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Kilian Q Weinberger

from Ithaca, NY
Age ~45
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Also known as:
Weinberger Kilian
Related to:
Anne Weinberger
Connected to:
Adrian S Weinberg, 51Alla Weinberger, 53Arlene Weinberg, 72Ayala M Weinberg, 66Ben Weinberg, 76Beverly S Weinberger, 78Brandee M Weinberger, 60Brooke Weinberg, 33Byron L Weinberg, 69Carole J Weinberger, 81

Kilian Weinberger Phones & Addresses

  • Ithaca, NY
  • Stowe, VT
  • Saint Louis, MO
  • Palo Alto, CA
  • Mountain View, CA
  • Philadelphia, PA

Work

Company: Washington university in st. louis Jan 2010 Address: Saint Louis Position: Assistant professor

Education

Degree: PhD School / High School: University of Pennsylvania 2003 to 2007 Specialities: Machine Learning

Skills

Machine Learning • Algorithms • Artificial Intelligence • Pattern Recognition • Data Mining • Natural Language Processing

Industries

Higher Education

Resumes

Resumes

Kilian Weinberger Photo 1

Associate Professor

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Location:
140 Homestead Cir, Ithaca, NY 14850
Industry:
Higher Education
Work:
Washington University in St. Louis - Saint Louis since Jan 2010
Assistant Professor
Yahoo! Jul 2007 - Jan 2010
Research Scientist
IBM May 2006 - Aug 2007
Research Internship
Education:
University of Pennsylvania 2003 - 2007
PhD, Machine Learning University of Pennsylvania 2003 - 2004
MSc, Computer Science University of Oxford 1999 - 2002
Skills:
Machine Learning
Algorithms
Artificial Intelligence
Pattern Recognition
Data Mining
Natural Language Processing

Publications

Us Patents

Method And Apparatus For Improved Reward-Based Learning Using Nonlinear Dimensionality Reduction

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US Patent:
8060454, Nov 15, 2011
Filed:
Oct 11, 2007
Appl. No.:
11/870698
Inventors:
Rajarshi Das - Armonk NY, US
Gerald J. Tesauro - Croton-on-Hudson NY, US
Kilian Q. Weinberger - Mountain View CA, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
The present invention is a method and an apparatus for reward-based learning of management policies. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance measure between pairs of exemplars is used to compute a Non-Linear Dimensionality Reduction (NLDR) mapping of (state, action) pairs into a lower-dimensional representation, thereby producing embedded exemplars, wherein one or more parameters of the NLDR are tuned to minimize a cross-validation Bellman error on a holdout set taken from the set of one or more exemplars. The mapping is then applied to the set of exemplars, and reward-based learning is applied to the embedded exemplars to obtain a learned management policy.

Distributed Spam Filtering Utilizing A Plurality Of Global Classifiers And A Local Classifier

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US Patent:
8108323, Jan 31, 2012
Filed:
May 19, 2008
Appl. No.:
12/123270
Inventors:
Kilian Quirin Weinberger - Mountain View CA, US
John Langford - White Plains NY, US
Assignee:
YAHOO! Inc. - Sunnyvale CA
International Classification:
G06F 15/16
G06N 5/02
US Classification:
706 10, 709205, 709224
Abstract:
Embodiments are directed towards using a community of weighted results from local and global message classifiers to determine whether a message is spam. Each local classifier may receive a message that is to be evaluated to determine whether it is spam. A local classifier receives the message and performs a classification of the message. The local classifier may receive predictions of whether the message is spam from at least one global classifier. The local and global predictions are combined using, in one embodiment, a regression analysis to generate a single local message classification. Combining the local and global predictions is directed towards enabling a community of predictions to be used to classify messages. The user may then re-classify this output, which in turn is used as feedback to modify weights to the local and received global predictions for a next message.

Method And Apparatus For Improved Reward-Based Learning Using Adaptive Distance Metrics

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US Patent:
20090099985, Apr 16, 2009
Filed:
Oct 11, 2007
Appl. No.:
11/870661
Inventors:
GERALD J. TESAURO - Croton-on-Hudson NY, US
Kilian Q. Weinberger - Mountain View CA, US
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance metric and a distance-based function approximator estimating long-range expected value are then initialized, where the distance metric computes a distance between two (state, action) pairs, and the distance metric and function approximator are adjusted such that a Bellman error measure of the function approximator on the set of exemplars is minimized. A management policy is then derived based on the trained distance metric and function approximator.

