Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Fiddler Labs Inc

Krishnaram Kenthapadi

Stuart MacDonald Ambler

Ryan Wade Sandler

Ahsan Latif Chudhary

Parul Jain

Anthony Duane Duerr

Stephanie Chou

Huan Hoang

Anitha Kannan

Bo Zhao

Nina Mishra

Kaushik Rangadurai

Fedor Vladimirovich Borisyuk

Joseph Florencio

Yan Liu

Sreenivas Gollapudi

Zachary Mason Roth

Michele Donini

Rakesh Agrawal

Liang Zhang

Alan D Halverson

Ganesh Venkataraman

Yi Li

Qi Guo

Gungor Polatkan

Rohan Ramanath

Sahin Cem Geyik

Cagri Ozcaglar

Muhammad Bilal Zafar

Victor Chen

Cedric Philippe Archambeau

Sergul Aydore

Luca Melis

Abhimanyu Das

Prateek Janardhan

Xinyu Liu

Lu Zheng

William Brown

Michael Kearns

Aaron Roth

Amaresh Ankit Siva

Edoardo M Airoldi

Tyler Stephen Hill

Pinar Altin Yilmaz

Deepak Agarwal

Ariel Fuxman

John Christopher Shafer

Songtao Guo

Alexandros Ntoulas

Aman Grover

Umar Syed

Siyu You

David J Stein

Aleksandrs Slivkins

Christopher Matthew Degiere

Kevin Kao

Valerio Perrone

Sanjiv Ranjan Das

Jeffrey Warren Lee

Cory Scott

Aleksandra Korolova

Michaela Hardt

Keerthan Harish Vasist

Pedro L Larroy

Ashish Rajendra Rathi

Nathalie Rauschmayr

Dylan Slack

Marios Kokkodis

Nathaniel Dion Parrish

Joshua G Rubin

Bashir Rastegarpanah

Amalendu K Iyer

Automatic failure diagnosis and correction may be performed on trained machine learning models. Input data that causes a trained machine learning model may be identified in order to determine different model failures. The model failures may be clustered in order to determine failure scenarios for the trained machine learning model. Examples of the failure scenarios may be generated and truth labels for the example scenarios obtained. The examples and truth labels may then be used to retrain the machine learning model to generate a corrected version of the machine learning model.

Automatic failure diagnosis and correction in machine learning models

Explanation jobs may be performed for computer vision tasks. A request to execute an explanation job for a computer vision machine learning model may be received. The execution job may be performed, including extracting different features from the image, determining the respective relative importance values of the different features on inferences generated by the computer vision machine learning model. The result of the explanation job, including the generated heat maps may be provided.

Performing explanation jobs for computer vision tasks

A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Global explanations of machine learning model predictions for input containing text attributes

An algorithm releases answers to very large numbers of statistical queries, e.g., k-way marginals, subject to differential privacy. The algorithm answers queries on a private dataset using simple perturbation, and then attempts to find a synthetic dataset that most closely matches the noisy answers. The algorithm uses a continuous relaxation of the synthetic dataset domain which makes the projection loss differentiable, and allows the use of efficient machine learning optimization techniques and tooling. Rather than answering all queries up front, the algorithm makes judicious use of a privacy budget by iteratively and adaptively finding queries for which relaxed synthetic data has high error, and then repeating the projection. The algorithm is effective across a range of parameters and datasets, especially when a privacy budget is small or a query class is large.

Generating relaxed synthetic data using adaptive projection

A set of clusters from a first set of vector representations (VRs) is identified. A center associated with each cluster from the set of clusters to generate a set of centers is determined. For each VR from the first set of VRs, and to generate a first set of distributions, a distribution of that VR is determined that indicates, for each center from the set of centers, similarity between that VR and that center. For each VR from a second set of VRs, and to generate a second set of distributions, a distribution of that VR is determined that indicates, for each center from the set of centers, similarity between that VR and that center. A set of divergence metrics associated with the first set of VRs and the second set of VRs are computed based on comparing the first set of distributions and the second set of distributions.

System and method for data drift detection to improve data integrity for data within a database

Hyperparameters for tuning a machine learning system may be optimized for fairness using Bayesian optimization with constraints for accuracy and bias. Hyperparameter optimization may be performed for a received training set and received accuracy and fairness constraints. Respective probabilistic models for accuracy and bias of the machine learning system 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 constrained expected improvement on the respective models, training the machine learning system using the identified values to determine measures of accuracy and bias, and updating the respective models using the determined measures.

