2.3k citations · 4.6k across the 26 of their papers we have counts for
4 papers · 1 filter
With Little Power Comes Great Responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal +3
Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored…
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
Shagun Sodhani, Mayoore S. Jaiswal, Lauren Baker +5
This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019. The goal of the report is t…
TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
Joshua Romoff, Peter Henderson, David Kanaa +4
We investigate whether Jacobi preconditioning, accounting for the bootstrap term in temporal difference (TD) learning, can help boost performance of adaptive optimizers. Our method…
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Peter Henderson, Jieru Hu, Joshua Romoff +3
Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this ea…