Generating Congruous Metadata For Multimedia

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US Patent:
20090228510, Sep 10, 2009
Filed:
Mar 4, 2008
Appl. No.:
12/042306
Inventors:
Malcolm Slaney - Sunnyvale CA, US
Kilian Weinberger - Mountain View CA, US
Assignee:
YAHOO! INC. - Sunnyvale CA
International Classification:
G06F 17/00
US Classification:
707102, 707E17009
Abstract:
A method of generating congruous metadata is provided. The method includes receiving a similarity measure between at least two multimedia objects. Each multimedia object has associated metadata. If the at least two multimedia objects are similar based on the similarity measure and a similarity threshold, the associated metadata of each of the multimedia objects are compared. Then, based on the comparison of the associated metadata of each of the at least two multimedia objects, the method further includes generating congruous metadata. Metadata may be tags, for example.

Hierarchical Recognition Through Semantic Embedding

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US Patent:
20090271339, Oct 29, 2009
Filed:
Apr 29, 2008
Appl. No.:
12/111500
Inventors:
Olivier Chapelle - Mountain View CA, US
Kilian Quirin Weinberger - Mountain View CA, US
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
Computer-implemented systems and methods, including servers, perform structure-based recognition processes that include matching and classification. Preprocessing subsystems and sub-methods embed a set of classes on which a loss function is defined into a semantic space and learn an input mapping between an input space and the semantic space. Recognition subsystems and methods accept a test object, representable in the input space, and apply the input mapping to the test object as part of a recognition process.

Playful Incentive For Labeling Content

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US Patent:
20090327168, Dec 31, 2009
Filed:
Jun 26, 2008
Appl. No.:
12/147342
Inventors:
Kilian Quirin Weinberger - Mountain View CA, US
Anirban Dasgupta - Berkeley CA, US
Raghu Ramakrishnan - Santa Clara CA, US
David Reiley - Palo Alto CA, US
Martin Andre Monroe Zinkevich - Santa Clara CA, US
Bo Pang - Sunnyvale CA, US
Daniel Kifer - Sunnyvale CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 3/048
US Classification:
706 11
Abstract:
Embodiments are directed towards employing a playful incentive to encourage users to provide feedback that is useable to train a classifier. The classifier being associated with any of a variety of different settings, including but not limited to classifying: messages as ham/spam, images, advertising, bookmarking, music, videos, photographs, shopping, or the like. An animated image, such as a pet, provides an interface to the classifier that encourages and responds to user feedback. Users may share their classifiers or aspects thereof with other users to enable a community of knowledge to be applied to a classification task, while preserving privacy of the user feedback. One form of sharing may be within the context of a competitive game. Various evaluations may be performed on a classifier to indicate user feedback consistency, or quality. Classifiers may also be used to provide users with advertisements, products, or services based on the user's feedback.

System And Method For Disambiguating Text Labeling Content Objects

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US Patent:
20090327877, Dec 31, 2009
Filed:
Jun 28, 2008
Appl. No.:
12/164039
Inventors:
Malcolm Slaney - Santa Clara CA, US
Kilian Quirin Weinberger - Mountain View CA, US
Roelof van Zwol - Badalona, ES
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/27
US Classification:
715256
Abstract:
An improved system and method for disambiguating text strings labeling content objects is provided. A text string set may be received from a user. Frequencies of co-occurring text strings in a text collection may be obtained, and a disambiguation measure may be determined for a pair of text strings that each co-occur with a text string in the text string set. The disambiguation measure may be based on a weighted KL divergence of text string distributions that maximizes the value of divergence when a text string set may occur in different contexts. A disambiguation measure may be determined for a list of the top most common pairs of text strings that co-occur with the text string set, and the pairs of text strings may be output in decreasing order by disambiguation measure for those pairs of text strings with a disambiguation measure that exceeds a threshold.

System And Method For Improved Classification

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US Patent:
20100158356, Jun 24, 2010
Filed:
Dec 22, 2008
Appl. No.:
12/341587
Inventors:
Marc Aurelio Ranzato - New York NY, US
Kilian Quirin Weinberger - Mountain View CA, US
Eva Hoerster - Augsburg, DE
Malcom Slaney - Sunnyvale CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06K 9/62
US Classification:
382159
Abstract:
A system and method for improved classification. A first classifier is trained using a first process running on at least one computing device using a first set of training images relating to a class of images. A set of additional images are selected using the first classifier from a source of additional images accessible to the computing device. The first set of training images and the set of additional images are merged using the computing device to create a second set of training images. A second classifier is trained using a second process running on the computing device using the second set of training images. A set of unclassified images are classified using the second classifier thereby creating a set of classified images. The first classifier and the second classifier employ different classification methods.
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Kilian Q Weinberger from Ithaca, NY, age ~45 Get Report