR abs/1812.11214 (2018) - 2017
- [j3]Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst:
Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Process. Mag. 34(4): 18-42 (2017) - [c16]C. Daniel Freeman, Joan Bruna:
Topology and Geometry of Half-Rectified Network Optimization. ICLR (Poster) 2017 - [c15]Thomas Moreau, Joan Bruna:
Understanding Trainable Sparse Coding with Matrix Factorization. ICLR (Poster) 2017 - [i20]Ilya Kostrikov, Joan Bruna, Daniele Panozzo, Denis Zorin:
Surface Networks. CoRR abs/1705.10819 (2017) - [i19]Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna:
A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks. CoRR abs/1706.07450 (2017) - [i18]Victor Garcia Satorras, Joan Bruna:
Few-Shot Learning with Graph Neural Networks. CoRR abs/1711.04043 (2017) - [i17]René Vidal, Joan Bruna, Raja Giryes, Stefano Soatto:
Mathematics of Deep Learning. CoRR abs/1712.04741 (2017) - 2016
- [j2]Mark Tygert, Joan Bruna, Soumith Chintala, Yann LeCun, Serkan Piantino, Arthur Szlam:
A Mathematical Motivation for Complex-Valued Convolutional Networks. Neural Comput. 28(5): 815-825 (2016) - [c14]Joan Bruna, Pablo Sprechmann, Yann LeCun:
Super-Resolution with Deep Convolutional Sufficient Statistics. ICLR (Poster) 2016 - [i16]Ivan Dokmanic, Joan Bruna, Stéphane Mallat, Maarten V. de Hoop:
Inverse Problems with Invariant Multiscale Statistics. CoRR abs/1609.05502 (2016) - [i15]Shariq Mobin, Joan Bruna:
Voice Conversion using Convolutional Neural Networks. CoRR abs/1610.08927 (2016) - [i14]C. Daniel Freeman, Joan Bruna:
Topology and Geometry of Half-Rectified Network Optimization. CoRR abs/1611.01540 (2016) - [i13]Alex Nowak, Joan Bruna:
Divide and Conquer with Neural Networks. CoRR abs/1611.02401 (2016) - [i12]Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst:
Geometric deep learning: going beyond Euclidean data. CoRR abs/1611.08097 (2016) - 2015
- [c13]Pablo Sprechmann, Joan Bruna, Yann LeCun:
Audio Source Separation with Discriminative Scattering Networks. LVA/ICA 2015: 259-267 - [c12]Joan Bruna, Pablo Sprechmann, Yann LeCun:
Source separation with scattering Non-Negative Matrix Factorization. ICASSP 2015: 1876-1880 - [c11]Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun:
Unsupervised Learning of Spatiotemporally Coherent Metrics. ICCV 2015: 4086-4093 - [c10]Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun:
Unsupervised Feature Learning from Temporal Data. ICLR (Workshop) 2015 - [c9]Pablo Sprechmann, Joan Bruna, Yann LeCun:
Audio Source Separation with Discriminative Scattering Networks. ICLR (Workshop) 2015 - [i11]Joan Bruna, Soumith Chintala, Yann LeCun, Serkan Piantino, Arthur Szlam, Mark Tygert:
A theoretical argument for complex-valued convolutional networks. CoRR abs/1503.03438 (2015) - [i10]Mikael Henaff, Joan Bruna, Yann LeCun:
Deep Convolutional Networks on Graph-Structured Data. CoRR abs/1506.05163 (2015) - 2014
- [c8]Joan Bruna Estrach, Arthur Szlam, Yann LeCun:
Signal recovery from Pooling Representations. ICML 2014: 307-315 - [c7]Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus:
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. NIPS 2014: 1269-1277 - [c6]Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun:
Spectral Networks and Locally Connected Networks on Graphs. ICLR 2014 - [c5]Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus:
Intriguing properties of neural networks. ICLR (Poster) 2014 - [i9]Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus:
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. CoRR abs/1404.0736 (2014) - [i8]Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun:
Unsupervised Learning of Spatiotemporally Coherent Metrics. CoRR abs/1412.6056 (2014) - [i7]Marc'Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michaël Mathieu, Ronan Collobert, Sumit Chopra:
Video (language) modeling: a baseline for generative models of natural videos. CoRR abs/1412.6604 (2014) - 2013
- [b1]Joan Bruna:
Scattering Representations for Recognition. (Representations en Scattering pour la Reconaissance). École Polytechnique, Palaiseau, France, 2013 - [j1]Joan Bruna, Stéphane Mallat:
Invariant Scattering Convolution Networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1872-1886 (2013) - [c4]Joan Bruna, Arthur Szlam, Yann LeCun:
Learning Stable Group Invariant Representations with Convolutional Networks. ICLR (Workshop Poster) 2013 - [i6]Joan Bruna, Stéphane Mallat:
Audio Texture Synthesis with Scattering Moments. CoRR abs/1311.0407 (2013) - [i5]Dilip Krishnan, Joan Bruna, Rob Fergus:
Blind Deconvolution with Re-weighted Sparsity Promotion. CoRR abs/1311.4029 (2013) - 2012
- [i4]Joan Bruna, Stéphane Mallat:
Invariant Scattering Convolution Networks. CoRR abs/1203.1513 (2012) - 2011
- [c3]Joan Bruna, Stéphane Mallat:
Classification with scattering operators. CVPR 2011: 1561-1566 - [c2]Joan Bruna, Stéphane Mallat:
Classification with invariant scattering representations. IVMSP 2011: 99-104 - [i3]Joan Bruna, Stéphane Mallat:
Geometric Models with Co-occurrence Groups. CoRR abs/1101.5766 (2011) - [i2]Joan Bruna, Stéphane Mallat:
Classification with Invariant Scattering Representations. CoRR abs/1112.1120 (2011) - 2010
- [c1]Joan Bruna, Stéphane Mallat:
Geometric models with co-occurrence groups. ESANN 2010 - [i1]Joan Bruna, Stéphane Mallat:
Classification with Scattering Operators. CoRR abs/1011.3023 (2010)
Coauthor Index
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