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09/19/2023
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization
We investigate nonlinear prediction/regression in an online setting and ...
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10/10/2022
Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI
We investigate the joint transmit beamforming and reconfigurable intelli...
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10/01/2022
Deep Intrinsically Motivated Exploration in Continuous Control
In continuous control, exploration is often performed through undirected...
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09/01/2022
Actor Prioritized Experience Replay
A widely-studied deep reinforcement learning (RL) technique known as Pri...
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08/07/2022
Optimal Tracking in Prediction with Expert Advice
We study the prediction with expert advice setting, where the aim is to ...
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08/01/2022
Off-Policy Correction for Actor-Critic Algorithms in Deep Reinforcement Learning
Compared to on-policy policy gradient techniques, off-policy model-free ...
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07/27/2022
Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
Learning in high dimensional continuous tasks is challenging, mainly whe...
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03/25/2022
A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization
We investigate nonlinear prediction in an online setting and introduce a...
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11/12/2021
AWD3: Dynamic Reduction of the Estimation Bias
Value-based deep Reinforcement Learning (RL) algorithms suffer from the ...
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11/02/2021
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay
The experience replay mechanism allows agents to use the experiences mul...
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09/24/2021
Parameter-Free Deterministic Reduction of the Estimation Bias in Continuous Control
Approximation of the value functions in value-based deep reinforcement l...
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09/22/2021
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods
In value-based deep reinforcement learning methods, approximation of val...
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09/05/2020
PySAD: A Streaming Anomaly Detection Framework in Python
PySAD is an open-source python framework for anomaly detection on stream...
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08/26/2020
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance
We investigate cross-lingual sentiment analysis, which has attracted sig...
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06/25/2020
Spatio-temporal Sequence Prediction with Point Processes and Self-organizing Decision Trees
We investigate spatio-temporal prediction and introduce a novel predicti...
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05/22/2020
A Tree Architecture of LSTM Networks for Sequential Regression with Missing Data
We investigate regression for variable length sequential data containing...
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05/16/2020
Achieving Online Regression Performance of LSTMs with Simple RNNs
Recurrent Neural Networks (RNNs) are widely used for online regression d...
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05/12/2020
Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization
We investigate unsupervised anomaly detection for high-dimensional data ...
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03/07/2020
RNN-based Online Learning: An Efficient First-Order Optimization Algorithm with a Convergence Guarantee
We investigate online nonlinear regression with continually running recu...
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11/25/2019
Stability of the Decoupled Extended Kalman Filter Learning Algorithm in LSTM-Based Online Learning
We investigate the convergence and stability properties of the decoupled...
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11/25/2019
Stability of the Decoupled Extended Kalman Filter in the LSTM-Based Online Learning
We investigate the convergence and stability properties of the decoupled...
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11/25/2019
Minimax Optimal Algorithms for Adversarial Bandit Problem with Multiple Plays
We investigate the adversarial bandit problem with multiple plays under ...
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10/22/2019
An Efficient EKF Based Algorithm For LSTM-Based Online Learning
We investigate online nonlinear regression with long short term memory (...
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04/19/2019
Minimax Optimal Online Stochastic Learning for Sequences of Convex Functions under Sub-Gradient Observation Failures
We study online convex optimization under stochastic sub-gradient observ...
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01/19/2017
Efficient Implementation Of Newton-Raphson Methods For Sequential Data Prediction
We investigate the problem of sequential linear data prediction for real...
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09/30/2014
Data Imputation through the Identification of Local Anomalies
We introduce a comprehensive and statistical framework in a model free s...
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01/23/2014
Predicting Nearly As Well As the Optimal Twice Differentiable Regressor
We study nonlinear regression of real valued data in an individual seque...
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11/25/2013
A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees
In this paper, we investigate adaptive nonlinear regression and introduc...
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03/20/2012