retrieve:
Return the details about the given Memento id.

list:
List all Memento objects.

GET /api/v1/mementos/48/events/?format=api&offset=20&ordering=fr_label
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
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            "id": 71838,
            "title": "Composable Performance Interfaces with Controlled Resolution",
            "slug": "composable-performance-interfaces-with-controlled",
            "event_url": "https://memento.epfl.ch/event/composable-performance-interfaces-with-controlled",
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            "start_date": "2026-06-26",
            "end_date": "2026-06-26",
            "start_time": "11:15:00",
            "end_time": "13:15:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Nate Foster<br>\r\nThesis advisor: Prof. George Candea<br>\r\nCo-examiner: Prof. Rachid Guerraoui<br>\r\n<br>\r\n<u>Abstract</u><br>\r\ncoming soon<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon",
            "image_description": "",
            "creation_date": "2026-05-08T16:49:17",
            "last_modification_date": "2026-05-08T16:49:17",
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            "id": 71980,
            "title": "Reasoning of Sovereign Language Models",
            "slug": "reasoning-of-sovereign-language-models",
            "event_url": "https://memento.epfl.ch/event/reasoning-of-sovereign-language-models",
            "visual_url": null,
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            "start_date": "2026-06-26",
            "end_date": "2026-06-26",
            "start_time": "13:00:00",
            "end_time": "15:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Robert West<br>\r\nThesis advisor: Prof. Emmanuel Abbé<br>\r\nThesis co-advisor: Prof Antoine Bosselut<br>\r\nCo-examiner: Prof. Michael Gastpar<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nThe strategic shift toward Sovereign AI addresses the critical need for nations and enterprises to maintain absolute control over their artificial intelligence infrastructure, data privacy, and jurisdictional compliance. As these localized models move beyond basic tasks to complex, multi-step problem solving, two major priorities have emerged: structural safety alignment and the capacity for autonomous self-improvement. By operating in secure, often air-gapped environments, Sovereign Reasoning Language Models (RLMs) must guarantee robust ethical guardrails and scale their intelligence without relying on continuous streams of external, human-curated data.<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ",
            "image_description": "",
            "creation_date": "2026-05-29T15:04:24",
            "last_modification_date": "2026-05-29T15:04:24",
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            "id": 71755,
            "title": "Controllable Generative Models via Test-Time Adaptation",
            "slug": "controllable-generative-models-via-test-time-adapt",
            "event_url": "https://memento.epfl.ch/event/controllable-generative-models-via-test-time-adapt",
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            "start_date": "2026-07-07",
            "end_date": "2026-07-07",
            "start_time": "09:30:00",
            "end_time": "11:30:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Martin Rajman<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Jean-Philippe Thiran<br>\r\n<br>\r\n<u>Abstract</u><br>\r\ncoming soon<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon",
            "image_description": "",
            "creation_date": "2026-04-29T10:00:51",
            "last_modification_date": "2026-05-08T11:17:00",
            "link_label": "",
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            "cancel_reason": "",
            "place_and_room": "BC 329",
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            "spoken_languages": [],
            "speaker": "Hantao Zhang",
            "organizer": "",
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            "id": 71933,
            "title": "Controlling and Guiding Generative Models for Three-Dimensional Shape Optimization",
            "slug": "controlling-and-guiding-generative-models-for-thre",
            "event_url": "https://memento.epfl.ch/event/controlling-and-guiding-generative-models-for-thre",
            "visual_url": null,
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            "lang": "en",
            "start_date": "2026-07-07",
            "end_date": "2026-07-07",
            "start_time": "11:00:00",
