Universal schemes for information processing tasks such as compression, communication, sequential probability assignments, prediction, denoising, and filtering. Characterization of performance limits in stochastic, semi-stochastic, and individual sequence settings. Ziv-Lempel compression as an end goal, and as an engine for other information processing tasks. Trade Offs between performance (e.g. accuracy), amounts of data processed, and computation.
3 units · Letter or Credit/No Credit
Universal schemes for information processing tasks such as compression, communication, sequential probability assignments, prediction, denoising, and filtering. Characterization of performance limits in stochastic, semi-stochastic, and individual sequence settings. Ziv-Lempel compression as an end goal, and as an engine for other information processing tasks. Trade Offs between performance (e.g. accuracy), amounts of data processed, and computation.
Offered in Spring 2027 at Stanford University.