
Variational ActorCritic Algorithms
We introduce a class of variational actorcritic algorithms based on a v...
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Shrinkage Estimation of Functions of Large Noisy Symmetric Matrices
We study the problem of estimating functions of a large symmetric matrix...
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Accelerating Inference for Sparse Extreme MultiLabel Ranking Trees
Treebased models underpin many modern semantic search engines and recom...
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Combining resampling and reweighting for faithful stochastic optimization
Many machine learning and data science tasks require solving nonconvex ...
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The Sobolev Regularization Effect of Stochastic Gradient Descent
The multiplicative structure of parameters and input data in the first l...
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A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networks
In this paper, we propose a semigroup method for solving highdimensiona...
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Approximate inversion of discrete Fourier integral operators
This paper introduces a factorization for the inverse of discrete Fourie...
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Operator Augmentation for General Noisy Matrix Systems
In the computational sciences, one must often estimate model parameters ...
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Topk eXtreme Contextual Bandits with Arm Hierarchy
Motivated by modern applications, such as online advertisement and recom...
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How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Performative distribution shift captures the setting where the choice of...
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MultiLevel FineTuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data
In scientific machine learning, regression networks have been recently a...
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An efficient dynamical lowrank algorithm for the BoltzmannBGK equation close to the compressible viscous flow regime
It has recently been demonstrated that dynamical lowrank algorithms can...
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Achieving Adversarial Robustness Requires An Active Teacher
A new understanding of adversarial examples and adversarial robustness i...
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Solving for high dimensional committor functions using neural network with online approximation to derivatives
This paper proposes a new method based on neural networks for computing ...
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Operator Augmentation for Noisy Elliptic Systems
In the computational sciences, one must often estimate model parameters ...
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Efficient LongRange Convolutions for Point Clouds
The efficient treatment of longrange interactions for point clouds is a...
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Why resampling outperforms reweighting for correcting sampling bias
A data set sampled from a certain population is biased if the subgroups ...
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A heuristic independent particle approximation to determinantal point processes
A determinantal point process is a stochastic point process that is comm...
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Distributedmemory ℋmatrix Algebra I: Data Distribution and Matrixvector Multiplication
We introduce a data distribution scheme for ℋmatrices and a distributed...
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A Simple Multiscale Method for Mean Field Games
This paper proposes a multiscale method for solving the numerical soluti...
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Natural Gradient for Combined Loss Using Wavelets
Natural gradients have been widely used in optimization of loss function...
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Borrowing From the Future: Addressing Double Sampling in Modelfree Control
In modelfree reinforcement learning, the temporal difference method and...
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Hierarchical Interpolative Factorization Preconditioner for Parabolic Equations
This note proposes an efficient preconditioner for solving linear and se...
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Mirror Descent Algorithms for Minimizing Interacting Free Energy
This note considers the problem of minimizing interacting free energy. M...
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A Meanfield Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth
Training deep neural networks with stochastic gradient descent (SGD) can...
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A Sharp Convergence Rate for the Asynchronous Stochastic Gradient Descent
We give a sharp convergence rate for the asynchronous stochastic gradien...
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Borrowing From the Future: An Attempt to Address Double Sampling
For modelfree reinforcement learning, the main difficulty of stochastic...
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Hierarchical Lowrank Structure of Parameterized Distributions
This note shows that the matrix forms of several oneparameter distribut...
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Solving Inverse Wave Scattering with Deep Learning
This paper proposes a neural network approach for solving two classical ...
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Solving Traveltime Tomography with Deep Learning
This paper introduces a neural network approach for solving twodimensio...
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CoarseProxy Reduced Basis Methods for Integral Equations
In this paper, we introduce a new reduced basis methodology for accelera...
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Solving Optical Tomography with Deep Learning
This paper presents a neural network approach for solving twodimensiona...
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Metalearning Pseudodifferential Operators with Deep Neural Networks
This paper introduces a metalearning approach for parameterized pseudo...
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Solving Electrical Impedance Tomography with Deep Learning
This paper introduces a new approach for solving electrical impedance to...
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Hessian transport Gradient flows
We derive new gradient flows of divergence functions in the probability ...
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SwitchNet: a neural network model for forward and inverse scattering problems
We propose a novel neural network architecture, SwitchNet, for solving t...
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BCRNet: a neural network based on the nonstandard wavelet form
This paper proposes a novel neural network architecture inspired by the ...
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Stochastic modified equations for the asynchronous stochastic gradient descent
We propose a stochastic modified equations (SME) for modeling the asynch...
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Solving for high dimensional committor functions using artificial neural networks
In this note we propose a method based on artificial neural network to s...
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Distributedmemory Hierarchical Interpolative Factorization
The hierarchical interpolative factorization (HIF) offers an efficient w...
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