66 citations · 283 across the 24 of their papers we have counts for
28 papers
Peppy: An AI-Assisted Workflow for Tight Convergence Analysis of Optimization Algorithms
Jaewook J. Suh, TaeHo Yoon, Edward Duc Hien Nguyen +2
This paper presents Peppy, an AI-assisted workflow for discovering tight, analytic convergence proofs for first-order optimization algorithms. Generic approaches to using LLMs to c…
Why Ghost Outputs Teach: A Kernel-Based Understanding of Subliminal Learning
Zhe Li, Bicheng Ying, Chaosheng Dong +1
Subliminal Learning (SL) is a recently identified phenomenon in which a student model acquires downstream task capabilities by matching seemingly unrelated auxiliary outputs from a…
Precision quantum simulation of magnon spectra and interactions
Trond I. Andersen, Nikita Astrakhantsev, Jeronimo Martinez +329
Quantum simulation promises to advance materials discovery by accurately simulating complex states of matter, their microscopic excitations, and macroscopic response functions. The…
Reinforcement Learning Control of Quantum Error Correction
Volodymyr Sivak, Alexis Morvan, Michael Broughton +296
Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. Its prerequisite is that errors must remain sufficiently rare, which…
Thermalization and Criticality on an Analog-Digital Quantum Simulator
Trond I. Andersen, Nikita Astrakhantsev, Amir H. Karamlou +224
Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requi…
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Zhe Li, Bicheng Ying, Zidong Liu +2
Federated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communicati…
Resisting high-energy impact events through gap engineering in superconducting qubit arrays
Matt McEwen, Kevin C. Miao, Juan Atalaya +24
Quantum error correction (QEC) provides a practical path to fault-tolerant quantum computing through scaling to large qubit numbers, assuming that physical errors are sufficiently…
Sparse factorization of the square all-ones matrix of arbitrary order
Xin Jiang, Edward Duc Hien Nguyen, César A. Uribe +1
In this paper, we study sparse factorization of the (scaled) square all-ones matrix of arbitrary order. We introduce the concept of hierarchically banded matrices and propose t…
On graphs with finite-time consensus and their use in gradient tracking
Edward Duc Hien Nguyen, Xin Jiang, Bicheng Ying +1
This paper studies sequences of graphs satisfying the finite-time consensus property (i.e., iterating through such a finite sequence is equivalent to performing global or exact ave…
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm
Lisang Ding, Kexin Jin, Bicheng Ying +2
Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Ra…
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Bicheng Ying, Kun Yuan, Hanbin Hu +2
Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On larg…
Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying, Kun Yuan, Yiming Chen +3
Decentralized SGD is an emerging training method for deep learning known for its much less (thus faster) communication per iteration, which relaxes the averaging step in parallel S…
On the Influence of Bias-Correction on Distributed Stochastic Optimization
Kun Yuan, Sulaiman A. Alghunaim, Bicheng Ying +1
Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed {\em deterministic} optimization prob…
Dynamic Average Diffusion with randomized Coordinate Updates
Bicheng Ying, Kun Yuan, Ali H. Sayed
This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During…
Supervised Learning Under Distributed Features
Bicheng Ying, Kun Yuan, Ali H. Sayed
This work studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in…
Stochastic Learning under Random Reshuffling with Constant Step-sizes
Bicheng Ying, Kun Yuan, Stefan Vlaski +1
In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementa…
Belief Control Strategies for Interactions over Weakly-Connected Graphs
Hawraa Salami, Bicheng Ying, Ali H. Sayed
In diffusion social learning over weakly-connected graphs, it has been shown recently that influential agents shape the beliefs of non-influential agents. This paper analyzes this…
Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling
Kun Yuan, Bicheng Ying, Jiageng Liu +1
A new amortized variance-reduced gradient (AVRG) algorithm was developed in \cite{ying2017convergence}, which has constant storage requirement in comparison to SAGA and balanced gr…
Variance-Reduced Stochastic Learning under Random Reshuffling
Bicheng Ying, Kun Yuan, Ali H. Sayed
Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properti…
Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Case
Bicheng Ying, Ali H. Sayed
The analysis in Part I revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization when gradient noise is present. These algorithm…
Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis
Kun Yuan, Bicheng Ying, Xiaochuan Zhao +1
Part I of this work [2] developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algo…
Exact Diffusion for Distributed Optimization and Learning --- Part I: Algorithm Development
Kun Yuan, Bicheng Ying, Xiaochuan Zhao +1
This work develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion st…
Social Learning over Weakly-Connected Graphs
Hawraa Salami, Bicheng Ying, Ali H. Sayed
In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents reg…
Coordinate-Descent Diffusion Learning by Networked Agents
Chengcheng Wang, Yonggang Zhang, Bicheng Ying +1
This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each…