no code implementations • 1 Apr 2024 • Ilayda Canyakmaz, Iosif Sakos, Wayne Lin, Antonios Varvitsiotis, Georgios Piliouras
To tackle this challenge, in this work we introduce the SIAR-MPC method, combining the recently introduced Side Information Assisted Regression (SIAR) method for system identification with Model Predictive Control (MPC).
no code implementations • 13 Jul 2023 • Iosif Sakos, Antonios Varvitsiotis, Georgios Piliouras
In this work, we propose a theoretical and algorithmic framework for real-time identification of the learning dynamics that govern agent behavior using a short burst of a single system trajectory.
no code implementations • 6 Jul 2023 • Ilayda Canyakmaz, Wayne Lin, Georgios Piliouras, Antonios Varvitsiotis
We study online convex optimization where the possible actions are trace-one elements in a symmetric cone, generalizing the extensively-studied experts setup and its quantum counterpart.
no code implementations • 2 Aug 2021 • Yong Sheng Soh, Antonios Varvitsiotis
Given a matrix $X\in \mathbb{R}^{m\times n}_+$ with non-negative entries, the cone factorization problem over a cone $\mathcal{K}\subseteq \mathbb{R}^k$ concerns computing $\{ a_1,\ldots, a_{m} \} \subseteq \mathcal{K}$ and $\{ b_1,\ldots, b_{n} \} \subseteq~\mathcal{K}^*$ belonging to its dual so that $X_{ij} = \langle a_i, b_j \rangle$ for all $i\in [m], j\in [n]$.
no code implementations • NeurIPS 2021 • Yong Sheng Soh, Antonios Varvitsiotis
The most widely used algorithm for computing NMFs of a matrix is the Multiplicative Update algorithm developed by Lee and Seung, in which nonnegativity of the updates is preserved by scaling with positive diagonal matrices.
no code implementations • 26 Feb 2020 • Ioannis Panageas, Stratis Skoulakis, Antonios Varvitsiotis, Xiao Wang
Non-negative matrix factorization (NMF) is a fundamental non-convex optimization problem with numerous applications in Machine Learning (music analysis, document clustering, speech-source separation etc).
1 code implementation • 11 Jun 2019 • Sandra S. Y. Tan, Antonios Varvitsiotis, Vincent Y. F. Tan
Program., 145(1):451--482, 2014], a powerful framework for determining convergence rates of first-order optimization algorithms.