Offline and Online Models for Learning Pairwise Relations in Data
Doctoral thesis, 2023

Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.

The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.

Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.

The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.

Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting.

Mulit-Armed Bandit

Pairwise Relations

Performance Modeling

Motion Trajectory Clustering

Minimax Distance

Bottleneck Identification

Online Learning

Representation Learning

Memory Efficiency

Concurrent Programming

Thompson Sampling

SB-H2,Sven Hultins Gata 6, Campus Johanneberg
Opponent: Prof. Bengt J. Nilsson

Author

Fazeleh Sadat Hoseini

Network and Systems

Modeling the performance of atomic primitives on modern architectures

ACM International Conference Proceeding Series,; (2019)

Paper in proceeding

Memory-Efficient Minimax Distance Measures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),; Vol. 13280 LNAI(2022)p. 419-431

Paper in proceeding

Vehicle Motion Trajectories Clustering via Embedding Transitive Relations

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC,; Vol. 2021-September(2021)p. 1314-1321

Paper in proceeding

Online Learning of Network Bottlenecks via Minimax Paths

Machine Learning,; Vol. 112(2023)p. 131-150

Journal article

Subject Categories

Other Computer and Information Science

Probability Theory and Statistics

Computer Science

ISBN

978-91-7905-820-3

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5286

Publisher

Chalmers

SB-H2,Sven Hultins Gata 6, Campus Johanneberg

Online

Opponent: Prof. Bengt J. Nilsson

More information

Latest update

4/25/2023