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05/05/2023
Fast Dynamic Programming in Trees in the MPC Model
We present a deterministic algorithm for solving a wide range of dynamic...
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03/15/2023
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Sequential decision making in the real world often requires finding a go...
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01/10/2023
CDA: Contrastive-adversarial Domain Adaptation
Recent advances in domain adaptation reveal that adversarial learning on...
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01/01/2023
A Functional approach for Two Way Dimension Reduction in Time Series
The rise in data has led to the need for dimension reduction techniques,...
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09/17/2022
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
Development of an accurate, flexible, and numerically efficient uncertai...
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07/27/2022
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Uncertainty quantification is one of the central challenges for machine ...
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03/02/2022
Sparse matrix multiplication in the low-bandwidth model
We study matrix multiplication in the low-bandwidth model: There are n c...
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01/18/2022
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents
Traditionally, the performance of multi-agent deep reinforcement learnin...
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09/28/2021
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Several machine learning and deep learning frameworks have been proposed...
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09/28/2021
Deep Reinforcement Learning with Adjustments
Deep reinforcement learning (RL) algorithms can learn complex policies t...
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09/04/2021
Dynamic Meta-theorems for Distance and Matching
Reachability, distance, and matching are some of the most fundamental gr...
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03/25/2021
Reachability and Matching in Single Crossing Minor Free Graphs
We construct in Logspace non-zero circulations for H-minor free graphs w...
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03/16/2021
Deep Time Series Models for Scarce Data
Time series data have grown at an explosive rate in numerous domains and...
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11/24/2020
A Non-linear Function-on-Function Model for Regression with Time Series Data
In the last few decades, building regression models for non-scalar varia...
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11/13/2020
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Deep learning classifiers are assisting humans in making decisions and h...
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11/09/2020
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
Dynamic dispatching aims to smartly allocate the right resources to the ...
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09/11/2020
Spatio-Temporal Functional Neural Networks
Explosive growth in spatio-temporal data and its wide range of applicati...
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08/24/2020
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
Dynamic dispatching is one of the core problems for operation optimizati...
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06/05/2020
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
Prognostics is concerned with predicting the future health of the equipm...
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05/13/2020
Time Space Optimal Algorithm for Computing Separators in Bounded Genus Graphs
A graph separator is a subset of vertices of a graph whose removal divid...
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12/31/2019
Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities
Popular conversational agents frameworks such as Alexa Skills Kit (ASK) ...
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12/21/2019
Regularized Operating Envelope with Interpretability and Implementability Constraints
Operating envelope is an important concept in industrial operations. Acc...
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10/04/2019
Manufacturing Dispatching using Reinforcement and Transfer Learning
Efficient dispatching rule in manufacturing industry is key to ensure pr...
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10/04/2019
Generative Adversarial Networks for Failure Prediction
Prognostics and Health Management (PHM) is an emerging engineering disci...
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04/12/2019
Remaining Useful Life Estimation Using Functional Data Analysis
Remaining Useful Life (RUL) of an equipment or one of its components is ...
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12/18/2018