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07/10/2023
Heisenberg-limited Hamiltonian learning for interacting bosons
We develop a protocol for learning a class of interacting bosonic Hamilt...
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05/25/2023
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality
This paper studies the use of a machine learning-based estimator as a co...
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05/02/2023
Computing Free Convolutions via Contour Integrals
This work proposes algorithms for computing additive and multiplicative ...
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03/02/2023
A note on spike localization for line spectrum estimation
This note considers the problem of approximating the locations of domina...
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01/21/2023
On efficient quantum block encoding of pseudo-differential operators
Block encoding lies at the core of many existing quantum algorithms. Mea...
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12/01/2022
High-dimensional density estimation with tensorizing flow
We propose the tensorizing flow method for estimating high-dimensional p...
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11/28/2022
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls
The optimal design of experiments typically involves solving an NP-hard ...
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10/14/2022
Continuous-in-time Limit for Bayesian Bandits
This paper revisits the bandit problem in the Bayesian setting. The Baye...
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10/13/2022
Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries
In this paper, we show that structures similar to self-attention are nat...
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09/28/2022
Minimax Optimal Kernel Operator Learning via Multilevel Training
Learning mappings between infinite-dimensional function spaces has achie...
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09/19/2022
Importance Tempering: Group Robustness for Overparameterized Models
Although overparameterized models have shown their success on many machi...
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09/03/2022
Generative Modeling via Tree Tensor Network States
In this paper, we present a density estimation framework based on tree t...
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08/17/2022
Correcting Convexity Bias in Function and Functional Estimate
A general framework with a series of different methods is proposed to im...
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08/03/2022
Bayesian regularization of empirical MDPs
In most applications of model-based Markov decision processes, the param...
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05/17/2022
Annealed importance sampling for Ising models with mixed boundary conditions
This note introduces a method for sampling Ising models with mixed bound...
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05/15/2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
In this paper, we study the statistical limits in terms of Sobolev norms...
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05/15/2022
Double Flip Move for Ising Models with Mixed Boundary Conditions
This note introduces the double flip move for accelerating the Swendsen-...
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04/24/2022
The Multiscale Structure of Neural Network Loss Functions: The Effect on Optimization and Origin
Local quadratic approximation has been extensively used to study the opt...
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03/30/2022
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Variational quantum algorithms stand at the forefront of simulations on ...
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02/21/2022
Accelerating Primal-dual Methods for Regularized Markov Decision Processes
Entropy regularized Markov decision processes have been widely used in r...
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02/20/2022
Analytic continuation from limited noisy Matsubara data
This note proposes a new algorithm for estimating spectral function from...
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02/15/2022
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games
In this paper, we study the problem of finding mixed Nash equilibrium fo...
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02/06/2022
Stable factorization for phase factors of quantum signal processing
This note proposes a new factorization algorithm for computing the phase...
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02/06/2022
Pole recovery from noisy data on imaginary axis
This note proposes an algorithm for identifying the poles and residues o...
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12/13/2021
How to Learn when Data Gradually Reacts to Your Model
A recent line of work has focused on training machine learning (ML) mode...
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10/25/2021
Operator Augmentation for Model-based Policy Evaluation
In model-based reinforcement learning, the transition matrix and reward ...
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10/17/2021
A Riemannian Mean Field Formulation for Two-layer Neural Networks with Batch Normalization
The training dynamics of two-layer neural networks with batch normalizat...
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10/13/2021
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
In this paper, we study the statistical limits of deep learning techniqu...
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10/05/2021
Quasi-Newton policy gradient algorithms
Policy gradient algorithms have been widely applied to reinforcement lea...
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08/03/2021
Variational Actor-Critic Algorithms
We introduce a class of variational actor-critic algorithms based on a v...
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06/09/2021
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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06/04/2021
Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees
Tree-based models underpin many modern semantic search engines and recom...
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05/31/2021
Combining resampling and reweighting for faithful stochastic optimization
Many machine learning and data science tasks require solving non-convex ...
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05/27/2021
The Sobolev Regularization Effect of Stochastic Gradient Descent
The multiplicative structure of parameters and input data in the first l...
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05/07/2021
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 high-dimensiona...
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05/06/2021
Approximate inversion of discrete Fourier integral operators
This paper introduces a factorization for the inverse of discrete Fourie...
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04/22/2021
Operator Augmentation for General Noisy Matrix Systems
In the computational sciences, one must often estimate model parameters ...
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02/15/2021
Top-k eXtreme Contextual Bandits with Arm Hierarchy
Motivated by modern applications, such as online advertisement and recom...
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02/15/2021
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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02/14/2021
Multi-Level Fine-Tuning: 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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01/18/2021
An efficient dynamical low-rank algorithm for the Boltzmann-BGK equation close to the compressible viscous flow regime
It has recently been demonstrated that dynamical low-rank algorithms can...
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12/14/2020
Achieving Adversarial Robustness Requires An Active Teacher
A new understanding of adversarial examples and adversarial robustness i...
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12/12/2020
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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10/19/2020
Operator Augmentation for Noisy Elliptic Systems
In the computational sciences, one must often estimate model parameters ...
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10/11/2020
Efficient Long-Range Convolutions for Point Clouds
The efficient treatment of long-range interactions for point clouds is a...
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09/28/2020
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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09/01/2020
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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08/28/2020
Distributed-memory ℋ-matrix Algebra I: Data Distribution and Matrix-vector Multiplication
We introduce a data distribution scheme for ℋ-matrices and a distributed...
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07/09/2020
A Simple Multiscale Method for Mean Field Games
This paper proposes a multiscale method for solving the numerical soluti...
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06/29/2020