retrieve:
Return the details about the given Event id.

list:
List all Event objects.

GET /api/v1/events/?format=api&offset=130&ordering=event__start_time
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "start_date": "2026-04-29",
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            "description": "<strong><a href=\"https://docs.google.com/forms/d/e/1FAIpQLSc1NukuuzIzzMnbDZkfudvj1M7wDJY6JeWQKoQrcl1ZIVFcUw/viewform?usp=header\">Registration</a><br>\r\n<br>\r\nAbstract</strong><br>\r\nThis workshop is designed for EPFL researchers interested in building effective, automated computer vision systems for scientific applications. During the workshop, we will review and explain the main steps of a machine learning-based computer vision project, from problem definition to image acquisition, annotation, training, validation, and inference. We will illustrate these steps in a practical way, by building a real computer vision application together until we can run it live on a camera. Along the way, we will discuss important concepts (data drift, overfitting, data augmentation) and observe how they manifest themselves in practice. We will also share tips and tricks to help you tackle your own computer vision challenges.<br>\r\n<br>\r\n<strong>Prerequisites:</strong><br>\r\nBasic familiarity with Python, image analysis, and machine learning concepts is desirable, but not strictly necessary.<br>\r\n<br>\r\n<strong>Level:</strong> <br>\r\nBeginner / Intermediate<br>\r\n ",
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            "organizer": "<a href=\"https://imaging.epfl.ch/\">Center for Imaging</a>",
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            "description": "<strong>Abstract</strong><br>\r\nWe propose an adaptive, “parameter-agnostic” algorithm for learning in harmonic games, a class of finite games characterized by strictly opposed, conflicting interests. In contrast to zero-sum games—which do not always capture conflict, even between two players—harmonic games may exhibit highly non-convex equilibrium configurations. In particular, if the game’s ambient dimension is odd, we show that the set of totally mixed Nash equilibria is a (typically non-convex) real algebraic variety of dimension at least one, which extends all the way to the boundary of the game’s strategy space. Despite the difficulties incurred by this complicated equilibrium structure, we provide an extrapolation-based variant of “follow-the-regularized-leader” (FTRL) which converges to Nash equilibrium with order-optimal regret guarantees: O(1) in self-play, and O( sqrt(T) ) against arbitrary play.<br>\r\n<br>\r\n<br>\r\n<strong>Biography</strong><br>\r\nDavide Legacci is a PhD candidate in Computer Science at Université Grenoble Alpes, CNRS and Inria, within the GHOST team, supervised by Panayotis Mertikopoulos and Bary S. R. Pradelski. His research lies at the intersection of game theory and online learning, with a focus on the geometric structure of competitive interactions and the design of no-regret learning dynamics for adversarial environments. His work has been presented at leading machine learning venues including ICML and NeurIPS. He is expected to defend his PhD in September 2026.<br>\r\n ",
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            "image_description": "",
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            "description": "<strong>Agenda</strong><br>\r\n12:10 – 12:30 25th Institute Anniversary<br>\r\n12:30 – 14:00 Standing Lunch",
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