101 citations · 270 across the 21 of their papers we have counts for
21 papers
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen +6
Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) h…
An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning
Yihua Zhang, Prashant Khanduri, Ioannis Tsaknakis +3
Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, B…
Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
Zhanpeng Zeng, Cole Hawkins, Mingyi Hong +4
Transformers are central in modern natural language processing and computer vision applications. Despite recent works devoted to reducing the quadratic cost of such models (as a fu…
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data
Xinwei Zhang, Mingyi Hong, Jie Chen
Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the same set of samples th…
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Yihua Zhang, Pranay Sharma, Parikshit Ram +3
Invariant risk minimization (IRM) has received increasing attention as a way to acquire environment-agnostic data representations and predictions, and as a principled solution for…
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Siliang Zeng, Chenliang Li, Alfredo Garcia +1
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demo…
On the Robustness of deep learning-based MRI Reconstruction to image transformations
Jinghan Jia, Mingyi Hong, Yimeng Zhang +2
Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities…
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Siliang Zeng, Chenliang Li, Alfredo Garcia +1
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented by an…
Advancing Model Pruning via Bi-level Optimization
Yihua Zhang, Yuguang Yao, Parikshit Ram +5
The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lotte…
A Framework for Understanding Model Extraction Attack and Defense
Xun Xian, Mingyi Hong, Jie Ding
The privacy of machine learning models has become a significant concern in many emerging Machine-Learning-as-a-Service applications, where prediction services based on well-trained…
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning
Ioannis Tsaknakis, Bhavya Kailkhura, Sijia Liu +4
Using deep learning (DL) to accelerate and/or improve scientific workflows can yield discoveries that are otherwise impossible. Unfortunately, DL models have yielded limited succes…
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Gaoyuan Zhang, Songtao Lu, Yihua Zhang +7
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against…
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang, Yuguang Yao, Jinghan Jia +4
The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustif…
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
Bingqing Song, Haoran Sun, Wenqiang Pu +2
Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key task…
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
Yihua Zhang, Guanhua Zhang, Prashant Khanduri +3
Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks. It is built on min-max optimization (…
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Xinwei Zhang, Xiangyi Chen, Mingyi Hong +2
Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion…
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
Sagar Shrestha, Xiao Fu, Mingyi Hong
The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an il…
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup
Han Shen, Kaiqing Zhang, Mingyi Hong +1
Asynchronous and parallel implementation of standard reinforcement learning (RL) algorithms is a key enabler of the tremendous success of modern RL. Among many asynchronous RL algo…
Flow-based Intrinsic Curiosity Module
Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong +1
In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module…
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho +2
Exploration bonus derived from the novelty of the states in an environment has become a popular approach to motivate exploration for deep reinforcement learning agents in the past…