Anthony Man-Cho So

research

∙ 05/24/2023

ReSync: Riemannian Subgradient-based Robust Rotation Synchronization

This work presents ReSync, a Riemannian subgradient-based algorithm for ...

0 Huikang Liu, et al. ∙

research

∙ 05/23/2023

Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity

The subgradient method is one of the most fundamental algorithmic scheme...

0 Xiao Li, et al. ∙

research

∙ 05/02/2023

LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees

Graph learning from signals is a core task in Graph Signal Processing (G...

0 Shangyuan Liu, et al. ∙

research

∙ 04/08/2023

Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder

The sequence-to-sequence (seq2seq) task aims at generating the target se...

0 Zihao Fu, et al. ∙

research

∙ 03/31/2023

Decentralized Weakly Convex Optimization Over the Stiefel Manifold

We focus on a class of non-smooth optimization problems over the Stiefel...

0 Jinxin Wang, et al. ∙

research

∙ 03/12/2023

A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data

In this work, we present the Bregman Alternating Projected Gradient (BAP...

0 Jiajin Li, et al. ∙

research

∙ 02/23/2023

Testing Stationarity Concepts for ReLU Networks: Hardness, Regularity, and Robust Algorithms

We study the computational problem of the stationarity test for the empi...

0 Lai Tian, et al. ∙

research

∙ 02/09/2023

Outlier-Robust Gromov Wasserstein for Graph Data

Gromov Wasserstein (GW) distance is a powerful tool for comparing and al...

0 Lemin Kong, et al. ∙

research

∙ 01/24/2023

A Stability Analysis of Fine-Tuning a Pre-Trained Model

Fine-tuning a pre-trained model (such as BERT, ALBERT, RoBERTa, T5, GPT,...

0 Zihao Fu, et al. ∙

research

∙ 12/26/2022

Doubly Smoothed GDA: Global Convergent Algorithm for Constrained Nonconvex-Nonconcave Minimax Optimization

Nonconvex-nonconcave minimax optimization has been the focus of intense ...

0 Taoli Zheng, et al. ∙

research

∙ 11/28/2022

On the Effectiveness of Parameter-Efficient Fine-Tuning

Fine-tuning pre-trained models has been ubiquitously proven to be effect...

0 Zihao Fu, et al. ∙

research

∙ 09/22/2022

Nonsmooth Composite Nonconvex-Concave Minimax Optimization

Nonconvex-concave minimax optimization has received intense interest in ...

0 Jiajin Li, et al. ∙

research

∙ 07/15/2022

Riemannian Natural Gradient Methods

This paper studies large-scale optimization problems on Riemannian manif...

0 Jiang Hu, et al. ∙

research

∙ 06/11/2022

Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering

The K-subspaces (KSS) method is a generalization of the K-means method f...

0 Peng Wang, et al. ∙

research

∙ 05/17/2022

Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Learning

In this paper, we study the design and analysis of a class of efficient ...

0 Jiajin Li, et al. ∙

research

∙ 02/22/2022

Exact Community Recovery over Signed Graphs

Signed graphs encode similarity and dissimilarity relationships among di...

0 Xiaolu Wang, et al. ∙

research

∙ 01/19/2022

Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part I

In this work, we investigate stochastic quasi-Newton methods for minimiz...

0 Jiaojiao Zhang, et al. ∙

research

∙ 12/28/2021

Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method

This paper proposes a Generalized Power Method (GPM) to tackle the probl...

0 Sijin Chen, et al. ∙

research

∙ 12/13/2021

Orthogonal Group Synchronization with Incomplete Measurements: Error Bounds and Linear Convergence of the Generalized Power Method

Group synchronization refers to estimating a collection of group element...

0 Linglingzhi Zhu, et al. ∙

research

∙ 09/30/2021

Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization

We show that stochastic acceleration can be achieved under the perturbed...

0 Kaiwen Zhou, et al. ∙

research

∙ 05/25/2021

Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums

The problem of finding near-stationary points in convex optimization has...

0 Kaiwen Zhou, et al. ∙

research

∙ 05/12/2021

Learning Graphs from Smooth Signals under Moment Uncertainty

We consider the problem of inferring the graph structure from a given se...

