[IDA ML Seminar] Machine Learning Seminar, 17/4 at 15:15: Bertrand Charpentier, Pruna AI, "Uncertainty Estimation for Independent and Non-Independent Data"

[Fredrik Lindsten]

Welcome to a Machine Learning Seminar on Wednesday, April 17 at 15:15 in Alan Turing (note the place)

Uncertainty Estimation for Independent and Non-Independent Data
Bertrand Charpentier<https://sharpenb.github.io/>, Cofounder & Chief Scientist at Pruna AI
Abstract: Both practical and theoretical reasons justify why we need uncertainty estimation to build reliable machine learning models. While uncertainty estimation is expected to provide trust, safety, fairness and facilitate maintenance in real-world applications, uncertainty estimation is also highly required to represent the real physical world which is inherently non-deterministic and only partially observable. Furthermore, machine learning models must deal with various types of input data (e.g. tabular, images, graph data, sequential data) and output data (classes, real values, counts, time events) which can be assumed either independent or non-independent. While the independence assumption is convenient to represent various data types, the non-independence assumption is particularly useful to represent complex data types with network effects or time effects. In this dissertation, we look at uncertainty estimation for both independent and nonindependent data. To this end, we elaborate on three main aspects. (1) We propose desiderata capturing the desired behavior of uncertainty estimation. These desiderata cover both aleatoric and epistemic uncertainty in the presence of perturbations - in particular adversarial perturbations -, as well as network effects, or time effects. Further, we analyze the desired behavior for uncertainty estimates at both training and testing time. (2) We present a large family of new Bayesian models which provide uncertainty estimates at a low practical cost. These models demonstrate strong empirical performance and have theoretical guarantees for different data types. (3) We develop extensive metrics to evaluate uncertainty estimates for practical tasks. These experimental setups cover correct/wrong prediction detection, Out-Of-Distribution (OOD) detection, dataset shifts, and calibration metrics in the presence of (adversarial) perturbations, network effects, or time effects. Finally, we analyze the benefit of using uncertainty estimates to achieve good exploration/exploitation trade-off with high sample efficiency.

Location: Alan Turing, https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan

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