199 citations · 1.3k across the 122 of their papers we have counts for
126 papers
Heisenberg-limited Hamiltonian learning for interacting bosons
Haoya Li, Yu Tong, Hongkang Ni +2
We develop a protocol for learning a class of interacting bosonic Hamiltonians from dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded-degree gra…
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality
Jose Blanchet, Haoxuan Chen, Yiping Lu +1
This paper studies the use of a machine learning-based estimator as a control variate for mitigating the variance of Monte Carlo sampling. Specifically, we seek to uncover the key…
Computing Free Convolutions via Contour Integrals
Alice Cortinovis, Lexing Ying
This work proposes algorithms for computing additive and multiplicative free convolutions of two given measures. We consider measures with compact support whose free convolution re…
A note on spike localization for line spectrum estimation
Haoya Li, Hongkang Ni, Lexing Ying
This note considers the problem of approximating the locations of dominant spikes for a probability measure from noisy spectrum measurements under the condition of residue signal,…
On adaptive low-depth quantum algorithms for robust multiple-phase estimation
Haoya Li, Hongkang Ni, Lexing Ying
This paper is an algorithmic study of quantum phase estimation with multiple eigenvalues. We present robust multiple-phase estimation (RMPE) algorithms with Heisenberg-limited scal…
On low-depth algorithms for quantum phase estimation
Hongkang Ni, Haoya Li, Lexing Ying
Quantum phase estimation is one of the critical building blocks of quantum computing. For early fault-tolerant quantum devices, it is desirable for a quantum phase estimation algor…
On efficient quantum block encoding of pseudo-differential operators
Haoya Li, Hongkang Ni, Lexing Ying
Block encoding lies at the core of many existing quantum algorithms. Meanwhile, efficient and explicit block encodings of dense operators are commonly acknowledged as a challenging…
High-dimensional density estimation with tensorizing flow
Yinuo Ren, Hongli Zhao, Yuehaw Khoo +1
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data. The method is based on tensor-train and flow-based gene…
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls
Yiping Lu, Jiajin Li, Lexing Ying +1
The optimal design of experiments typically involves solving an NP-hard combinatorial optimization problem. In this paper, we aim to develop a globally convergent and practically e…
Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries
Chao Ma, Lexing Ying
In this paper, we show that structures similar to self-attention are natural to learn many sequence-to-sequence problems from the perspective of symmetry. Inspired by language proc…
Continuous-in-time Limit for Bayesian Bandits
Yuhua Zhu, Zachary Izzo, Lexing Ying
This paper revisits the bandit problem in the Bayesian setting. The Bayesian approach formulates the bandit problem as an optimization problem, and the goal is to find the optimal…
Bayesian regularization of empirical MDPs
Samarth Gupta, Daniel N. Hill, Lexing Ying +1
In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy…
Correcting Convexity Bias in Function and Functional Estimate
Chao Ma, Lexing Ying
A general framework with a series of different methods is proposed to improve the estimate of convex function (or functional) values when only noisy observations of the true input…
Generative Modeling via Tree Tensor Network States
Xun Tang, Yoonhaeng Hur, Yuehaw Khoo +1
In this paper, we present a density estimation framework based on tree tensor-network states. The proposed method consists of determining the tree topology with Chow-Liu algorithm,…
Minimax Optimal Kernel Operator Learning via Multilevel Training
Jikai Jin, Yiping Lu, Jose Blanchet +1
Learning mappings between infinite-dimensional function spaces has achieved empirical success in many disciplines of machine learning, including generative modeling, functional dat…
Annealed importance sampling for Ising models with mixed boundary conditions
Lexing Ying
This note introduces a method for sampling Ising models with mixed boundary conditions. As an application of annealed importance sampling and the Swendsen-Wang algorithm, the metho…
Double Flip Move for Ising Models with Mixed Boundary Conditions
Lexing Ying
This note introduces the double flip move for accelerating the Swendsen-Wang algorithm for Ising models with mixed boundary conditions below the critical temperature. The double fl…
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
Yiping Lu, Jose Blanchet, Lexing Ying
In this paper, we study the statistical limits in terms of Sobolev norms of gradient descent for solving inverse problem from randomly sampled noisy observations using a general cl…
Beyond the Quadratic Approximation: the Multiscale Structure of Neural Network Loss Landscapes
Chao Ma, Daniel Kunin, Lei Wu +1
A quadratic approximation of neural network loss landscapes has been extensively used to study the optimization process of these networks. Though, it usually holds in a very small…
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Jiahao Yao, Haoya Li, Marin Bukov +2
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only…
Analytic continuation from limited noisy Matsubara data
Lexing Ying
This note proposes a new algorithm for estimating spectral function from limited noisy Matsubara data. We consider both the molecule and condensed matter cases. In each case, the a…
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games
Chao Ma, Lexing Ying
In this paper, we study the problem of finding mixed Nash equilibrium for mean-field two-player zero-sum games. Solving this problem requires optimizing over two probability distri…
Pole recovery from noisy data on imaginary axis
Lexing Ying
This note proposes an algorithm for identifying the poles and residues of a meromorphic function from its noisy values on the imaginary axis. The algorithm uses Möbius transform an…
Accelerating Primal-dual Methods for Regularized Markov Decision Processes
Haoya Li, Hsiang-fu Yu, Lexing Ying +1
Entropy regularized Markov decision processes have been widely used in reinforcement learning. This paper is concerned with the primal-dual formulation of the entropy regularized p…