Xu U Miao, 481608 Belvoir Dr, Los Altos, CA 94024

2019035, Nov 21, 2019

May 21, 2019

16/418848

- Menlo Park CA, US
Xu MIAO - Los Altos CA, US
Zhenjie Zhang - Fremont CA, US
Masayo IIDA - Mountain View CA, US
Maran NAGENDRAPRASAD - San Ramon CA, US

G06K 9/62
G06N 3/04
G06F 3/0482

One embodiment of the present invention sets forth a technique for processing training data for a machine learning model. The technique includes training the machine learning model using training data comprising a set of features and a set of original labels associated with the set of features. The technique also includes generating multiple groupings of the training data based on internal representations of the training data in the machine learning model. The technique further includes replacing, in a first subset of groupings of the training data, a first subset of the original labels with updated labels based at least on occurrences of values for the original labels in the first subset of groupings.

2018028, Oct 4, 2018

Apr 3, 2017

15/477782

- Sunnyvale CA, US
Xu Miao - Los Altos CA, US
Chang-Ming Tsai - Fremont CA, US
Joel D. Young - Milpitas CA, US

LinkedIn Corporation - Sunnyvale CA

G06N 7/02
G06N 3/04
G06F 15/18
G06N 3/08
G06N 99/00
G06F 17/30

The disclosed embodiments provide a system for performing online hyperparameter tuning in distributed machine learning. During operation, the system uses input data for a first set of versions of a statistical model for a set of entities to calculate a batch of performance metrics for the first set of versions. Next, the system applies an optimization technique to the batch to produce updates to a set of hyperparameters for the statistical model. The system then uses the updates to modulate the execution of a second set of versions of the statistical model for the set of entities. When a new entity is added to the set of entities, the system updates the set of hyperparameters with a new dimension for the new entity.

2017022, Aug 3, 2017

Apr 13, 2017

15/487015

- Sunnyvale CA, US
Eric Huang - San Francisco CA, US
Xu Miao - Sunnyvale CA, US
Yitong Zhou - Sunnyvale CA, US
David Hardtke - Oakland CA, US
Joel Daniel Young - Milpitas CA, US

G06Q 10/10
H04L 29/08
G06N 99/00

Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

2017010, Apr 20, 2017

Feb 17, 2016

15/046199

- Mountain View CA, US
Xu Miao - Sunnyvale CA, US
Lance M. Wall - San Francisco CA, US
Joel D. Young - Milpitas CA, US
Eric Huang - San Francisco CA, US
Songxiang Gu - Sunnyvale CA, US
Da Teng - Sunnyvale CA, US
Chang-Ming Tsai - Fremont CA, US
Sumit Rangwala - Fremont CA, US

LinkedIn Corporation - Mountain View CA

G06N 99/00
G06F 17/30

The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

2017000, Jan 5, 2017

Jun 30, 2015

14/788711

- Mountain View CA, US
Eric Huang - San Francisco CA, US
Xu Miao - Sunnyvale CA, US
Yitong Zhou - Sunnyvale CA, US
David Hardtke - Oakland CA, US
Joel Daniel Young - Milpitas CA, US

G06Q 10/10
G06N 99/00
G06F 17/30

Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

2017000, Jan 5, 2017

Jun 30, 2015

14/788717

- Mountain View CA, US
Eric Huang - San Francisco CA, US
Xu Miao - Sunnyvale CA, US
Yitong Zhou - Sunnyvale CA, US
David Hardtke - Oakland CA, US
Jeol Daniel Young - Milpitas CA, US

G06Q 10/10
G06N 99/00
G06F 17/30

Nonlinear featurization of decision trees for linear regression modeling in the context of an on-line social network is described. A computer-implemented converter is provided that is capable of reading a decision tree structure that is included in the learning to rank algorithm and convert each path from root to a leaf into an s-expression. The s-expressions are used as additional features to train a logistic regression model.

2016020, Jul 14, 2016

Jan 9, 2015

14/593955

- Redmond WA, US
Michael J. Conrad - Monroe WA, US
Tim Burrell - Kirkland WA, US
Xu Miao - Seattle WA, US
Zicheng Liu - Bellevue WA, US
Qin Cai - Clyde Hill WA, US
Zhengyou Zhang - Bellevue WA, US

G06F 3/01
G06K 9/62
H04N 5/33
G06K 9/00

Examples are disclosed herein that are related to gaze tracking via image data. One example provides, on a gaze tracking system comprising an image sensor, a method of determining a gaze direction, the method comprising acquiring image data via the image sensor, detecting in the image data facial features of a human subject, determining an eye rotation center based upon the facial features using a calibrated face model, determining an estimated position of a center of a lens of an eye from the image data, determining an optical axis based upon the eye rotation center and the estimated position of the center of the lens, determining a visual axis by applying an adjustment to the optical axis, determining the gaze direction based upon the visual axis, and providing an output based upon the gaze direction.

2015017, Jun 18, 2015

Dec 13, 2013

14/105650

- Redmond WA, US
Xu Miao - Seattle WA, US

Microsoft Corporation - Redmond WA

G06N 99/00

Machine learning may be personalized to individual users of personal computing devices, and can be used to increase machine learning prediction accuracy and speed, and/or reduce memory footprint. Personalizing machine learning can include selecting a subset of a machine learning model to load into memory. Such selecting is based, at least in part, on information collected locally by the personal computing device. Personalizing machine learning can additionally or alternatively include adjusting a classification threshold of the machine learning model based, at least in part, on the information collected locally by the personal computing device. Moreover, personalizing machine learning can additionally or alternatively include normalizing a feature output of the machine learning model accessible by an application based, at least in part, on the information collected locally by the personal computing device.