Fundamental Limits Of Learning In Data-Driven Problems

APA

(2022). Fundamental Limits Of Learning In Data-Driven Problems. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/fundamental-limits-learning-data-driven-problems

MLA

Fundamental Limits Of Learning In Data-Driven Problems. The Simons Institute for the Theory of Computing, Feb. 11, 2022, https://simons.berkeley.edu/talks/fundamental-limits-learning-data-driven-problems

BibTex

          @misc{ scivideos_19618,
            doi = {},
            url = {https://simons.berkeley.edu/talks/fundamental-limits-learning-data-driven-problems},
            author = {},
            keywords = {},
            language = {en},
            title = {Fundamental Limits Of Learning In Data-Driven Problems},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {feb},
            note = {19618 see, \url{https://scivideos.org/Simons-Institute/19618}}
          }
          
Yanjun Han (Simons Institute)
Source Repository Simons Institute

Abstract

What is the best we can do with the amount of data at our disposal with a given learning task? Modern learning problems---with a modest amount of data or subject to data processing constraints---frequently raise the need to understand the fundamental limits and make judicious use of the available small or imperfect data. This talk will cover several examples of learning where exploiting the key structure, as well as optimally trading between real-world resources, are vital to achieve statistical efficiency.