Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models.
In this work we consider the problem of repeated hyperparameter and neural architecture search (HNAS).
Bayesian optimization (BO) is a sample efficient approach to automatically tune the hyperparameters of machine learning models.
no code implementations • 15 Dec 2020 • Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias Seeger, Cédric Archambeau
To democratize access to machine learning systems, it is essential to automate the tuning.
no code implementations • 15 Dec 2020 • Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias Seeger, Bhaskar Dutt, Laurence Rouesnel
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.
Learning attribute applicability of products in the Amazon catalog (e. g., predicting that a shoe should have a value for size, but not for battery-type at scale is a challenge.
We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints , which are constraints that need to be satisfied when accumulated over a finite number of rounds T , but can be violated in intermediate rounds.
Access to web-scale corpora is gradually bringing robust automatic knowledge base creation and extension within reach.
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as special cases.
Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed.