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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...
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08/26/2023
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context
The integration of external personalized context information into docume...
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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...
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03/05/2023
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Recently, min-max optimization problems have received increasing attenti...
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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...
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12/19/2022
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
Many real-world problems not only have complicated nonconvex functional ...
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10/03/2022
ASGNN: Graph Neural Networks with Adaptive Structure
The graph neural network (GNN) models have presented impressive achievem...
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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...
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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...
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06/13/2022
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Current deep neural networks (DNNs) are vulnerable to adversarial attack...
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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...
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11/04/2021
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks
In this paper, we investigate the performance of a practical aggregated ...
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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) ...
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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...
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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...
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04/21/2021
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Large-scale distributed training of Deep Neural Networks (DNNs) on state...
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03/31/2021
Robust Beamforming Design for Covert Communications
In this paper, we consider a common unicast beamforming network where Al...
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03/02/2021
Adversarial Examples for Unsupervised Machine Learning Models
Adversarial examples causing evasive predictions are widely used to eval...
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02/08/2021
Federated Acoustic Modeling For Automatic Speech Recognition
Data privacy and protection is a crucial issue for any automatic speech ...
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09/29/2020
Learned Fine-Tuner for Incongruous Few-Shot Learning
Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
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01/15/2020
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization
We propose a new randomized Bregman (block) coordinate descent (RBCD) me...
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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...
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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 ...
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12/04/2019
Learn Electronic Health Records by Fully Decentralized Federated Learning
Federated learning opens a number of research opportunities due to its h...
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10/22/2019
Online Meta-Learning on Non-convex Setting
The online meta-learning framework is designed for the continual lifelon...
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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...
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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...
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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...
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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...
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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 ...
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09/18/2018
Power Market Price Forecasting via Deep Learning
A study on power market price forecasting by deep learning is presented....
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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...
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03/24/2017
