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Optimization

Finding optimal solution for AI tasks ↓
Optimization

Mathematical optimization is a field of study on the intersection of mathematics, computer science, and electrical engineering that deals with the selection of a best element out of a set with respect to some criterion. The elements of the set are known as feasible solutions and the criterion is known as the objective function. Over the past couple of centuries, much of the work in mathematical optimization has focussed on the case of a convex, time-invariant set of feasible solutions and convex, time-invariant objective functions. This special case has become the work horse of machine learning, artificial intelligence, and most fields of engineering.

Research Focus

In our basic research, we focus study extensions towards (1) certain smooth, non-convex feasible sets and objective functions and (2) time-varying feasible sets and objective functions. The smooth non-convex problems, known as commutative and non-commutative polynomial optimization, have extensive applications in power systems, control theory, and machine learning, among others. The same applications can often benefit from the time-varying extensions.

Particular examples of the latter (2) include our papers at AAAI 2019 (https://arxiv.org/abs/1809.05870) and AAAI 2020 (https://arxiv.org/abs/1809.03550), which deal with time-varying optimization.

Particular examples of the former (1) include our paper at AAAI 2024 (https://arxiv.org/abs/2310.04469), which deals with smoothing of a non-smooth non-convex optimization problem and our papers at AAAI 2021 (https://arxiv.org/abs/2006.07315), in the Journal of AI Research (https://arxiv.org/abs/2209.05274), and IEEE Transactions on Automatic Control (https://arxiv.org/abs/2002.01444) deal with non-commutative polynomial optimization. Notably, we can get the present best results on the COMPAS dataset.

Independent of this, in a series of papers in Automatica (e.g., https://arxiv.org/abs/1807.03256, https://arxiv.org/abs/2110.03001, https://arxiv.org/abs/2112.06767) and IJC (https://arxiv.org/abs/2209.13273), we are working on the control on non-linear systems under uncertainty.

TENORS

TENORS

Several Ph.D. positions are available within Marie Skłodowska-Curie Doctoral Network for tensor modeling, geometry and optimization.

CoDiet

CoDiet

Horizon Europe project aiming to combat diet-related diseases through innovative diet-monitoring technologies, AI-assisted data analysis and personalized nutrition.

AutoFair (Human-Compatible AI with Guarantees)

AutoFair (Human-Compatible AI with Guarantees)

Horizon Europe project for fair AI algorithms supported by the EU with 3,8 million Euros. Imperial College London, the Israeli Institute of Technology Technion and the National and Capodistrian University of Athens as well as partners from the industry collaborate on developing explainable and transparent algorithms.

Research Results

Predictability and Fairness in Social Sensing

Predictability and Fairness in Social Sensing

The manner in which agents contribute to a social-computing platform (e.g., social sensing, social media) is often governed by distributed algorithms. We explore the guarantees available in situations, where fairness among the agents contributing to the platform is needed.

Hacking
human future

AI Act on the horizon: Companies should start preparing

AI Act on the horizon: Companies should start preparing

Before Christmas, so-called trilogue negotiations between the European Parliament, the Council of the European Union and the European Commission culminated in a preliminary agreement on a regulation known as the Artificial Intelligence Act ("Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence"). A few more formal steps will be needed, but the regulation will be in force before the European Parliament elections in June 2024. And after transitional periods (from 6 to 36 months, depending on the risks), it will come into effect.

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