Patents by Inventor Roland Jasper Snoek
Roland Jasper Snoek has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11501192Abstract: Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function.Type: GrantFiled: September 4, 2018Date of Patent: November 15, 2022Assignees: President and Fellows of Harvard College, The Governing Council of the University of TorontoInventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky, Richard Zemel
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Publication number: 20220253747Abstract: The present disclosure is directed to systems and method to perform improved detection of out-of-distribution (OOD) inputs. In particular, current deep generative model-based approaches for OOD detection are significantly negatively affected by and struggle to distinguish population level background statistics from semantic content relevant to the in-distribution examples. In fact, such approaches have even been experimentally observed to assign higher likelihood to OOD inputs, which is opposite to the desired behavior. To resolve this problem, the present disclosure proposes a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.Type: ApplicationFiled: May 26, 2020Publication date: August 11, 2022Inventors: Jie Ren, Balaji Lakshminarayanan, Peter Junteng Liu, Joshua Vincent Dillon, Roland Jasper Snoek, Ryan Poplin, Mark Andrew DePristo, Emily Amanda Fertig
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Publication number: 20200027012Abstract: Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function.Type: ApplicationFiled: September 4, 2018Publication date: January 23, 2020Applicants: President and Fellows of Harvard College, Governing Council of the Univ. of Toronto, TheInventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky, Richard Zemel
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Patent number: 10346757Abstract: Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: beginning evaluation of the objective function at a first point; before evaluating the objective function at the first point is completed: identifying, based on likelihoods of potential outcomes of evaluating the objective function at the first point, a second point different from the first point at which to evaluate the objective function; and beginning evaluation of the objective function at the second point.Type: GrantFiled: May 30, 2014Date of Patent: July 9, 2019Assignees: President and Fellows of Harvard College, Socpra Sciences ET Genie S.E.C., The Governing Council of the University of TorontoInventors: Ryan P. Adams, Roland Jasper Snoek, Hugo Larochelle
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Publication number: 20180349158Abstract: Optimizing performance of a Java Virtual Machine (JVM) using an objective function is described. At least one computer hardware processor performs: identifying a set of parameter values corresponding to at least one hyper-parameter of the JVM, the identifying performed at least in part by using a probabilistic model of an objective function relating the at least one hyper-parameter of the JVM to measure performance of the JVM; evaluating the objective function at the identified set of parameter values to obtain a value identifying a measure of performance of the JVM when operated using the set of parameter values, the evaluating performed at least in part by executing the JVM when configured with the set of parameter values; and updating, based on the value identifying the measure of performance of the JVM, the probabilistic model of the objective function to obtain an updated probabilistic model of the objective function.Type: ApplicationFiled: March 22, 2018Publication date: December 6, 2018Inventors: Kevin Swersky, Roland Jasper Snoek, Ryan P. Adams, Alexander B. Wiltschko
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Patent number: 10074054Abstract: Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function.Type: GrantFiled: May 30, 2014Date of Patent: September 11, 2018Assignees: President and Fellows of Harvard College, Governing Council of the Univ. of Toronto, TheInventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky, Richard Zemel
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Patent number: 9864953Abstract: Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: identifying, using an integrated acquisition utility function and a probabilistic model of the objective function, at least a first point at which to evaluate the objective function; evaluating the objective function at least at the identified first point; and updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.Type: GrantFiled: May 30, 2014Date of Patent: January 9, 2018Assignees: President and Fellows of Harvard College, SOCPRA Sciences et Genie S.E.C., Governing Council of the Univ. of Toronto, TheInventors: Ryan P. Adams, Roland Jasper Snoek, Hugo Larochelle
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Patent number: 9858529Abstract: Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.Type: GrantFiled: May 30, 2014Date of Patent: January 2, 2018Assignees: President and Fellows of Harvard College, The Governing Council of the Univ. of TorontoInventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky
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Publication number: 20160328655Abstract: Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.Type: ApplicationFiled: May 30, 2014Publication date: November 10, 2016Applicants: President and Fellows of Harvard College, Governing Council of the Univ. of Toronto, The MaRS Centre; Heritage BuildingInventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky
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Publication number: 20160328653Abstract: Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: beginning evaluation of the objective function at a first point; before evaluating the objective function at the first point is completed: identifying, based on likelihoods of potential outcomes of evaluating the objective function at the first point, a second point different from the first point at which to evaluate the objective function; and beginning evaluation of the objective function at the second point.Type: ApplicationFiled: May 30, 2014Publication date: November 10, 2016Applicants: Universite de Sherbrooke, President and Fellows of Harvard College, Governing Council of the Univ. of Toronto, The MaRS Centre; Heritage BuildingInventors: Ryan P. Adams, Roland Jasper Snoek, Hugo Larochelle
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Publication number: 20160292129Abstract: Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: identifying, using an integrated acquisition utility function and a probabilistic model of the objective function, at least a first point at which to evaluate the objective function; evaluating the objective function at least at the identified first point; and updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.Type: ApplicationFiled: May 30, 2014Publication date: October 6, 2016Applicants: President and Fellows of Harvard College, Governing Council of the Univ. of Toronto, TheInventors: Ryan P. Adams, Roland Jasper Snoek, Hugo Larochelle
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Publication number: 20140358831Abstract: Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function.Type: ApplicationFiled: May 30, 2014Publication date: December 4, 2014Applicants: President and Fellows of Harvard College, Governing Council of the Univ. of Toronto, The MaRS CentreInventors: Ryan P. Adams, Roland Jasper Snoek, Kevin Swersky, Richard Zemel