19 citations · 109 across the 21 of their papers we have counts for
22 papers
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Takuya Kanazawa, Chetan Gupta
Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that emb…
CDA: Contrastive-adversarial Domain Adaptation
Nishant Yadav, Mahbubul Alam, Ahmed Farahat +3
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target doma…
A Functional approach for Two Way Dimension Reduction in Time Series
Aniruddha Rajendra Rao, Haiyan Wang, Chetan Gupta
The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and com…
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
Takuya Kanazawa, Chetan Gupta
Development of an accurate, flexible, and numerically efficient uncertainty quantification (UQ) method is one of fundamental challenges in machine learning. Previously, a UQ method…
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
Takuya Kanazawa, Haiyan Wang, Chetan Gupta
Uncertainty quantification is one of the central challenges for machine learning in real-world applications. In reinforcement learning, an agent confronts two kinds of uncertainty,…
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents
Hamed Khorasgani, Haiyan Wang, Hsiu-Khuern Tang +1
Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of age…
Data-driven Residual Generation for Early Fault Detection with Limited Data
Hamed Khorasgani, Ahmed Farahat, Chetan Gupta
Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems.…
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Hamed Khorasgani, Haiyan Wang, Chetan Gupta +1
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to…
Deep Reinforcement Learning with Adjustments
Hamed Khorasgani, Haiyan Wang, Chetan Gupta +1
Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated prob…
Deep Time Series Models for Scarce Data
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time seri…
A Non-linear Function-on-Function Model for Regression with Time Series Data
Qiyao Wang, Haiyan Wang, Chetan Gupta +2
In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from…
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz +5
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant beha…
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
Hamed Khorasgani, Haiyan Wang, Chetan Gupta
Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization i…
Spatio-Temporal Functional Neural Networks
Aniruddha Rajendra Rao, Qiyao Wang, Haiyan Wang +2
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The s…
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
Chi Zhang, Philip Odonkor, Shuai Zheng +3
Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to t…
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches fo…
Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities
Walid Shalaby, Adriano Arantes, Teresa GonzalezDiaz +1
Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deploym…
Regularized Operating Envelope with Interpretability and Implementability Constraints
Qiyao Wang, Haiyan Wang, Chetan Gupta +1
Operating envelope is an important concept in industrial operations. Accurate identification for operating envelope can be extremely beneficial to stakeholders as it provides a set…
Manufacturing Dispatching using Reinforcement and Transfer Learning
Shuai Zheng, Chetan Gupta, Susumu Serita
Efficient dispatching rule in manufacturing industry is key to ensure product on-time delivery and minimum past-due and inventory cost. Manufacturing, especially in the developed w…
Generative Adversarial Networks for Failure Prediction
Shuai Zheng, Ahmed Farahat, Chetan Gupta
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the ke…
Remaining Useful Life Estimation Using Functional Data Analysis
Qiyao Wang, Shuai Zheng, Ahmed Farahat +2
Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estim…
Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
Karan Aggarwal, Onur Atan, Ahmed Farahat +3
One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in…