Trong Nghia Hoang | DeepAI

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∙ 08/26/2023

Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization

Data summarization is the process of generating interpretable and repres...

0 Alireza Ghods, et al. ∙

∙ 06/05/2023

Personalized Federated Domain Adaptation for Item-to-Item Recommendation

Item-to-Item (I2I) recommendation is an important function in most recom...

0 Ziwei Fan, et al. ∙

∙ 06/02/2023

Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs

Learning an effective global model on private and decentralized datasets...

0 Tengfei Ma, et al. ∙

∙ 07/19/2022

Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms

As deep learning models have gradually become the main workhorse of time...

0 Linbo Liu, et al. ∙

∙ 03/18/2022

Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback

This paper studies the item-to-item recommendation problem in recommende...

0 Trong Nghia Hoang, et al. ∙

∙ 05/31/2021

Adaptive Multi-Source Causal Inference

Data scarcity is a tremendous challenge in causal effect estimation. In ...

0 Thanh Vinh Vo, et al. ∙

∙ 05/31/2021

Federated Estimation of Causal Effects from Observational Data

Many modern applications collect data that comes in federated spirit, wi...

0 Thanh Vinh Vo, et al. ∙

∙ 11/17/2020

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes

We introduce a new scalable approximation for Gaussian processes with pr...

0 Quang Minh Hoang, et al. ∙

∙ 05/21/2020

CHEER: Rich Model Helps Poor Model via Knowledge Infusion

There is a growing interest in applying deep learning (DL) to healthcare...

0 Cao Xiao, et al. ∙

∙ 11/15/2019

CASTER: Predicting Drug Interactions with Chemical Substructure Representation

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidit...

0 Kexin Huang, et al. ∙

∙ 11/01/2019

Statistical Model Aggregation via Parameter Matching

We consider the problem of aggregating models learned from sequestered, ...

0 Mikhail Yurochkin, et al. ∙

∙ 05/28/2019

Bayesian Nonparametric Federated Learning of Neural Networks

In federated learning problems, data is scattered across different serve...

0 Mikhail Yurochkin, et al. ∙

∙ 05/23/2018

Collective Online Learning via Decentralized Gaussian Processes in Massive Multi-Agent Systems

Distributed machine learning (ML) is a modern computation paradigm that ...

0 Trong Nghia Hoang, et al. ∙

∙ 11/19/2017

Decentralized High-Dimensional Bayesian Optimization with Factor Graphs

This paper presents a novel decentralized high-dimensional Bayesian opti...

0 Trong Nghia Hoang, et al. ∙

∙ 11/01/2017

Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models

This paper presents a novel variational inference framework for deriving...

0 Haibin Yu, et al. ∙

∙ 10/17/2017

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

A key challenge in multi-robot and multi-agent systems is generating sol...

0 Trong Nghia Hoang, et al. ∙

∙ 11/18/2016

A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression

While much research effort has been dedicated to scaling up sparse Gauss...

0 Quang Minh Hoang, et al. ∙

∙ 11/21/2015

Near-Optimal Active Learning of Multi-Output Gaussian Processes

This paper addresses the problem of active learning of a multi-output Ga...

0 Yehong Zhang, et al. ∙

∙ 04/18/2013

Interactive POMDP Lite: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents

A key challenge in non-cooperative multi-agent systems is that of develo...

0 Trong Nghia Hoang, et al. ∙

∙ 04/07/2013

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior

Recent advances in Bayesian reinforcement learning (BRL) have shown that...

0 Trong Nghia Hoang, et al. ∙

∙ 09/19/2012

Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments

A central problem of surveillance is to monitor multiple targets moving ...

0 Prabhu Natarajan, et al. ∙

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