Project
AA4-11
Using Mathematical Programming to Enhance Multiobjective Learning
Project Heads
Aswin Kannan
Project Members
Zijun Li
Project Duration
01.11.2022 − 31.12.2025
Located at
HU Berlin
Description
The relatively newer space of Multiobjective machine learning has focused on Bayesian optimization, with tuning limited to the space of model hyperparameters. Examples of objectives include accuracy, inference time, and complexity. We aim to develop tractable optimization formulations to address the extensive tunable space of models, hyperparameters, model parameters, and feature engineering embodiments. We will also address questions related to multi-label classification, regression, and algorithmic bias (example: fairness). From an Implementation perspective, our focus will be on developing tailored versions of solvers like SGD (by exploitation of problem structure). Our main applications of interest include problems in energy markets and imaging.
Bachelors and Masters Theses
Bachelors
(1) Thomas Dittmar (HU Berlin, summer 2023): Comparison of machine learning models on tabular weather data for the prediction of wind power production.
(2) Hasan Mert Goekalp (HU Berlin, summer 2023): On effectively handling metrics beyond accuracy in renewable energy based regression problems.
(3) Marco Giacomo (HU Berlin, summer 24): Multiobjective learning meets Real Estate Pricing.
(4) Aleksandra Zinoveva (HU Berlin, summer 24): Solving the relaxed/continuous versions of multi-objective unit commitment problem for variable renewable energy networks.
Masters
(1) Ece Tevruez (HU-Berlin, ongoing, to be completed, August 2024): Multi-Objective Unit Commitment: Comparison of Algorithms and Approximations.
Related Publications
(1) Zijun Li and Aswin Kannan, Algorithm Switching for Multiobjective Predictions in Renewable Energy Markets, Proceedings of the Learning and Intelligent Optimization Conference, June 2024.
(2) Aswin Kannan, Benefits of Multiobjective Learning in Solar Energy predictions, Proceedings of the AI2SE Workshop, AAAI, February 2023.
(3) Aswin Kannan Et. Al., HyperASPO: Fusion of Model and Hyper Parameter Optimization for Multi-objective Machine Learning, Proceedings of the IEEE Conference on Big Data, December 2021.
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