14 citations · 92 across the 50 of their papers we have counts for
56 papers
Poisson contingency tables, Gibbs permutations, and entropic optimal transport
Danny Duan, Hanbaek Lyu, William Powell
We study random contingency tables obtained by conditioning a matrix of independent Poisson entries on its row and column sums. We identify such a table with the block counts of a…
Finding Koopman Invariant Subspaces via Personalized PageRank
Hyukpyo Hong, Qin Li, Matthew J. Colbrook +1
Selecting a finite dictionary of observables whose span is Koopman-invariant is a central challenge in data-driven Koopman operator approximation. We address this problem by exploi…
Scaling limit of Sinkhorn-rescaled Random Matrices via Stability of Static Schrödinger Bridges
Danny Duan, Hanbaek Lyu, William Powell
We analyze the asymptotic behavior and scaling limits of large random matrices rescaled via the Sinkhorn algorithm to match prescribed row and column margins. For a random matrix w…
Diffusive Scaling limit of stochastic Box-Ball systems and PushTASEP
David Keating, Minjun Kim, Eva Loeser +1
We introduce the Stochastic Box-Ball System (SBBS), a probabilistic cellular automaton that generalizes the classic Takahashi-Satsuma Box-Ball System. In SBBS, particles are transp…
Regularized Overestimated Newton
Danny Duan, Hanbaek Lyu
We propose Regularized Overestimated Newton (RON), a Newton-type method with low per-iteration cost and strong global and local convergence guarantees for smooth convex optimizatio…
Sobolev acceleration for neural networks
Jong Kwon Oh, Hanbaek Lyu, Hwijae Son
Sobolev training, which integrates target derivatives into the loss functions, has been shown to accelerate convergence and improve generalization compared to conventional tr…
Sample Complexity of Branch-length Estimation by Maximum Likelihood
David Clancy, Hanbaek Lyu, Sebastien Roch
We consider the branch-length estimation problem on a bifurcating tree: a character evolves along the edges of a binary tree according to a two-state symmetric Markov process, and…
Likelihood-Based Root State Reconstruction on a Tree: Sensitivity to Parameters and Applications
David Clancy, Hanbaek Lyu, Sebastien Roch +1
We consider a broadcasting problem on a tree where a binary digit (e.g., a spin or a nucleotide's purine/pyrimidine type) is propagated from the root to the leaves through symmetri…
Likelihood landscape of binary latent model on a tree
David Clancy, Hanbaek Lyu, Sebastien Roch
We investigate the optimization landscape of maximum likelihood estimation (MLE) for the Cavender-Farris-Neyman (CFN) model, a two-state latent tree model fundamental to statistica…
Large random matrices with given margins
Hanbaek Lyu, Sumit Mukherjee
We study large random matrices with i.i.d. entries conditioned to have prescribed row and column sums (margins), a problem connected to relative entropy minimization, Schrödinger b…
Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
Yuchen Li, Laura Balzano, Deanna Needell +1
We analyze inexact Riemannian gradient descent (RGD) where Riemannian gradients and retractions are inexactly (and cheaply) computed. Our focus is on understanding when inexact RGD…
New directions in algebraic statistics: Three challenges from 2023
Yulia Alexandr, Miles Bakenhus, Mark Curiel +11
In the last quarter of a century, algebraic statistics has established itself as an expanding field which uses multilinear algebra, commutative algebra, computational algebra, geom…
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu
We consider the problem of finding stationary points in Bilevel optimization when the lower-level problem is unconstrained and strongly convex. The problem has been extensively stu…
Four-parameter coalescing ballistic annihilation
Kimberly Affeld, Christian Dean, Matthew Junge +3
In coalescing ballistic annihilation, infinitely many particles move with fixed velocities across the real line and, upon colliding, either mutually annihilate or generate a new pa…
Stochastic optimization with arbitrary recurrent data sampling
William G. Powell, Hanbaek Lyu
For obtaining optimal first-order convergence guarantee for stochastic optimization, it is necessary to use a recurrent data sampling algorithm that samples every data point with s…
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin Interactions
Vishal Rana, Jianhao Peng, Chao Pan +4
Dictionary learning (DL) is commonly used in computational biology to tackle ubiquitous clustering problems due to its conceptual simplicity and relatively low computational comple…
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
Yuchen Li, Laura Balzano, Deanna Needell +1
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in eac…
A latent linear model for nonlinear coupled oscillators on graphs
Agam Goyal, Zhaoxing Wu, Richard P. Yim +3
A system of coupled oscillators on an arbitrary graph is locally driven by the tendency to mutual synchronization between nearby oscillators, but can and often exhibit nonlinear be…
Exponentially Convergent Algorithms for Supervised Matrix Factorization
Joowon Lee, Hanbaek Lyu, Weixin Yao
Supervised matrix factorization (SMF) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a prio…
Supervised low-rank semi-nonnegative matrix factorization with frequency regularization for forecasting spatio-temporal data
Keunsu Kim, Hanbaek Lyu, Jinsu Kim +1
We propose a novel methodology for forecasting spatio-temporal data using supervised semi-nonnegative matrix factorization (SSNMF) with frequency regularization. Matrix factorizati…
Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning
Dohyun Kwon, Hanbaek Lyu
We consider the block coordinate descent methods of Gauss-Seidel type with proximal regularization (BCD-PR), which is a classical method of minimizing general nonconvex objectives…
Diffusion-limited annihilating-coalescing systems
Sungwon Ahn, Matthew Junge, Hanbaek Lyu +3
We study a family of interacting particle systems with annihilating and coalescing reactions. Two types of particles are interspersed throughout a transitive unimodular graph. Both…
Supervised Dictionary Learning with Auxiliary Covariates
Joowon Lee, Hanbaek Lyu, Weixin Yao
Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a prior…
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data
Ahmet Alacaoglu, Hanbaek Lyu
We focus on analyzing the classical stochastic projected gradient methods under a general dependent data sampling scheme for constrained smooth nonconvex optimization. We show the…