0 Xiaolu Wang, et al. ∙

research

∙ 03/18/2021

Probabilistic Simplex Component Analysis

This study presents PRISM, a probabilistic simplex component analysis ap...

0 Ruiyuan Wu, et al. ∙

research

∙ 01/18/2021

Quartic Perturbation-based Outage-constrained Robust Design in Two-hop One-way Relay Networks

In this work, we study a classic robust design problem in two-hop one-wa...

0 Sissi Xiaoxiao Wu, et al. ∙

research

∙ 12/29/2020

A Theoretical Analysis of the Repetition Problem in Text Generation

Text generation tasks, including translation, summarization, language mo...

0 Zihao Fu, et al. ∙

research

∙ 10/24/2020

Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine

Wasserstein Distributionally Robust Optimization (DRO) is concerned with...

0 Jiajin Li, et al. ∙

research

∙ 09/16/2020

A Unified Approach to Synchronization Problems over Subgroups of the Orthogonal Group

Given a group 𝒢, the problem of synchronization over the group 𝒢 is a co...

0 Huikang Liu, et al. ∙

research

∙ 06/29/2020

Non-Convex Exact Community Recovery in Stochastic Block Model

Learning community structures in graphs that are randomly generated by s...

9 Peng Wang, et al. ∙

research

∙ 06/26/2020

Understanding Notions of Stationarity in Non-Smooth Optimization

Many contemporary applications in signal processing and machine learning...

0 Jiajin Li, et al. ∙

research

∙ 05/25/2020

Boosting First-order Methods by Shifting Objective: New Schemes with Faster Worst Case Rates

We propose a new methodology to design first-order methods for unconstra...

20 Kaiwen Zhou, et al. ∙

research

∙ 05/05/2020

Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning

We consider the problem of maximizing the ℓ_1 norm of a linear map over ...

0 Shixiang Chen, et al. ∙

research

∙ 11/12/2019

Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods

Nonsmooth Riemannian optimization is a still under explored subfield of ...

0 Xiao Li, et al. ∙

research

∙ 10/28/2019

A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression

Wasserstein distance-based distributionally robust optimization (DRO) ha...

24 Jiajin Li, et al. ∙

research

∙ 07/26/2019

Incremental Methods for Weakly Convex Optimization

We consider incremental algorithms for solving weakly convex optimizatio...

0 Xiao Li, et al. ∙

research

∙ 07/02/2019

Voting-Based Multi-Agent Reinforcement Learning

The recent success of single-agent reinforcement learning (RL) encourage...

0 Yue Xu, et al. ∙

research

∙ 03/12/2019

An Efficient Augmented Lagrangian Based Method for Constrained Lasso

Variable selection is one of the most important tasks in statistics and ...

0 Zengde Deng, et al. ∙

research

∙ 11/02/2018

Proximal Gradient Method for Manifold Optimization

This paper considers manifold optimization problems with nonsmooth and n...

0 Shixiang Chen, et al. ∙

research

∙ 10/07/2018

A Framework for One-Bit and Constant-Envelope Precoding over Multiuser Massive MISO Channels

Consider the following problem: A multi-antenna base station (BS) sends ...

0 Mingjie Shao, et al. ∙

research

∙ 09/24/2018

Nonconvex Robust Low-rank Matrix Recovery

In this paper we study the problem of recovering a low-rank matrix from ...

0 Xiao Li, et al. ∙

research

∙ 04/23/2018

Minimum Symbol Error Rate-Based Constant Envelope Precoding for Multiuser Massive MISO Downlink

This paper considers the problem of constant envelope (CE) precoder desi...

0 Mingjie Shao, et al. ∙

research

∙ 05/31/2016

Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis

Generalized canonical correlation analysis (GCCA) aims at finding latent...

0 Xiao Fu, et al. ∙

research

∙ 12/11/2015

A Unified Approach to Error Bounds for Structured Convex Optimization Problems

Error bounds, which refer to inequalities that bound the distance of vec...

0 Zirui Zhou, et al. ∙

Anthony Man-Cho So

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