research
∙
08/21/2023
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Online decision making plays a crucial role in numerous real-world appli...
share
research
∙
05/03/2023
Efficient Online Decision Tree Learning with Active Feature Acquisition
Constructing decision trees online is a classical machine learning probl...
research
∙
04/06/2023
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction
Most machine learning and data analytics applications, including perform...
research
∙
03/30/2023
Utilizing Reinforcement Learning for de novo Drug Design
Deep learning-based approaches for generating novel drug molecules with ...
research
∙
03/27/2023
Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs
In this paper, we propose machine learning solutions to predict the time...
research
∙
02/20/2023
Active Learning with Positive and Negative Pairwise Feedback
In this paper, we propose a generic framework for active clustering with...
research
∙
01/17/2023
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles
In this work, we address the problem of long-distance navigation for bat...
research
∙
10/28/2022
An Online Learning Approach for Vehicle Usage Prediction During COVID-19
Today, there is an ongoing transition to more sustainable transportation...
research
∙
08/25/2022
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks
Predicting the performance of production code prior to actually executin...
research
∙
07/04/2022
Autonomous Drug Design with Multi-armed Bandits
Recent developments in artificial intelligence and automation could pote...
research
∙
06/16/2022
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification
Bottleneck identification is a challenging task in network analysis, esp...
research
∙
03/04/2022
Passive and Active Learning of Driver Behavior from Electric Vehicles
Modeling driver behavior provides several advantages in the automotive i...
research
∙
01/21/2022
Deep Q-learning: a robust control approach
In this paper, we place deep Q-learning into a control-oriented perspect...
research
∙
11/03/2021
Online Learning of Energy Consumption for Navigation of Electric Vehicles
Energy-efficient navigation constitutes an important challenge in electr...
research
∙
10/25/2021
Shift of Pairwise Similarities for Data Clustering
Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide t...
research
∙
09/17/2021
Online Learning of Network Bottlenecks via Minimax Paths
In this paper, we study bottleneck identification in networks via extrac...
research
∙
08/06/2021
Analysis of Driving Scenario Trajectories with Active Learning
Annotating the driving scenario trajectories based only on explicit rule...
research
∙
07/19/2021
Constrained Policy Gradient Method for Safe and Fast Reinforcement Learning: a Neural Tangent Kernel Based Approach
This paper presents a constrained policy gradient algorithm. We introduc...
research
∙
01/12/2021
A Unified Framework for Online Trip Destination Prediction
Trip destination prediction is an area of increasing importance in many ...
research
∙
09/25/2020
A Generic Framework for Clustering Vehicle Motion Trajectories
The development of autonomous vehicles requires having access to a large...
research
∙
09/22/2020
Model-Centric and Data-Centric Aspects of Active Learning for Neural Network Models
We study different data-centric and model-centric aspects of active lear...
research
∙
07/28/2020
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories
We propose a unified deep learning framework for generation and analysis...
research
∙
07/22/2020
Frank-Wolfe Optimization for Dominant Set Clustering
We study Frank-Wolfe algorithms – standard, pairwise, and away-steps – f...
research
∙
05/26/2020
Memory-Efficient Sampling for Minimax Distance Measures
Minimax distance measure extracts the underlying patterns and manifolds ...
research
∙
03/30/2020
On the Unreasonable Effectiveness of Knowledge Distillation: Analysis in the Kernel Regime
Knowledge distillation (KD), i.e. one classifier being trained on the ou...
research
∙
03/27/2020
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Spiking neural networks (SNNs) can be used in low-power and embedded sys...
research
∙
03/03/2020
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Energy-efficient navigation constitutes an important challenge in electr...
research
∙
02/18/2020
Hierarchical Correlation Clustering and Tree Preserving Embedding
We propose a hierarchical correlation clustering method that extends the...
research
∙
06/24/2019
Lifelong Learning Starting From Zero
We present a deep neural-network model for lifelong learning inspired by...
research
∙
05/13/2019
Spectral Analysis of Kernel and Neural Embeddings: Optimization and Generalization
We extend the recent results of (Arora et al., 2019) by a spectral analy...
research
∙
04/27/2019
Nonparametric feature extraction based on Minimax distance
We investigate the use of Minimax distances to extract in a nonparametri...
research
∙
12/25/2018
Trip Prediction by Leveraging Trip Histories from Neighboring Users
We propose a novel approach for trip prediction by analyzing user's trip...
research
∙
12/21/2018
Nonparametric Feature Extraction from Dendrograms
We study nonparametric feature extraction from hierarchies. The commonly...
research
∙
12/20/2018
Reliable Agglomerative Clustering
We analyze the general behavior of agglomerative clustering methods, and...
research
∙
03/16/2017