Search Results for author:
Found 18 papers, 3 papers with code
AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology
The delivery of appropriate targeted therapies to cancer patients requires the complete analysis of the molecular profiling of tumors and the patient's clinical characteristics in the context of existing knowledge and recent findings described in biomedical literature and several other sources.
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks
The emerging machine learning paradigm of decentralized federated learning (DFL) has the promise of greatly boosting the deployment of artificial intelligence (AI) by directly learning across distributed agents without centralized coordination.
Active Learning for WBAN-based Health Monitoring
These challenges make our problem fundamentally different from classical active learning, where unlabeled samples are free and labels can be queried in real time.
FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs
This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs).
Energy-efficient Decentralized Learning via Graph Sparsification
A solution with guaranteed performance is proposed for the special case of fully-connected base topology and a greedy heuristic is proposed for the general case.
An Approach for Fast Cascading Failure Simulation in Dynamic Models of Power Systems
To solve this, we propose a fast cascading failure simulation approach based on implicit Backward Euler method (BEM) with stiff decay property.
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs.
Topology Estimation Following Islanding and its Impact on Preventive Control of Cascading Failure
Knowledge of power grid's topology during cascading failure is an essential element of centralized blackout prevention control, given that multiple islands are typically formed, as a cascade progresses.
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers.
Verifiable Failure Localization in Smart Grid under Cyber-Physical Attacks
Our numerical evaluations based on the Polish power grid and IEEE 300-bus system demonstrate that the proposed algorithms are highly successful in verifying the states of truly failed links, and can thus greatly help in prioritizing repairs during the recovery process.
Performance
Identification of Additive Link Metrics: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013.
Networking and Internet Architecture
Node Failure Localization: Theorem Proof
This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7. 3 Performance, 2015.
Networking and Internet Architecture
Nonparametric Predictive Inference for Asian options
It is attractive to infer the Asian option price with few assumptions of the underlying asset distribution and adopt to the historical data with a nonparametric method.
Online Learning of Facility Locations
In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world applications.
Sharing Models or Coresets: A Study based on Membership Inference Attack
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data summaries (coreset).
Robust Coreset Construction for Distributed Machine Learning
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations.
Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Optimization and Control
