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Found 12 papers, 3 papers with code
The Vizier Gaussian Process Bandit Algorithm
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a large-scale service.
SmartChoices: Augmenting Software with Learned Implementations
In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior.
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts.
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Single-objective black box optimization (also known as zeroth-order optimization) is the process of minimizing a scalar objective $f(x)$, given evaluations at adaptively chosen inputs $x$.
Gradientless Descent: High-Dimensional Zeroth-Order Optimization
Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$.
Google Vizier: A Service for Black-Box Optimization
Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand.
Hidden Technical Debt in Machine Learning Systems
Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly.
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments
How should we dynamically rank information sources to maximize the value of the ranking?
Near-Optimal Bayesian Active Learning with Noisy Observations
In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perform near-optimally.
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge.
Online Learning of Assignments
Which ads should we display in sponsored search in order to maximize our revenue?
An Online Algorithm for Maximizing Submodular Functions
We present an algorithm for solving a broad class of online resource allocation problems.
