Search Results for author: Ting He

Found 13 papers, 2 papers with code

An Approach for Fast Cascading Failure Simulation in Dynamic Models of Power Systems

no code implementations29 Apr 2022 Sina Gharebaghi, Nilanjan Ray Chaudhuri, Ting He, Thomas La Porta

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

no code implementations13 Apr 2022 Hanlin Lu, Changchang Liu, Shiqiang Wang, Ting He, Vijay Narayanan, Kevin S. Chan, Stephen Pasteris

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.

BIG-bench Machine Learning Quantization

Topology Estimation Following Islanding and its Impact on Preventive Control of Cascading Failure

no code implementations13 Apr 2021 Sai Gopal Vennelaganti, Nilanjan Ray Chaudhuri, Ting He, Thomas La Porta

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

no code implementations8 Feb 2021 Hanlin Lu, Ting He, Shiqiang Wang, Changchang Liu, Mehrdad Mahdavi, Vijaykrishnan Narayanan, Kevin S. Chan, Stephen Pasteris

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.

BIG-bench Machine Learning Dimensionality Reduction +1

Verifiable Failure Localization in Smart Grid under Cyber-Physical Attacks

no code implementations18 Jan 2021 Yudi Huang, Ting He, Nilanjan Ray Chaudhuri, Thomas La Porta

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.


Identification of Additive Link Metrics: Proof of Selected Theorems

no code implementations18 Dec 2020 Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley

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

no code implementations17 Dec 2020 Liang Ma, Ting He, Ananthram Swami, Don Towsley, Kin K. Leung

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

no code implementations30 Aug 2020 Ting He

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

no code implementations6 Jul 2020 Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, Mark Herbster

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

no code implementations6 Jul 2020 Hanlin Lu, Changchang Liu, Ting He, Shiqiang Wang, Kevin S. Chan

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).

Federated Learning Inference Attack +1

Robust Coreset Construction for Distributed Machine Learning

no code implementations11 Apr 2019 Hanlin Lu, Ming-Ju Li, Ting He, Shiqiang Wang, Vijaykrishnan Narayanan, Kevin S. Chan

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.

BIG-bench Machine Learning Clustering

Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process

1 code implementation17 Jun 2015 Shiqiang Wang, Rahul Urgaonkar, Murtaza Zafer, Ting He, Kevin Chan, Kin K. Leung

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

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