appliedAI — TransferLab

We identify, test and disseminate established and emerging techniques in machine learning in order to provide practitioners with the best tools for their applications

What we do

Trainings

Our hands-on, one to three day workshops cover fundamental and advanced topics in machine learning. We illustrate concepts with abundant real-world examples backed by rigorous theory.

Playbooks

Guided walkthroughs and industry-grade implementations of common tasks in machine learning

Reviews

In-depth analyses of the recently possible, as well as established techniques essential to the daily application of machine learning

Showcases

Visually rich, accessible explanations of novel concepts in machine learning, using open data

Software

Code libraries developed during our research and supporting our work, open sourced

Explore our work

Review Partner area

Data valuation techniques. Applications, advances and problems

The attribution of value to data points can help in data collection, active learning, gauging model robustness, improving model performance …

May 15, 2021

Review Partner area

Calibration of Classifiers

We discuss the calibration of probabilistic classifiers, i.e. the question of whether the output vectors of such classifiers accurately …

Apr 29, 2021

Software

pyDVL: the python Data Valuation Library

pyDVL strives to offer reference implementations of algorithms for data valuation with a common interface compatible with sklearn and a …

Apr 3, 2021

Training

Calibration of modern classifiers

An introduction to the pitfalls of uncalibrated classifiers and modern techniques for (re)calibration.

Come to our seminar

Data (e)valuation and model interpretation: a game theoretic approach

May 20, 2021

Attributing a “fair” (for some definition of fair) value to training samples has multiple applications. It can be used to investigate or improve data sources, but it can also help detect outliers and to investigate how certain features and the values thereof influence global model performance. It is also possible to improve model performance: removing points of low value can decrease model error.

Series: Data Valuation

Introduction to simulation-based inference

Dec 17, 2020

Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator’s parameters so that its outputs match data is challenging.

Álvaro Tejero-Cantero

Hamiltonian Monte Carlo Sampling

Dec 3, 2020

Metropolis, Slice or Gibbs sampling tend to be inefficient when applied in high dimensions due to their local random walk behaviour. Hamiltonian Monte Carlo (HMC) was developed around a geometrical understanding of the target distribution, borrowing concepts from physics to generate transitions and exploring the target distribution. Ultimately, moving more rapidly through the target distribution.

Series: Bayesian ML and Probabilistic Programming

Maternus Herold

Bayesian optimal experiment design (2 of 2)

Nov 26, 2020

This is the second of a two talks providing an introduction to Bayesian optimal experimental design (BOED) and how it relates to exploration in reinforcement learning and control. The first part dealt with the theoretical foundations for BOED. This talk completes the topic by presenting an application in control, which was created during my master thesis at TUDa.

Markus Semmler

Bayesian optimal experiment design (1 of 2)

Nov 19, 2020

These two talks give an introduction to Bayesian optimal experimental design (BOED) and how it relates to exploration in reinforcement learning and control. The first part deals with the theoretical foundations for BOED. Different design types are covered, e.g. space filling designs, alphabetic designs and Bayesian designs. Afterwards it is shown how Bayes' theorem relates to the mutual information (MI) or equally the expected information gain (EIG).

Markus Semmler

Read our blog

Natural, Trust Region and Proximal Policy Optimization

We present an overview of the theory behind three popular and related algorithms for gradient based policy optimization: natural policy …

Aug 10, 2021

Cross-validation: what does it estimate?

In Cross-validation: what does it estimate and how well does it do it? Bates et al. focus on cross validation for error estimation and show …

Jun 11, 2021

Blog

Solving PDEs With Neural Networks

We discuss some current trends in applying ML to the solution of differential equations, and the difficulties faced. We focus on a family of …

Jan 30, 2021

What we do

Trainings

Our hands-on, one to three day workshops cover fundamental and advanced topics in machine learning. We illustrate concepts with abundant real-world examples backed by rigorous theory.

Playbooks

Guided walkthroughs and industry-grade implementations of common tasks in machine learning

Reviews

In-depth analyses of the recently possible, as well as established techniques essential to the daily application of machine learning

Showcases

Visually rich, accessible explanations of novel concepts in machine learning, using open data

Software

Code libraries developed during our research and supporting our work, open sourced

Explore our work

Data valuation techniques. Applications, advances and problems

The attribution of value to data points can help in data collection, active learning, gauging model robustness, improving model performance …

Calibration of Classifiers

We discuss the calibration of probabilistic classifiers, i.e. the question of whether the output vectors of such classifiers accurately …

pyDVL: the python Data Valuation Library

pyDVL strives to offer reference implementations of algorithms for data valuation with a common interface compatible with sklearn and a …

Calibration of modern classifiers

An introduction to the pitfalls of uncalibrated classifiers and modern techniques for (re)calibration.

Come to our seminar

Read our blog

Natural, Trust Region and Proximal Policy Optimization

We present an overview of the theory behind three popular and related algorithms for gradient based policy optimization: natural policy …

Cross-validation: what does it estimate?

In Cross-validation: what does it estimate and how well does it do it? Bates et al. focus on cross validation for error estimation and show …

Solving PDEs With Neural Networks

We discuss some current trends in applying ML to the solution of differential equations, and the difficulties faced. We focus on a family of …

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