Empirical Inference for Intelligent Systems

Empirical Inference is the process of drawing conclusions from observational data. For instance, the data can be measurements from an experiment, which are used by a researcher to infer a scientific law. Another kind of empirical inference is performed by living beings, continuously recording data from their environment and carrying out appropriate actions. Do these problems have anything in common, and are there underlying principles governing the extraction of regularities from data? What characterizes hard inference problems, and how can we solve them?

The talk will describe some of the main ideas and problems of machine learning. It will provide illustrative examples of real-world machine learning applications. Time permitting, it will touch upon recent developments in estimating causal structures, and/or probabilistic programming.

Speaker Details

My scientific interests are in the field of inference from empirical data, in particular machine learning and perception ( Department of Empirical Inference). In particular, I study kernel methods for extracting regularities from possibly high-dimensional data. These regularities are usually statistical ones, however, in recent years I have also become interested in methods for finding causal regularities.

Date:
Speakers:
Bernhard Schölkopf

    • Portrait of Jeff Running

      Jeff Running