4 citations · 8 across the 11 of their papers we have counts for
11 papers
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio, Danilo Vucetic, Zichu Liu +3
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minimizing projected Bellman error and min-ma…
A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm
Junhyung Lyle Kim, Nai-Hui Chia, Anastasios Kyrillidis
Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can a…
On the Error-Propagation of Inexact Hotelling's Deflation for Principal Component Analysis
Fangshuo Liao, Junhyung Lyle Kim, Cruz Barnum +1
Principal Component Analysis (PCA) aims to find subspaces spanned by the so-called principal components that best represent the variance in the dataset. The deflation method is a p…
Adaptive Federated Learning with Auto-Tuned Clients
Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe +1
Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients…
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis
Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis +1
The extragradient method has gained popularity due to its robust convergence properties for differentiable games. Unlike single-objective optimization, game dynamics involve comple…
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
Junhyung Lyle Kim, Mohammad Taha Toghani, César A. Uribe +1
We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix ov…
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis
Stochastic gradient descent with momentum (SGDM) is the dominant algorithm in many optimization scenarios, including convex optimization instances and non-convex neural network tra…
How much pre-training is enough to discover a good subnetwork?
Cameron R. Wolfe, Fangshuo Liao, Qihan Wang +2
Neural network pruning is useful for discovering efficient, high-performing subnetworks within pre-trained, dense network architectures. More often than not, it involves a three-st…
Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks
Junhyung Kim, Byungyoon Park, Charmgil Hong
We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) using 1-dimensional convolution kernels to learn label depend…
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Junhyung Lyle Kim, JA Lara Benitez, Mohammad Taha Toghani +3
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.…
Fast quantum state reconstruction via accelerated non-convex programming
Junhyung Lyle Kim, George Kollias, Amir Kalev +2
We propose a new quantum state reconstruction method that combines ideas from compressed sensing, non-convex optimization, and acceleration methods. The algorithm, called Momentum-…