Seminar | TransferLab — appliedAI Institute

Seminar

The TransferLab Seminar is a platform where researchers and engineers share and discuss recent advances in AI/ML, striking a balance between accessibility and mathematical depth.

We organize comprehensive talks on ML, RL, DL and statistics, emphasizing both theoretical understanding and practical implementation. These range from introductory overviews to recent developments - and are open to all interested individuals!

Each talk, lasting about 45min followed by around 15min discussion, focuses on a specific topic related to our ongoing series topics. To stay informed, follow the appliedAI Institute on LinkedIn. If you’re interested in presenting your work, contact us. Recordings of past seminars are available on YouTube.

Upcoming Talks

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Previous Talks

Duality in RL and the [*]-DICE Family (part 2) - Robert Müller (appliedAI Institute)

Oct 29, 2020

The goal of reinforcement learning is to find an optimal policy. Two commonly used classes of methods are i) policy gradient and ii) dynamic programming based methods. In this talk I will describe an alternative solution strategy based on LP duality. It is based on the duality of Q-values and state-action visitations.

Bayesian ML and Probabilistic Programming 2: MCMC, Bayesian Regression and Causality - Mustafa Gaja (appliedAI Initiative)

Oct 22, 2020

In this talk, we lay the ground for Markov Chain Monte Carlo (MCMC) methods to inferring the posterior distribution, after looking into the limitations posed by the analytical approach. We demonstrate how MCMC works and use it to solve a Bayesian regression problem and introduce concepts related to causal inference. Before doing so, we have a quick tutorial on using Pyro as a probabilistic programming language.

Probabilistic Models

Duality in RL and the [*]-DICE Family (part 1) - Robert Müller (appliedAI Institute)

Oct 15, 2020

The goal of reinforcement learning is to find an optimal policy. Two commonly used classes of methods are i) policy gradient and ii) dynamic programming based methods. In this talk I will describe an alternative solution strategy based on LP duality. It is based on the duality of Q-values and state-action visitations.

Normalizing Flows for Policy Representation in Reinforcement Learning - Frederik Heetmeyer

Aug 20, 2020

In part 3 on Normalizing Flows, we will discuss how Reinforcement Learning could benefit from this class of methods for policy representation: Using Normalizing Flows to represent stochastic policies in reinforcement learning: I assume that everyone has a basic understanding of RL. Therefore, I will only briefly review the fundamentals (state, action, environment, …)

Generative Models

Frederik Heetmeyer

Emerging Applications of Normalizing Flows in Reinforcement Learning - Frederik Heetmeyer

Aug 13, 2020

This is Part 2 on Normalizing Flows.

Generative Models

Frederik Heetmeyer

An Introduction to Normalizing Flows - Frederik Heetmeyer

Jul 30, 2020

Normalizing Flows are a new class of methods that transform a simple input distribution (e.g. Gaussian) into a complex distribution through a series of invertible mappings.

Generative Models

Frederik Heetmeyer

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Our Block Seminars

Methods and issues in Explainable AI

This seminar series explores the landscape of Explainable Artificial Intelligence (XAI) beyond the content in our training on XAI, and through a comprehensive selection of papers in the field. By the end of this series, participants will …

May 17, 2023

Uncertainty quantification for neural networks

In this seminar series, we review seminal and recent papers on uncertainty quantification that didn’t make it into our training on Bayesian ML.

Oct 22, 2022