We identify, test and disseminate established and emerging techniques in machine learning in order to provide practitioners with the best tools for their applications
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Data (e)valuation and model interpretation: a game theoretic approach
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.
Introduction to simulation-based inference
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.
Hamiltonian Monte Carlo Sampling
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.
Bayesian optimal experiment design (2 of 2)
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.
Bayesian optimal experiment design (1 of 2)