1 code implementation • 24 Nov 2023 • Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths
Most applications of machine learning to classification assume a closed set of balanced classes.
1 code implementation • 22 Nov 2023 • Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task.
1 code implementation • 17 Nov 2023 • Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths
Bayesian filtering approximates the true underlying behavior of a time-varying system by inverting an explicit generative model to convert noisy measurements into state estimates.
no code implementations • NeurIPS 2023 • Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard Zemel
Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning.
1 code implementation • 27 Dec 2022 • Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel
In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor.