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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 107: Mathematical and Scientific Machine Learning, 20-24 July 2020, Princeton University, Princeton, NJ, USA

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Editors: Jianfeng Lu, Rachel Ward

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Deep learning interpretation: Flip points and homotopy methods

; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:1-26

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Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning

Alia Abbaras, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:27-54

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Exact asymptotics for phase retrieval and compressed sensing with random generative priors

Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:55-73

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SchrödingerRNN: Generative modeling of raw audio as a continuously observed quantum state

Beñat Mencia Uranga, Austen Lamacraft; Proceedings of the First Mathematical and Scientific Machine Learning Conference, PMLR 107:74-106

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On the stable recovery of deep structured linear networks under sparsity constraints

François Malgouyres; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:107-127

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Neural network integral representations with the ReLU activation function

Armenak Petrosyan, Anton Dereventsov, Clayton G. Webster; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:128-143

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A type of generalization error induced by initialization in deep neural networks

Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:144-164

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Non-Gaussian processes and neural networks at finite widths

Sho Yaida; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:165-192

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SelectNet: Learning to Sample from the Wild for Imbalanced Data Training

Yunru Liu, Tingran Gao, Haizhao Yang; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:193-206

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Calibrating Multivariate Lévy Processes with Neural Networks

Kailai Xu, Eric Darve; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:207-220

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Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games

Jiequn Han, Ruimeng Hu; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:221-245

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Borrowing From the Future: An Attempt to Address Double Sampling

Yuhua Zhu, Lexing Ying; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:246-268

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Deep Domain Decomposition Method: Elliptic Problems

Wuyang Li, Xueshuang Xiang, Yingxiang Xu; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:269-286

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Landscape Complexity for the Empirical Risk of Generalized Linear Models

Antoine Maillard, Gérard Ben Arous, Giulio Biroli; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:287-327

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DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:328-351

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NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data

Yifan Sun, Linan Zhang, Hayden Schaeffer; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:352-372

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The Slow Deterioration of the Generalization Error of the Random Feature Model

Chao Ma, Lei Wu, Weinan E; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:373-389

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Large deviations for the perceptron model and consequences for active learning

Hugo Cui, Luca Saglietti, Lenka Zdeborova; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:390-430

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Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization

Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:431-450

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Deep learning Markov and Koopman models with physical constraints

Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:451-475

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Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs

Tankut Can, Kamesh Krishnamurthy, David J. Schwab; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:476-511

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Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint

Eric C. Cyr, Mamikon A. Gulian, Ravi G. Patel, Mauro Perego, Nathaniel A. Trask; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:512-536

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New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert Advice

Vladimir A. Kobzar, Robert V. Kohn, Zhilei Wang; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:537-554

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Data-driven Compact Models for Circuit Design and Analysis

K. Aadithya, P. Kuberry, B. Paskaleva, P. Bochev, K. Leeson, A. Mar, T. Mei, E. Keiter; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:555-569

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Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:570-604

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Policy Gradient based Quantum Approximate Optimization Algorithm

Jiahao Yao, Marin Bukov, Lin Lin; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:605-634

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Quantum Ground States from Reinforcement Learning

Ariel Barr, Willem Gispen, Austen Lamacraft; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:635-653

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