Learn why Two Sigma standardized on our optimization solution to scale their research efforts. Read More
All Model Types, Modeling Best Practices
Experiment, Suggestion, Observation: How we named our HPO API objectsBackground Hyperparameter optimization, hyperparameter tuning, model tuning, model optimization, and occasionally, model selection, all refer to roughly the same thing: we have a model, the model has hyperparameters, and we want to find “the best” hyperparameters to maximize the performance of our model.
Our research team at SigOpt has been very fortunate to be able to collaborate with outstanding researchers around the world, including through our internship program.
Advanced Optimization Techniques, Machine Learning
AutoML at ICML 2018Here at SigOpt, we provide a tuning platform for practitioners to develop machine learning models efficiently. To stay at the cutting edge, we regularly attend conferences such as the International Conference on Machine Learning (ICML).
All Model Types, SigOpt 101
SigOpt Fall UpdateThis is the first edition of our new quarterly newsletter. In these updates, we will discuss newly released features, showcase content we have produced, published, or been cited in, and share interesting machine learning research that our research team has found. We hope you find these valuable and informative!
All Model Types, Augmented ML Workflow
SigOpt is Now Available on AWS MarketplaceToday we announce the availability of SigOpt on AWS Marketplace. Now, with a single click, data scientists and researchers can access SigOpt’s powerful optimization-as-a-service platform designed to automatically fine-tune their machine learning (ML) and artificial intelligence (AI) workloads.
All Model Types, Modeling Best Practices
Clustering Applied to Acquisition FunctionsWe have revisited one of the traditional acquisition functions of Bayesian optimization, the upper confidence bound and looked to a slightly different interpretation to better inform optimization of it. In our article, we present a number of examples of this clustering-guided UCB method being applied to various optimization problems.
Advanced Optimization Techniques, All Model Types
Building a Better Mousetrap via Multicriteria Bayesian OptimizationIn this post, we want to analyze a more complex situation in which the parameters of a given model produce a random output, and our multicriteria problem involves maximizing the mean while minimizing the variance of that random variable.
All Model Types, SigOpt 101
We Raised $6.6 Million To Amplify Your ResearchToday is a big day at SigOpt. Since the seed round we secured last year, we’ve continued to build toward our mission to ‘optimize everything,’ and are now helping dozens of companies amplify their research and development and drive business results with our cloud Bayesian optimization platform.
All Model Types, Training & Tuning
Breaking Free of the GridIn this post, we review the history of some of the tools implemented within SigOpt, and then we discuss the original solution to this black box optimization problem, known as full factorial experiments or grid search. Finally, we compare both naive and intelligent grid-based strategies to the latest advances in Bayesian optimization, delivered via the SigOpt platform.
Advanced Optimization Techniques, Deep Learning
Deep Neural Network Optimization with SigOpt and Nervana Cloud In this post, we will detail how to use SigOpt with the Nervana Cloud and show results on how SigOpt and Nervana are able to reproduce, and beat, the state of the art performance in two papers.
Applied AI Insights, Simulations & Backtests
Winning on Wall Street: Tuning Trading Models with Bayesian OptimizationSigOpt provides customers the opportunity to build better machine learning and financial models by providing users a path to efficiently maximizing key metrics which define their success. In this post we demonstrate the relevance of model tuning on a basic prediction strategy for investing in bond futures.
Advanced Optimization Techniques, Deep Learning
TensorFlow ConvNets on a Budget with Bayesian OptimizationIn this post on integrating SigOpt with machine learning frameworks, we will show you how to use SigOpt and TensorFlow to efficiently search for an optimal configuration of a convolutional neural network (CNN).
Advanced Optimization Techniques, Deep Learning
Unsupervised Learning with Even Less Supervision Using Bayesian OptimizationIn this post on integrating SigOpt with machine learning frameworks, we will show you how to use SigOpt and XGBoost to efficiently optimize an unsupervised learning algorithm’s hyperparameters to increase performance on a classification task.
All Model Types, Modeling Best Practices
Intuition behind Gaussian ProcessesSigOpt allows experts to build the next great model and apply their domain expertise instead of searching in the dark for the best experiment to run next. With SigOpt you can conquer this tedious, but necessary, element of development and unleash your experts on designing better products with less trial and error.
All Model Types, Modeling Best Practices
Approximation of DataSigOpt provides customers the opportunity to define uncertainty with their observations and using this knowledge we can balance observed results against their variance to make predictions and identify the true behavior behind the uncertainty.
All Model Types, Modeling Best Practices
Intuition Behind Covariance KernelsGaussian processes are powerful because they allow you to exploit previous observations about a system to make informed, and provably optimal, predictions about unobserved behavior. They do this by defining an expected relationship between all possible situations; this relationship is called the covariance and is the topic of this post.
Applied AI Insights, Simulations & Backtests
Making a Better Airplane using SigOpt and RescaleRescale gives users the dynamic computational resources to run their simulations and SigOpt provides tools to optimize them. By using Rescale’s platform and SigOpt’s tuning, efficient cloud simulation is easier than ever.