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13

Feb

Link to Simplifying Debiased Inference via Automatic Differentiation and Probabilistic Programming

AI Keynote Series  •  13.02.2025  •  Online via Zoom

10:00 am - 11:30 am

Simplifying Debiased Inference via Automatic Differentiation and Probabilistic Programming

Alex Luedtke, Department of Statistics, University of Washington

The speaker would introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. ‘Dimple’ takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known …

13

Feb

Link to Generative AI With Diffusion Models

Virtual Workshop on Generative AI with Diffusion Models  •  13.02.2025  •  Virtual

9:00 am - 5:00 pm

Generative AI With Diffusion Models

Virtual Workshop From Nvidia

Generative AI plays a significant role across industries. In this course, learners will take a deeper dive into denoising diffusion models.

12

Feb

Link to Fundamentals of Accelerated Data Science With RAPIDS

Virtual Workshop on Data Science with RAPIDS  •  12.02.2025  •  Virtual

9:00 am - 5:00 pm

Fundamentals of Accelerated Data Science With RAPIDS

Virtual Workshop From Nvidia

In this workshop, you’ll learn how to build and execute end-to-end GPU-accelerated data science workflows. Using the RAPIDS™-accelerated data science libraries, you’ll apply a wide variety of GPU-accelerated machine learning algorithms for data analysis.

06

Feb

Link to MCML Stammtisch - February Edition

MCML Stammtisch  •  06.02.2025  •  Gasthaus FUX, Türkenstraße 38, 80799 München

6:00 pm - 8:00 pm

MCML Stammtisch - February Edition

Join Us for an Evening of Connection and Conversation

We heartily invite to the next MCML-Stammtisch edition, taking place on Thursday, February 6th, at 6 pm, at Gasthaus FUX, Türkenstraße 38, Munich (very close to “U3 Universität”). Happy to see you there.

06

Feb

Link to Causal Discovery: What Can We Learn From Heterogeneous Noise?

Lecture  •  06.02.2025  •  LMU Munich, Ludwigstr. 28 VG/II; Room 211b

11:00 am - 12:00 pm

Causal Discovery: What Can We Learn From Heterogeneous Noise?

Alexander Marx, TU Dortmund

Causal discovery aims to learn causal networks, i.e., directed acyclic graphs (DAGs), from observational data. Although the problem is not feasible in the most general form, as it is not possible to infer causal relations from pure correlations, we can define structural assumptions about the functional relations underlying our observed system to …

06

Feb


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07

Feb

Link to AI, Science, and Society Conference

Conference  •  06.02.2025 - 07.02.2025  •  Institut Polytechnique de Paris, École Polytechnique Campus, Route de Saclay, 91120 Palaiseau, France

9:30 am - 5:30 pm

AI, Science, and Society Conference

Daniel Cremers, MCML

We are pleased to announce that Daniel Cremers, Director of the MCML, will be a featured speaker at the upcoming conference titled “AI, Science, and Society: Connections, Collectives, and Collaboration.” This interdisciplinary event is scheduled to take place on February 6 and 7, 2025, at the Institut Polytechnique de Paris.

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