            "end_time": "13:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Martin Rajman<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Mark Pauly<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nOptimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifold drift: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space. Existing mitigation strategies, including latent regularization and flow-matching approaches, either sacrifice expressiveness, demand a difficult trade-off between objective guidance and generative fidelity that remains prone to manifold drift, or are computationally infeasible to scale to modern, large-capacity 3D shape models. We introduce a novel optimizer-corrector framework that alternates between gradient steps for objective minimization and guided flow matching to drive the latent state back to the valid shape manifold. By decoupling objective minimization from flow-based correction, optimizing freely and correcting strictly, this alternating design avoids inherent trade-offs, preserving geometric validity without sacrificing expressiveness while remaining computationally feasible on modern 3D shape models. We demonstrate its effectiveness across generative priors of varying complexity, from simple vector latent spaces to large-scale architectures across a variety of downstream optimization tasks, including aerodynamic drag reduction and object compliance optimization.<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ",
            "image_description": "",
            "creation_date": "2026-05-22T13:14:54",
            "last_modification_date": "2026-05-22T13:14:54",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 333",
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            "url_online_room": "",
            "spoken_languages": [],
            "speaker": "Emilien Seiler",
            "organizer": "",
            "contact": "[email protected]",
            "is_internal": "False",
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            "keywords": "EDIC candidacy exam",
            "file": null,
            "icalendar_url": "https://memento.epfl.ch/event/export/120840/",
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                "id": 1,
                "code": "CONF",
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        {
            "id": 71930,
            "title": "Generative models for black-box optimization of complex objectives",
            "slug": "generative-models-for-black-box-optimization-of-co",
            "event_url": "https://memento.epfl.ch/event/generative-models-for-black-box-optimization-of-co",
            "visual_url": null,
            "visual_large_url": null,
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            "lang": "en",
            "start_date": "2026-07-09",
            "end_date": "2026-07-09",
            "start_time": "10:00:00",
            "end_time": "12:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Nicolas Flammarion<br>\r\nThesis advisor: Prof. Pascal Fua<br>\r\nCo-examiner: Prof. Alexander Mathis<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nMany high-impact problems in science and engineering reduce to optimizing a complex objective, where each evaluation is costly and often unreliable: physical experiments fail, simulators diverge, and stochastic systems return noisy feedback. Classical black-box optimizers struggle in this regime, where evaluation budgets are tight and the feasible region is hard to characterize a priori. Our work investigates how the statistical expressivity of modern generative samplers can be leveraged in black-box optimization. We propose that these samplers enable efficient navigation of feasible sets and optimization of geometrically complex objectives under limited, unreliable observations. <br>\r\n<br>\r\nTwo complementary lines of work have already been studied. The main one, SPARROW, demonstrated that a sampler-driven optimizer can substantially reduce the number of function calls needed to maximize complex and unreliable objectives. A parallel line established that samplers can be steered to satisfy hard constraints without sacrificing the statistical diversity of the generated samples. Because SPARROW imposes minimal assumptions on the underlying sampler, these constrained samplers can drive it directly, opening a path to constrained black-box optimization.<br>\r\n<br>\r\nBuilding on these results, two directions are planned. The first is theoretical: a deeper mathematical study of sampler-driven optimization, drawing on the broader literature on sample-efficient black-box optimization, in order to derive stronger guarantees and more efficient algorithms. The second is applied: applying the methods to concrete domains and exploiting the structure those domains expose to sharpen performance beyond what a pure black-box treatment can achieve.<br>\r\n<br>\r\nThe applications of low-budget black-box optimization are diverse, including engineering design (e.g. aerodynamic shape optimization, mechanical components), scientific discovery (e.g. protein and molecule design under synthesizability and stability constraints), and the safety and security of opaque ML systems (e.g. query-efficient adversarial probing, red-teaming of black-box models).<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ",
            "image_description": "",
            "creation_date": "2026-05-22T12:53:32",
            "last_modification_date": "2026-05-29T15:08:13",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 333",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20333",
            "url_online_room": "",
            "spoken_languages": [],
            "speaker": "Edouard Dufour",
            "organizer": "",
            "contact": "[email protected]",
            "is_internal": "False",
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            "keywords": "EDIC candidacy exam",