Hyperparameter optimization using fairness constraints

Aspects of the present disclosure relate to cryptography. In particular, example embodiments relate to computing a relationship between private data of a first entity and private data of a second entity, while preserving privacy of the entities and preventing inter-entity data sharing. A server includes a first component to compute an intersection of two datasets, without directly accessing either dataset. The server includes a second component to compute a relationship, such as a regression, between data in the first dataset and data in the second dataset, without directly accessing either dataset.

Relating data while preventing inter-entity data sharing

In an example, each of a plurality of members of social networking service is mapped to a weighted skill vector, each weighted skill vector including a list of skills for the member with an associated weight indicating strength of the skill. Members of the social networking service that belong to an industry are aggregated to obtain a weighted matrix of members and skills along with compensation vectors indicating compensation for each of the members in the matrix. The weighted matrix of users and skills and corresponding compensation vectors is used to train a machine learning skill monetary value prediction model to output a predicted monetary value for one or more skills contained in a candidate vector fed to the machine learning skill monetary value prediction model.

Machine learning technique for recommendation of courses in a social networking service based on confidential data

Techniques for implementing a learning semantic representations of sparse entities using unsupervised embeddings are disclosed herein. In some embodiments, a computer system accesses corresponding profile data of users indicating at least one entity of a first facet type associated with the user, and generating a graph data structure comprising nodes and edges based on the accessed profile data, with each node corresponding to a different entity indicated by the accessed profile data, and each edge directly connecting a different pair of nodes and indicating a number of users whose profile data indicates both entities of the pair of nodes. The computer system generating a corresponding embedding vector for the entities based on the graph data structure using an unsupervised machine learning algorithm.

Unsupervised learning of entity representations using graphs

A member profile including a vector containing a field for each of a plurality of skills and a rating of one or more of the skills in the vector for a member of a social networking service is obtained. A first distance indicating a vector distance between the vector of the member profile and a vector of a hypothetical member profile representing the perfect job candidate is obtained. A hypothetical member profile for the member is created by combining the vector of the member profile with the indication of how each of the one or more skills is improved through taking the course from course information. A second distance between the member and the hypothetical perfect candidate for the job is obtained, and the difference between the first distance and the second distance is calculated to determine an estimate of how much the course will increase the member's job chances.

Estimating effects of courses

In an example, one or more member profiles and corresponding elapsed times indicating, for each of the one or more member profiles, how long the corresponding member of a social networking service took to respond to a request for confidential data with a submission of confidential data are obtained. Then a first set of one or more features are extracted from the one or more member profiles. The first set of one or more features and corresponding elapsed times are fed into a machine learning algorithm to train a confidential data response time prediction model to output a predicted time to respond to a request for confidential data for a candidate member profile. A second set of one or more features are obtained from a candidate member profile and fed to the confidential data response time prediction model, outputting the predicted time to respond to a request for confidential data.

Estimating the conditional response time to a request for confidential data

Methods, systems, and computer programs are presented for searching job postings for a member of a social network based on transitions from educational institutions to companies. A method includes determining educational-company transition scores indicating a transition probability from educational institution to company. The method identifies jobs based on a search performed for a first member, with a profile including one or more educational institutions, each job associated with a respective company. A server determines a member-company transition score based on the educational-company transition scores of the educational institutions in the profile. For each job, a job affinity score is determined based on data of the job and the profile of the first member. The server ranks the jobs based on the member-company transition score of the company of the job and the job affinity score. Some of the ranked jobs are presented to the first member based on the ranking.

Job search based on member transitions from educational institution to company

In an example, one or more job postings, as well as corresponding confidential data values, are obtained from a social networking service. A first set of one or more features are extracted from the one or more job postings. The first set of one or more features and corresponding confidential data values are fed into a machine learning algorithm to train a confidential data value prediction model to output a predicted confidential data value for a candidate job posting. Then, the candidate job posting is obtained and a second set of one or more features are extracted from the candidate job posting. The extracted second set of one or more features is fed to the confidential data value prediction model, outputting the predicted confidential data value.

Machine learning model to estimate confidential data values based on job posting

Techniques for processing search queries are described. Consistent with some embodiments, a computer system generates a profile vector representation for each of several user profiles based on the user profile data of the user profiles, and then stores the vector representations. A subsequent query is processed to generate a query vector representation for the query. A neural network is used to generate a similarity score for each pairing of the query vector representation and a profile vector representation. Finally, some number of user profiles having the highest similarity scores are provided as search results.

Techniques for querying user profiles using neural networks

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Sunnyvale, CA, United States of America

Krishnaram Kenthapadi