TY - JOUR A1 - Abbas, Amira A1 - Ambainis, Andris A1 - Augustino, Brandon A1 - Bärtschi, Andreas A1 - Buhrman, Harry A1 - Coffrin, Carleton A1 - Cortiana, Giorgio A1 - Dunjko, Vedran A1 - Egger, Daniel J. A1 - Elmegreen, Bruce G. A1 - Franco, Nicola A1 - Fratini, Filippo A1 - Fuller, Bryce A1 - Gacon, Julien A1 - Gonciulea, Constantin A1 - Gribling, Sander A1 - Gupta, Swati A1 - Hadfield, Stuart A1 - Heese, Raoul A1 - Kircher, Gerhard A1 - Kleinert, Thomas A1 - Koch, Thorsten A1 - Korpas, Georgios A1 - Lenk, Steve A1 - Marecek, Jakub A1 - Markov, Vanio A1 - Mazzola, Guglielmo A1 - Mensa, Stefano A1 - Mohseni, Naeimeh A1 - Nannicini, Giacomo A1 - O’Meara, Corey A1 - Tapia, Elena Peña A1 - Pokutta, Sebastian A1 - Proissl, Manuel A1 - Rebentrost, Patrick A1 - Sahin, Emre A1 - Symons, Benjamin C. B. A1 - Tornow, Sabine A1 - Valls, Víctor A1 - Woerner, Stefan A1 - Wolf-Bauwens, Mira L. A1 - Yard, Jon A1 - Yarkoni, Sheir A1 - Zechiel, Dirk A1 - Zhuk, Sergiy A1 - Zoufal, Christa T1 - Challenges and opportunities in quantum optimization JF - Nature Reviews Physics Y1 - 2024 U6 - https://doi.org/10.1038/s42254-024-00770-9 SN - 2522-5820 VL - 6 SP - 718 EP - 735 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Carderera, Alejandro A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Scalable Frank-Wolfe on generalized self-concordant functions via simple steps JF - SIAM Journal on Optimization Y1 - 2024 U6 - https://doi.org/10.1137/23M1616789 VL - 34 IS - 3 ER - TY - JOUR A1 - Parczyk, Olaf A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Szabó, Tibor T1 - New Ramsey multiplicity bounds and search heuristics JF - Foundations of Computational Mathematics Y1 - 2024 U6 - https://doi.org/10.1007/s10208-024-09675-6 ER - TY - CHAP A1 - Pauls, Jan A1 - Zimmer, Max A1 - Kelly, Una M A1 - Schwartz, Martin A1 - Saatchi, Sassan A1 - Ciais, Philippe A1 - Pokutta, Sebastian A1 - Brandt, Martin A1 - Gieseke, Fabian T1 - Estimating canopy height at scale T2 - Proceedings of the 41st International Conference on Machine Learning N2 - We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring. Y1 - 2024 UR - https://raw.githubusercontent.com/mlresearch/v235/main/assets/pauls24a/pauls24a.pdf VL - 235 SP - 39972 EP - 39988 ER - TY - CHAP A1 - Hendrych, Deborah A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Solving the optimal experiment design problem with mixed-integer convex methods T2 - 22nd International Symposium on Experimental Algorithms (SEA 2024) N2 - We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. The objective is to maximize some measure of information gained about the system from the observations, leading to a convex integer optimization problem. We leverage Boscia.jl, a recent algorithmic framework, which is based on a nonlinear branch-and-bound algorithm with node relaxations solved to approximate optimality using Frank-Wolfe algorithms. One particular advantage of the method is its efficient utilization of the polytope formed by the original constraints which is preserved by the method, unlike alternative methods relying on epigraph-based formulations. We assess our method against both generic and specialized convex mixed-integer approaches. Computational results highlight the performance of our proposed method, especially on large and challenging instances. Y1 - 2024 U6 - https://doi.org/10.4230/LIPIcs.SEA.2024.16 VL - 301 SP - 16:1 EP - 16:22 ER - TY - CHAP A1 - Kiem, Aldo A1 - Pokutta, Sebastian A1 - Spiegel, Christoph T1 - Categorification of Flag Algebras T2 - Discrete Mathematics Days 2024 Y1 - 2024 U6 - https://doi.org/10.37536/TYSP5643 SP - 259 EP - 264 ER - TY - CHAP A1 - Kiem, Aldo A1 - Pokutta, Sebastian A1 - Spiegel, Christoph T1 - The Four-Color Ramsey Multiplicity of Triangles T2 - Discrete Mathematics Days 2024 Y1 - 2024 U6 - https://doi.org/10.37536/TYSP5643 SP - 13 EP - 18 ER - TY - JOUR A1 - Mundinger, Konrad A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Zimmer, Max T1 - Extending the Continuum of Six-Colorings JF - Geombinatorics Quarterly Y1 - 2024 VL - 34 IS - 1 SP - 20 EP - 29 ER - TY - CHAP A1 - Mundinger, Konrad A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Zimmer, Max T1 - Extending the Continuum of Six-Colorings T2 - Discrete Mathematics Days 2024 Y1 - 2024 U6 - https://doi.org/10.37536/TYSP5643 SP - 178 EP - 183 ER - TY - CHAP A1 - Martínez-Rubio, David A1 - Roux, Christophe A1 - Pokutta, Sebastian T1 - Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point T2 - Proceedings of the 41st International Conference on Machine Learning N2 - In this work, we analyze two of the most fundamental algorithms in geodesically convex optimization: Riemannian gradient descent and (possibly inexact) Riemannian proximal point. We quantify their rates of convergence and produce different variants with several trade-offs. Crucially, we show the iterates naturally stay in a ball around an optimizer, of radius depending on the initial distance and, in some cases, on the curvature. Previous works simply assumed bounded iterates, resulting in rates that were not fully quantified. We also provide an implementable inexact proximal point algorithm and prove several new useful properties of Riemannian proximal methods: they work when positive curvature is present, the proximal operator does not move points away from any optimizer, and we quantify the smoothness of its induced Moreau envelope. Further, we explore beyond our theory with empirical tests. Y1 - 2024 UR - https://raw.githubusercontent.com/mlresearch/v235/main/assets/marti-nez-rubio24a/marti-nez-rubio24a.pdf VL - 235 SP - 34920 EP - 34948 ER -