b'Songtao Lu'

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Featured Co-authors

Wei Zhang
393 publications
Meng Wang
217 publications
Pin-Yu Chen
183 publications
Xin Zhang
158 publications
Fan Zhang
151 publications
Han Zhang
115 publications
Sijia Liu
112 publications
Jia Liu
108 publications
Shuai Zhang
100 publications
Hang Li
95 publications
Han Yu
94 publications

research

∙ 08/29/2023

FedLogic: Interpretable Federated Multi-Domain Chain-of-Thought Prompt Selection for Large Language Models

Leveraging “chain-of-thought (CoT)” reasoning to elicit rapid and precis...

0 Pengwei Xing, et al. ∙

research

∙ 08/26/2023

How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context

The integration of external personalized context information into docume...

0 Hui Wan, et al. ∙

research

∙ 06/04/2023

A Generalized Alternating Method for Bilevel Optimization under the Polyak-Łojasiewicz Condition

Bilevel optimization has recently regained interest owing to its applica...

0 Quan Xiao, et al. ∙

research

∙ 03/05/2023

PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities

Recently, min-max optimization problems have received increasing attenti...

0 Zhuqing Liu, et al. ∙

research

∙ 02/06/2023

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

Due to the significant computational challenge of training large-scale g...

0 Shuai Zhang, et al. ∙

research

∙ 12/19/2022

Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization

Many real-world problems not only have complicated nonconvex functional ...

0 Zichong Li, et al. ∙

research

∙ 10/03/2022

ASGNN: Graph Neural Networks with Adaptive Structure

The graph neural network (GNN) models have presented impressive achievem...

0 Zepeng Zhang, et al. ∙

research

∙ 07/27/2022

INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks

In recent years, decentralized bilevel optimization problems have receiv...

0 Zhuqing Liu, et al. ∙

research

∙ 07/12/2022

A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization

Nonconvex constrained optimization problems can be used to model a numbe...

0 Songtao Lu, et al. ∙

research

∙ 06/13/2022

Distributed Adversarial Training to Robustify Deep Neural Networks at Scale

Current deep neural networks (DNNs) are vulnerable to adversarial attack...

15 Gaoyuan Zhang, et al. ∙

research

∙ 03/03/2022

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning

This paper is the first to propose a generic min-max bilevel multi-objec...

0 Alex Gu, et al. ∙

research

∙ 11/04/2021

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated ...

0 Shuai Ma, et al. ∙

research

∙ 06/14/2021

Latent Correlation-Based Multiview Learning and Self-Supervision: A Unifying Perspective

Multiple views of data, both naturally acquired (e.g., image and audio) ...

0 Qi Lyu, et al. ∙

research

∙ 06/05/2021

Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition

Deep neural networks have been shown as a class of useful tools for addr...

0 Yihong Dong, et al. ∙

research

∙ 05/12/2021

An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization

XGBoost is one of the most widely used machine learning models in the in...

0 Lunchen Xie, et al. ∙

research

∙ 04/21/2021

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training

Large-scale distributed training of Deep Neural Networks (DNNs) on state...

1 Chia-Yu Chen, et al. ∙

research

∙ 03/31/2021

Robust Beamforming Design for Covert Communications

In this paper, we consider a common unicast beamforming network where Al...

0 Shuai Ma, et al. ∙

research

∙ 03/02/2021

Adversarial Examples for Unsupervised Machine Learning Models

Adversarial examples causing evasive predictions are widely used to eval...

74 Chia-Yi Hsu, et al. ∙

research

∙ 02/08/2021

Federated Acoustic Modeling For Automatic Speech Recognition

Data privacy and protection is a crucial issue for any automatic speech ...

0 Xiaodong Cui, et al. ∙

research

∙ 09/29/2020

Learned Fine-Tuner for Incongruous Few-Shot Learning

Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...

0 Pu Zhao, et al. ∙

research

∙ 01/15/2020

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization

We propose a new randomized Bregman (block) coordinate descent (RBCD) me...

0 Tianxiang Gao, et al. ∙

research

∙ 01/14/2020

Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond

Distributed learning has become a critical enabler of the massively conn...

0 Tsung-Hui Chang, et al. ∙

research

∙ 12/16/2019

Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems

In the applications of signal processing and data analytics, there is a ...

0 Tianxiang Gao, et al. ∙

research

∙ 12/04/2019

Learn Electronic Health Records by Fully Decentralized Federated Learning

Federated learning opens a number of research opportunities due to its h...

0 Songtao Lu, et al. ∙

research

∙ 10/22/2019

Online Meta-Learning on Non-convex Setting

The online meta-learning framework is designed for the continual lifelon...

0 Zhenxun Zhuang, et al. ∙

research

∙ 10/13/2019

Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach

Many modern large-scale machine learning problems benefit from decentral...

0 Haoran Sun, et al. ∙

research

∙ 09/30/2019

Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML

In this paper, we study the problem of constrained robust (min-max) opti...

0 Sijia Liu, et al. ∙

research

∙ 07/09/2019

SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems

This paper proposes low-complexity algorithms for finding approximate se...

0 Songtao Lu, et al. ∙

research

∙ 03/08/2019

Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

In this paper, we investigate deep learning (DL)-enabled signal demodula...

8 Hongmei Wang, et al. ∙

research

∙ 02/21/2019

Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

The min-max problem, also known as the saddle point problem, is a class ...

1 Songtao Lu, et al. ∙

research

∙ 09/18/2018

Power Market Price Forecasting via Deep Learning

A study on power market price forecasting by deep learning is presented....

0 Yongli Zhu, et al. ∙

research

∙ 02/28/2018

On the Sublinear Convergence of Randomly Perturbed Alternating Gradient Descent to Second Order Stationary Solutions

The alternating gradient descent (AGD) is a simple but popular algorithm...

0 Songtao Lu, et al. ∙

research

∙ 03/24/2017

A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality

Symmetric nonnegative matrix factorization (SymNMF) has important applic...

0 Songtao Lu, et al. ∙

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