My research interests lie in the areas of information theory, statistics, and machine learning. My current focus is on understanding the implicit regularization and stability properties of neural network optimization algorithms.
In my PhD, I developed new methods to decompose information into parts that allow for a fine-grained analysis of how information is distributed over composite systems consisting of multiple interacting parts or subsystems. These methods are potentially useful in applications ranging from neuroscience and representation learning, to robotics, and cryptography.
See my Google Scholar page for an updated list of publications.
Pradeep Kr. Banerjee, Guido Montúfar (2021)
PAC-Bayes and Information Complexity
ICLR 2021 Neural Compression Workshop
Johannes Rauh*, Pradeep Kr. Banerjee*, Eckehard Olbrich, and Jürgen Jost (2019)
Unique Information and Secret Key Decompositions
Proceedings of the IEEE International Symposium on Information Theory
arXiv | ISIT 2019
Pradeep Kr. Banerjee, Eckehard Olbrich, Jürgen Jost, and Johannes Rauh (2018)
Unique Informations and Deficiencies
Proceedings of the 56th Annual Allerton Conference on Communication, Control and Computing
arXiv | Allerton 2018
Service: Reviewer for ICML, ISIT, IEEE Transactions on Neural Networks and Learning Systems.