公开(公告)号：US12165082B1

公开(公告)日：2024-12-10

申请号：US16915610

申请日：2020-06-29

Applicant: Amazon Technologies, Inc.

Inventor： Giovanni Zappella ,  Valerio Perrone ,  Iaroslav Shcherbatyi ,  Rodolphe Jenatton ,  Cedric Philippe Archambeau ,  Matthias Seeger

IPC: G06N7/01 ,  G06N3/08 ,  G06N20/00

Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

公开(公告)号：US11593704B1

公开(公告)日：2023-02-28

申请号：US16455356

申请日：2019-06-27

Applicant: Amazon Technologies, Inc.

Inventor： Rodolphe Jenatton ,  Miroslav Miladinovic ,  Valerio Perrone

IPC: G06N20/00 ,  G06N3/08 ,  G06N7/00 ,  G06N7/02 ,  G06F16/2455

Abstract: Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.

公开(公告)号：US10853735B1

公开(公告)日：2020-12-01

申请号：US15174108

申请日：2016-06-06

Applicant: Amazon Technologies, Inc.

Inventor： Yu Gan ,  Cédric Philippe Archambeau ,  Rodolphe Jenatton ,  Jim Huang ,  Fabian Lutz-Frank Wauthier

IPC: G06N7/00 ,  G06F3/0484 ,  G06F16/2457

Abstract: Systems, methods, and computer-readable media are disclosed for maximizing quantifiable user interaction via modification of adjustable parameters. In one embodiment, an example method may include determining a first output to maximize, where the first output is a function of a first adjustable parameter and a second adjustable parameter, determining first data comprising a first actual value of the first output when the first adjustable parameter is set to a first value and the second adjustable parameter is set to a second value, and determining a first predictive model that generates a first predicted value of the first output. Example methods may include determining, using the first predictive model, a third value for the first adjustable parameter and a fourth value for the second adjustable parameter to maximize the first predicted value, and sending the third value and the fourth value.

公开(公告)号：US20250013899A1

公开(公告)日：2025-01-09

申请号：US18888047

申请日：2024-09-17

Applicant: Amazon Technologies, Inc.

Inventor： Giovanni Zappella ,  Valerio Perrone ,  Iaroslav Shcherbatyi ,  Rodolphe Jenatton ,  Cedric Philippe Archambeau ,  Matthias Seeger

IPC: G06N7/01 ,  G06N3/08 ,  G06N20/00

Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

公开(公告)号：US10257275B1

公开(公告)日：2019-04-09

申请号：US14923237

申请日：2015-10-26

Applicant: Amazon Technologies, Inc.

Inventor： Leo Parker Dirac ,  Rodolphe Jenatton

IPC: H04L29/08 ,  G06N5/02

Abstract: An optimizer for a software execution environment determines an objective function and permitted settings for various tunable parameters of the environment. To represent the execution environment, the optimizer generates a Bayesian optimization model employing Gaussian process priors. The optimizer implements a plurality of iterations of execution of the model, interleaved with observation collection intervals. During a given observation collection interval, tunable parameter settings suggested by the previous model execution iteration are used in the execution environment, and the observations collected during the interval are used as inputs for the next model execution iteration. When an optimization goal is attained, the tunable settings that led to achieving the goal are stored.

公开(公告)号：US10049375B1

公开(公告)日：2018-08-14

申请号：US14666023

申请日：2015-03-23

Applicant: Amazon Technologies, Inc.

Inventor： Giovanni Zappella ,  Marcel Ackermann ,  Rodolphe Jenatton ,  David Spike Palfrey ,  Samuel Theodore Sandler

IPC: G06Q30/02 ,  G06F17/30

Abstract: A system is disclosed that identifies early adopter users by creating a directed graph of item access information for an item category and performing a page rank type process on the item access information. This directed graph may be created in a reverse temporal order. The early adopter users can be identified as the users with nodes in the directed graph that have a threshold number or rate of incoming links directly or indirectly pointing towards the nodes. Using the early adopter users as a sample, systems herein can determine whether to recommend an item based on the popularity of the item with respect to the early adopter users. Further, systems herein can determine an inventory level to maintain for an item based on the popularity of the item with respect to the early adopter users.