research
∙
08/21/2023
One-Time Compilation of Device-Level Instructions for Quantum Subroutines
A large class of problems in the current era of quantum devices involve ...
share
research
∙
08/16/2023
Developing a Conceptual Tribal Crash Safety Dashboard: Data-Driven Strategies for Identifying High-Risk Areas and Enhancing Tribal Safety Programs
Tribal lands in the United States have consistently exhibited higher cra...
research
∙
06/27/2023
MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning
Fine-tuning large-scale pre-trained language models has been demonstrate...
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...
research
∙
05/25/2023
Towards Automatic Neural Architecture Search within General Super-Networks
Existing neural architecture search (NAS) methods typically rely on pre-...
research
∙
04/17/2023
MoDA: Modeling Deformable 3D Objects from Casual Videos
In this paper, we focus on the challenges of modeling deformable 3D obje...
research
∙
03/13/2023
OTOV2: Automatic, Generic, User-Friendly
The existing model compression methods via structured pruning typically ...
research
∙
02/10/2023
On Penalty-based Bilevel Gradient Descent Method
Bilevel optimization enjoys a wide range of applications in hyper-parame...
research
∙
12/17/2022
FSCNN: A Fast Sparse Convolution Neural Network Inference System
Convolution neural networks (CNNs) have achieved remarkable success, but...
research
∙
11/14/2022
Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization
Stochastic bilevel optimization, which captures the inherent nested stru...
research
∙
10/25/2022
Modular Software for Real-Time Quantum Control Systems
Real-time control software and hardware is essential for operating quant...
research
∙
10/23/2022
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach
Machine learning problems with multiple objective functions appear eithe...
research
∙
09/25/2022
On the Stability Analysis of Open Federated Learning Systems
We consider the open federated learning (FL) systems, where clients may ...
research
∙
09/09/2022
Sparsity-guided Network Design for Frame Interpolation
DNN-based frame interpolation, which generates intermediate frames from ...
research
∙
07/01/2022
COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning
Platooning and coordination are two implementation strategies that are f...
research
∙
06/24/2022
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach
As a strategy to reduce travel delay and enhance energy efficiency, plat...
research
∙
06/21/2022
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences
Stochastic approximation (SA) with multiple coupled sequences has found ...
research
∙
06/14/2022
Lazy Queries Can Reduce Variance in Zeroth-order Optimization
A major challenge of applying zeroth-order (ZO) methods is the high quer...
research
∙
06/09/2022
Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rules
This work focuses on decentralized stochastic optimization in the presen...
research
∙
06/08/2022
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Model-agnostic meta learning (MAML) is currently one of the dominating a...
research
∙
05/26/2022
AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks
Benford's law describes the distribution of the first digit of numbers a...
research
∙
04/05/2022
Federated Cross Learning for Medical Image Segmentation
Federated learning (FL) can collaboratively train deep learning models u...
research
∙
03/06/2022
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?
Meta learning aims at learning a model that can quickly adapt to unseen ...
research
∙
01/07/2022
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction
Crime prediction is crucial for public safety and resource optimization,...
research
∙
12/06/2021
Dense and well-connected subgraph detection in dual networks
Dense subgraph discovery is a fundamental problem in graph mining with a...
research
∙
10/26/2021
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Recent studies show that private training data can be leaked through the...
research
∙
10/11/2021
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees
Multi-agent reinforcement learning (MARL) has attracted much research at...
research
∙
07/15/2021
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural...
research
∙
06/25/2021
Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems
Stochastic nested optimization, including stochastic compositional, min-...
research
∙
02/09/2021
A Single-Timescale Stochastic Bilevel Optimization Method
Stochastic bilevel optimization generalizes the classic stochastic optim...
research
∙
12/31/2020
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup
Asynchronous and parallel implementation of standard reinforcement learn...
research
∙
12/31/2020
CADA: Communication-Adaptive Distributed Adam
Stochastic gradient descent (SGD) has taken the stage as the primary wor...
research
∙
12/22/2020
Hybrid Federated Learning: Algorithms and Implementation
Federated learning (FL) is a recently proposed distributed machine learn...
research
∙
11/24/2020
Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road
Early risk diagnosis and driving anomaly detection from vehicle stream a...
research
∙
11/10/2020
Neural Network Compression Via Sparse Optimization
The compression of deep neural networks (DNNs) to reduce inference cost ...
research
∙
10/25/2020
A Simple Spectral Failure Mode for Graph Convolutional Networks
We present a simple generative model in which spectral graph embedding f...
research
∙
08/25/2020
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Stochastic compositional optimization generalizes classic (non-compositi...
research
∙
07/21/2020
Recurrent Exposure Generation for Low-Light Face Detection
Face detection from low-light images is challenging due to limited photo...
research
∙
07/12/2020
VAFL: a Method of Vertical Asynchronous Federated Learning
Horizontal Federated learning (FL) handles multi-client data that share ...
research
∙
06/17/2020
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
This work investigates fault-resilient federated learning when the data ...
research
∙
04/07/2020
Orthant Based Proximal Stochastic Gradient Method for ℓ_1-Regularized Optimization
Sparsity-inducing regularization problems are ubiquitous in machine lear...
research
∙
02/26/2020
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
This paper targets solving distributed machine learning problems such as...
research
∙
02/20/2020
Adaptive Temporal Difference Learning with Linear Function Approximation
This paper revisits the celebrated temporal difference (TD) learning alg...
research
∙
12/29/2019
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
This paper deals with distributed finite-sum optimization for learning o...
research
∙
09/23/2019
Decentralized Markov Chain Gradient Descent
Decentralized stochastic gradient method emerges as a promising solution...
research
∙
09/17/2019
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
The present paper develops a novel aggregated gradient approach for dist...
research
∙
09/09/2019
Communication-Censored Distributed Stochastic Gradient Descent
This paper develops a communication-efficient algorithm to solve the sto...
research
∙
07/27/2019
Generative Adversarial Network for Handwritten Text
Generative adversarial networks (GANs) has proven hugely successful in v...
research
∙
04/17/2019
Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries
In the densest subgraph problem, given a weighted undirected graph G(V,E...
research
∙
12/07/2018