Search Results for author:
Found 23 papers, 6 papers with code
Learning-augmented Online Minimization of Age of Information and Transmission Costs
To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee.
Learning-augmented Online Algorithm for Two-level Ski-rental Problem
In this paper, we study the two-level ski-rental problem, where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i. e., rent), buying individual items (i. e., single purchase), and buying all the items (i. e., combo purchase).
Understanding the Role of Feedback in Online Learning with Switching Costs
To design algorithms that can achieve the minimax regret, it is instructive to consider a more general setting where the learner has a budget of $B$ total observations.
(Private) Kernelized Bandits with Distributed Biased Feedback
This problem is motivated by several real-world applications (such as dynamic pricing, cellular network configuration, and policy making), where users from a large population contribute to the reward of the action chosen by a central entity, but it is difficult to collect feedback from all users.
FSCNN: A Fast Sparse Convolution Neural Network Inference System
Convolution neural networks (CNNs) have achieved remarkable success, but typically accompany high computation cost and numerous redundant weight parameters.
Minute ventilation measurement using Plethysmographic Imaging and lighting parameters
Respiratory Measurements such as minute ventilation can be used in correlation with other physiological measurements such as heart rate and heart rate variability for remote monitoring of health and detecting symptoms of such breathing related disorders.
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable.
Differentially Private Linear Bandits with Partial Distributed Feedback
To tackle this problem, we consider differentially private distributed linear bandits, where only a subset of users from the population are selected (called clients) to participate in the learning process and the central server learns the global model from such partial feedback by iteratively aggregating these clients' local feedback in a differentially private fashion.
A Closer Look at Branch Classifiers of Multi-exit Architectures
Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks.
Multi-Scale Memory-Based Video Deblurring
Video deblurring has achieved remarkable progress thanks to the success of deep neural networks.
Ranked #3 on
Analog Video Restoration
on TAPE
On Kernelized Multi-Armed Bandits with Constraints
Our ultimate goal is to study how to utilize the nature of soft constraints to attain a finer complexity-regret-constraint trade-off in the kernelized bandit setting.
$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar
Specifically, we propose a novel \textit{Frequency Component Detection} method to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector's observations at multiple anchor points.
Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach
Considering the achieved training accuracy of the global model as the utility of the selected workers, which is typically a monotone submodular function, we formulate the worker selection problem as a new multi-round monotone submodular maximization problem with cardinality and fairness constraints.
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices.
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e. g., feature selection, compressed sensing and model compression.
Robust Sequence Submodular Maximization
Compared to robust submodular maximization for set function, new challenges arise when sequence functions are concerned.
Neural Network Compression Via Sparse Optimization
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications.
Orthant Based Proximal Stochastic Gradient Method for $\ell_1$-Regularized Optimization
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression.
Waiting but not Aging: Optimizing Information Freshness Under the Pull Model
Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side.
Generative Adversarial Network for Handwritten Text
The main features of the new framework include: (i) A discriminator consists of an integrated CNN-Long-Short-Term- Memory (LSTM) based feature extraction with Path Signature Features (PSF) as input and a Feedforward Neural Network (FNN) based binary classifier; (ii) A recurrent latent variable model as generator for synthesizing sequential handwritten data.
Combinatorial Sleeping Bandits with Fairness Constraints
To tackle this new problem, we extend an online learning algorithm, UCB, to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints.
Joint Placement and Allocation of VNF Nodes with Budget and Capacity Constraints
With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware.
Networking and Internet Architecture Data Structures and Algorithms
The Power of Waiting for More than One Response in Minimizing the Age-of-Information
Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side.
Networking and Internet Architecture
