Rethinking evaluation for machine learning models

Add to your list(s) Download to your calendar using vCal

• Adrian Goldwaser & Shoaib Siddiqui
• Wednesday 01 June 2022, 11:00-12:30
• Cambridge University Engineering Department, CBL Seminar room BE4-38.

If you have a question about this talk, please contact Elre Oldewage.

Machine learning research combines both theoretical and empirical approaches. With the explosion of methods in the recent past and more focus on empirical rather than theoretically driven research, this demands even more care in evaluating these methods to enable a fair comparison. In this discussion session, we aim to highlight and discuss some of these best practices and common pitfalls of evaluating machine learning models. Given that machine learning is a relatively new field as compared to others, this requires a careful dialogue in terms of establishing best practices as compared to a set of prebaked ideas. The aim of this reading session is to enable a more careful and thorough discussion on these topics within CBL .

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge University Engineering Department Talks
• Cambridge University Engineering Department, CBL Seminar room BE4-38
• Cambridge talks
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• ML
• Machine Learning Reading Group
• Machine Learning Reading Group @ CUED
• Machine Learning Summary
• Quantum Matter Journal Club
• Required lists for MLG
• School of Technology
• Simon Baker's List
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.