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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...
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06/05/2023
Personalized Federated Domain Adaptation for Item-to-Item Recommendation
Item-to-Item (I2I) recommendation is an important function in most recom...
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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...
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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...
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03/18/2022
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
This paper studies the item-to-item recommendation problem in recommende...
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05/31/2021
Adaptive Multi-Source Causal Inference
Data scarcity is a tremendous challenge in causal effect estimation. In ...
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05/31/2021
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, wi...
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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...
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05/21/2020
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare...
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11/15/2019
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidit...
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11/01/2019
Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, ...
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05/28/2019
Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different serve...
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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 ...
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11/19/2017
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs
This paper presents a novel decentralized high-dimensional Bayesian opti...
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11/01/2017
Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models
This paper presents a novel variational inference framework for deriving...
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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...
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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...
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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...
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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...
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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...
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09/19/2012