22 citations · 146 across the 47 of their papers we have counts for
52 papers
Active Feature Acquisition With Incomplete Training Data
Reza Rezvan, Valter Schütz, Han Wu +2
In many prediction tasks, acquiring all features can be a prohibitively expensive or outright impossible task. Further, in many cases a static subset of features may not be enough…
Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
Hannes Nilsson, Rafael Basso, Balázs Kulcsár +1
In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses du…
Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
Erik Börve, Nikolce Murgovski, Morteza Haghir Chehreghani +1
We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego-agent, we aim to first learn the decision distribut…
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
Linus Aronsson, Morteza Haghir Chehreghani
Active feature acquisition (AFA) considers prediction problems in which features are costly to obtain and the learner adaptively decides which feature values to acquire for each in…
Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
Filip Rydin, Morteza Haghir Chehreghani, Balázs Kulcsár
Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multigraphs, where parallel edges repr…
Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics
Kilian Freitag, Knut Åkesson, Morteza Haghir Chehreghani
Deep Reinforcement Learning is a promising tool for robotic control, yet practical application is often hindered by the difficulty of designing effective reward functions. Real-wor…
Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles
Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
A central difficulty in applying reinforcement learning to real-world applications such as autonomous driving is that conventional scalar reward formulations obtained by aggregatin…
Learning for routing: A guided review of recent developments and future directions
Fangting Zhou, Attila Lischka, Balazs Kulcsar +3
This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the t…
Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs
Filip Rydin, Attila Lischka, Jiaming Wu +2
Learning-based methods for routing have gained significant attention in recent years, both in single-objective and multi-objective contexts. Yet, existing methods are unsuitable fo…
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
Hampus Gummesson Svensson, Ola Engkvist, Jon Paul Janet +2
In many real-world applications, evaluating the quality of instances is costly and time-consuming, e.g., human feedback and physics simulations, in contrast to proposing new instan…
A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
Peter Samoaa, Marcus Vukojevic, Morteza Haghir Chehreghani +1
Graph-level regression underpins many real-world applications, yet public benchmarks remain heavily skewed toward molecular graphs and citation networks. This limited diversity hin…
A Survey on Active Feature Acquisition Strategies
Linus Aronsson, Arman Rahbar, Morteza Haghir Chehreghani
Active feature acquisition (AFA) studies how to sequentially acquire features for each data instance to trade off predictive performance against acquisition cost. This survey offer…
An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks
Linus Aronsson, Morteza Haghir Chehreghani
Signed networks, where edges are labeled as positive or negative to represent friendly or antagonistic interactions, provide a natural framework for analyzing polarization, trust,…
Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
Jack Sandberg, Morteza Haghir Chehreghani
Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions. The characteristics of the unknown function depend heavily on…
Curriculum Reinforcement Learning for Complex Reward Functions
Kilian Freitag, Kristian Ceder, Rita Laezza +2
Reinforcement learning (RL) has emerged as a powerful tool for tackling control problems, but its practical application is often hindered by the complexity arising from intricate r…
Diversity-Aware Reinforcement Learning for de novo Drug Design
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist +1
Fine-tuning a pre-trained generative model has demonstrated good performance in generating promising drug molecules. The fine-tuning task is often formulated as a reinforcement lea…
GREAT: A Graph Edge Attention Network for the Electric Vehicle Routing Problem with Asymmetric Costs
Attila Lischka, Filip Rydin, Jiaming Wu +2
Due to the ongoing electrification of transportation systems, energy-based routing problems such as the Electric Vehicle Routing Problem (EVRP) are becoming increasingly important.…
Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks
Peter Samoaa, Mehrdad Farahani, Antonio Longa +2
The landscape of deep learning has vastly expanded the frontiers of source code analysis, particularly through the utilization of structural representations such as Abstract Syntax…
Less Is More -- On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP
Attila Lischka, Jiaming Wu, Rafael Basso +2
Most of the recent studies tackling routing problems like the Traveling Salesman Problem (TSP) with machine learning use a transformer or Graph Neural Network (GNN) based encoder a…
Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in…
Tree Ensembles for Contextual Bandits
Hannes Nilsson, Rikard Johansson, Niklas Åkerblom +1
We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Samp…
Information-Theoretic Active Correlation Clustering
Linus Aronsson, Morteza Haghir Chehreghani
Correlation clustering is a flexible framework for partitioning data based solely on pairwise similarity or dissimilarity information, without requiring the number of clusters as i…
Bayesian Analysis of Combinatorial Gaussian Process Bandits
Jack Sandberg, Niklas Åkerblom, Morteza Haghir Chehreghani
We consider the combinatorial volatile Gaussian process (GP) semi-bandit problem. Each round, an agent is provided a set of available base arms and must select a subset of them to…
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Arman Rahbar, Niklas Åkerblom, Morteza Haghir Chehreghani
Online decision making plays a crucial role in numerous real-world applications. In many scenarios, the decision is made based on performing a sequence of tests on the incoming dat…