Microsoft Technology Licensing, LLC

Thomas William Finley

Mohammad Zeeshan Siddiqui

Gary Shon Katzenberger

Costin I Eseanu

Eric Anthony Erhardt

Ivan Matantsev

Yael Dekel

Ran Gilad-Bachrach

Sarthak D Shah

Mikhail Bilenko

Sambavi Muthukrishnan

Pengtao Xie

Bhuvan Middha

Tanton Holt Gibbs

Petro Luferenko

Pete Luferenko

Herbert De Melo Duarte

Dehu Qi

A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

Categorical feature enhancement mechanism for gradient boosting decision tree

Methods, systems, and computer products are herein provided for lazy evaluation of input data by a machine learning (ML) framework. An ML pipeline receives input data and compiles a chain of operators into a chain of dataviews configured for lazy evaluation of the input data. Each dataview in the chain represents a computation over data as a non-materialized view of the data. The ML pipeline receives a request for column data and selects a chain of delegates comprising one or more delegates for one or more dataviews in the chain to fulfill the request. The ML pipeline processes the input data with the selected chain of delegates. The ML pipeline performs delegate chaining on a dataview. A feature value for a feature column of the dataview is determined based on the delegate chaining and provided to an ML algorithm to predict column data.

Efficient streaming based lazily-evaluated machine learning framework

Embodiments provide a machine learning framework that enables developers to author and deploy machine learning pipelines into their applications regardless of the programming language in which the applications are structured. The framework may provide a programming language-specific API that enables the application to call a plurality of operators provided by the framework. The framework provides any number of APIs, each for a different programming language. The pipeline generated via the application is represented as an execution graph comprising node(s), where each node represents a particular operator. When a pipeline is submitted for execution, calls to the operators are detected, and nodes corresponding to the operators are generated for the execution graph. Once the execution graph is complete, the execution graph is provided to an execution engine, which reconstructs a corresponding execution graph that is structured in accordance with the operators' programming language and executes the reconstructed execution graph.

Optimized machine learning pipeline execution based on execution graphs

Embodiments described herein are directed to methods and systems for performing neural network computations on encrypted data. Encrypted data is received from a user. The encrypted data is encrypted with an encryption scheme that allows for computations on the ciphertext to generate encrypted results data. Neural network computations are performed on the encrypted data, using approximations of neural network functions to generate encrypted neural network results data from encrypted data. The approximations of neural network functions can approximate activation functions, where the activation functions are approximated using polynomial expressions. The encrypted neural network results data are communicated to the user associated with the encrypted data such that the user decrypts the encrypted data based on the encryption scheme. The functionality of the neural network system can be provided using a cloud computing platform that supports restricted access to particular neural networks.

Neural networks for encrypted data

Methods, systems, and computer-readable media for a method of propagating signals across a web graph. A signal describes a document or otherwise provides useful information about a document in a web graph. A web graph is a collection of documents that are related to one another through links, such as hyperlinks. The signals are propagated in the sense that information from the related pages is associated with the target page even though the information may not be directly found in the target page. This information may then be used by a search engine to determine that a particular page is relevant to a search query.

Propagating signals across a web graph

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Bellevue, WA, United States of America

Thomas William Finley