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


Editors: Jianfeng Lu, Rachel Ward


Deep learning interpretation: Flip points and homotopy methods

Roozbeh Yousefzadeh,  Dianne P. O’Leary ; PMLR 107:1-26

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

Alia Abbaras,  Benjamin Aubin,  Florent Krzakala,  Lenka Zdeborová ; PMLR 107:27-54

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

Benjamin Aubin,  Bruno Loureiro,  Antoine Baker,  Florent Krzakala,  Lenka Zdeborová ; PMLR 107:55-73

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

Beñat Mencia Uranga,  Austen Lamacraft ; PMLR 107:74-106

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

François Malgouyres ; PMLR 107:107-127

Neural network integral representations with the ReLU activation function

Armenak Petrosyan,  Anton Dereventsov,  Clayton G. Webster ; PMLR 107:128-143

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

Yaoyu Zhang,  Zhi-Qin John Xu,  Tao Luo,  Zheng Ma ; PMLR 107:144-164

Non-Gaussian processes and neural networks at finite widths

Sho Yaida ; PMLR 107:165-192

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

Yunru Liu,  Tingran Gao,  Haizhao Yang ; PMLR 107:193-206

Calibrating Multivariate Lévy Processes with Neural Networks

Kailai Xu,  Eric Darve ; PMLR 107:207-220

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

Jiequn Han,  Ruimeng Hu ; PMLR 107:221-245

Borrowing From the Future: An Attempt to Address Double Sampling

Yuhua Zhu,  Lexing Ying ; PMLR 107:246-268

Deep Domain Decomposition Method: Elliptic Problems

Wuyang Li,  Xueshuang Xiang,  Yingxiang Xu ; PMLR 107:269-286

Landscape Complexity for the Empirical Risk of Generalized Linear Models

Antoine Maillard,  Gérard Ben Arous,  Giulio Biroli ; PMLR 107:287-327

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 ; PMLR 107:328-351

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

Yifan Sun,  Linan Zhang,  Hayden Schaeffer ; PMLR 107:352-372

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

Chao Ma,  Lei Wu,  Weinan E ; PMLR 107:373-389

Large deviations for the perceptron model and consequences for active learning

Hugo Cui,  Luca Saglietti,  Lenka Zdeborova ; PMLR 107:390-430

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

Zhongshu Xu,  Yingzhou Li,  Xiuyuan Cheng ; PMLR 107:431-450

Deep learning Markov and Koopman models with physical constraints

Andreas Mardt,  Luca Pasquali,  Frank Noé,  Hao Wu ; PMLR 107:451-475

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

Tankut Can,  Kamesh Krishnamurthy,  David J. Schwab ; PMLR 107:476-511

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 ; PMLR 107:512-536

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

Vladimir A. Kobzar,  Robert V. Kohn,  Zhilei Wang ; PMLR 107:537-554

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 ; PMLR 107:555-569

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

Michael Perlmutter,  Feng Gao,  Guy Wolf,  Matthew Hirn ; PMLR 107:570-604

Policy Gradient based Quantum Approximate Optimization Algorithm

Jiahao Yao,  Marin Bukov,  Lin Lin ; PMLR 107:605-634

Quantum Ground States from Reinforcement Learning

Ariel Barr,  Willem Gispen,  Austen Lamacraft ; PMLR 107:635-653

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