            "file": null,
            "icalendar_url": "https://memento.epfl.ch/event/export/120837/",
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                "code": "CONF",
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        {
            "id": 71982,
            "title": "Formally verified native compilation for modern regexes",
            "slug": "formally-verified-native-compilation-for-modern-re",
            "event_url": "https://memento.epfl.ch/event/formally-verified-native-compilation-for-modern-re",
            "visual_url": null,
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            "lang": "en",
            "start_date": "2026-08-04",
            "end_date": "2026-08-04",
            "start_time": "09:30:00",
            "end_time": "11:30:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Viktor Kuncak<br>\r\nThesis advisor: Prof. Clément Pit-Claudel<br>\r\nCo-examiner: Prof. Nate Foster<br>\r\n<br>\r\n<u>Abstract</u><br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ",
            "image_description": "",
            "creation_date": "2026-05-29T15:10:17",
            "last_modification_date": "2026-05-29T15:10:17",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "INN 326",
            "url_place_and_room": "https://plan.epfl.ch/?room==INN%20326",
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            "spoken_languages": [],
            "speaker": "Victor Yuefan Deng",
            "organizer": "",
            "contact": "[email protected]",
            "is_internal": "False",
            "theme": "",
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            "icalendar_url": "https://memento.epfl.ch/event/export/120909/",
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            "id": 71986,
            "title": "Fault Detection in AI Data Center Networks",
            "slug": "fault-detection-in-ai-data-center-networks-2",
            "event_url": "https://memento.epfl.ch/event/fault-detection-in-ai-data-center-networks-2",
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            "lang": "en",
            "start_date": "2026-08-06",
            "end_date": "2026-08-06",
            "start_time": "13:00:00",
            "end_time": "15:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Patrick Thiran<br>\r\nThesis advisor: Prof. Katerina Argyraki<br>\r\nThesis co-advisor: Dr Pavlos Nikolopoulos<br>\r\nCo-examiner: Prof. Nate Foster<br>\r\n<br>\r\n<u>Abstract</u><br>\r\n<br>\r\n<u>Selected papers</u>",
            "image_description": "",
            "creation_date": "2026-05-29T15:16:57",
            "last_modification_date": "2026-05-29T15:16:57",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 133",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20133",
            "url_online_room": "",
            "spoken_languages": [],
            "speaker": "Lisa Suzuki",
            "organizer": "",
            "contact": "[email protected]",
            "is_internal": "False",
            "theme": "",
            "vulgarization": {
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                "fr_label": "Tout public",
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                "en_label": "Free"
            },
            "keywords": "EDIC candidacy exam",
            "file": null,
            "icalendar_url": "https://memento.epfl.ch/event/export/120913/",
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                "code": "CONF",
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                "https://memento.epfl.ch/api/v1/mementos/48/?format=api"
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        {
            "id": 71984,
            "title": "Efficient Byzantine Agreement",
            "slug": "efficient-byzantine-agreement",
            "event_url": "https://memento.epfl.ch/event/efficient-byzantine-agreement",
            "visual_url": null,
            "visual_large_url": null,
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            "lang": "en",
            "start_date": "2026-08-18",
            "end_date": "2026-08-18",
            "start_time": "17:00:00",
            "end_time": "19:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Mika Göös<br>\r\nThesis advisor: Prof. Rachid Guerraoui<br>\r\nCo-examiner: Prof. Bryan Ford<br>\r\n<br>\r\n<u>Abstract</u><br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n ",
            "image_description": "",
            "creation_date": "2026-05-29T15:13:26",
            "last_modification_date": "2026-05-29T15:19:19",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 420",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20420",
            "url_online_room": "",
            "spoken_languages": [],
            "speaker": "Grigorii Emdin",
            "organizer": "",
            "contact": "[email protected]",
            "is_internal": "False",
            "theme": "",
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            "keywords": "EDIC candidacy exam",
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            "icalendar_url": "https://memento.epfl.ch/event/export/120911/",
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