Patents by Inventor Sebastian TSCHIATSCHEK
Sebastian TSCHIATSCHEK 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).
-
Publication number: 20230394368Abstract: A method of training a model comprising a generative network mapping a latent vector to a feature vector, wherein weights in the generative network are modelled as probabilistic distributions. The method comprises: a) obtaining one or more observed data points, each comprising an incomplete observation of the features in the feature vector; b) training the model based on the observed data points to learn values of the weights of the generative network which map the latent vector to the feature vector; c) from amongst a plurality of potential next features to observe, searching for a target feature of the feature vector which maximizes a measure of expected reduction in uncertainty in a distribution of said weights of the generative network given the observed data points so far; and d) outputting a request to collect a target data point comprising at least the target feature.Type: ApplicationFiled: August 15, 2023Publication date: December 7, 2023Inventors: Cheng ZHANG, Wenbo GONG, Richard Eric TURNER, Sebastian TSCHIATSCHEK, Josè Miguel HERNÁNDEZ LOBATO
-
Patent number: 11769074Abstract: A method of training a model comprising a generative network mapping a latent vector to a feature vector, wherein weights in the generative network are modelled as probabilistic distributions. The method comprises: a) obtaining one or more observed data points, each comprising an incomplete observation of the features in the feature vector; b) training the model based on the observed data points to learn values of the weights of the generative network which map the latent vector to the feature vector; c) from amongst a plurality of potential next features to observe, searching for a target feature of the feature vector which maximizes a measure of expected reduction in uncertainty in a distribution of said weights of the generative network given the observed data points so far; and d) outputting a request to collect a target data point comprising at least the target feature.Type: GrantFiled: July 9, 2019Date of Patent: September 26, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Cheng Zhang, Wenbo Gong, Richard Eric Turner, Sebastian Tschiatschek, José Miguel Hernández Lobato
-
Patent number: 11741357Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.Type: GrantFiled: June 17, 2019Date of Patent: August 29, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Cheng Zhang, Reinhard Sebastian Bernhard Nowozin, Ameera Patel, Danielle Charlotte Mary Belgrave, Konstantina Palla, Anja Thieme, Iain Edward Buchan, Chao Ma, Sebastian Tschiatschek, Jose Miguel Hernandez Lobato
-
Patent number: 11710080Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.Type: GrantFiled: June 17, 2019Date of Patent: July 25, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Cheng Zhang, Reinhard Sebastian Bernhard Nowozin, Ameera Patel, Danielle Charlotte Mary Belgrave, Konstantina Palla, Anja Thieme, Iain Edward Buchan, Chao Ma, Sebastian Tschiatschek, Jose Miguel Hernandez Lobato
-
Publication number: 20230111659Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.Type: ApplicationFiled: November 2, 2022Publication date: April 13, 2023Inventors: Sam Michael DEVLIN, Maximilian IGL, Kamil Andrzej CIOSEK, Yingzhen LI, Sebastian TSCHIATSCHEK, Cheng ZHANG, Katja HOFMANN
-
Patent number: 11526812Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.Type: GrantFiled: October 1, 2019Date of Patent: December 13, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Sam Michael Devlin, Maximilian Igl, Kamil Andrzej Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
-
Publication number: 20220343111Abstract: A method of selecting data for privacy preserving machine learning comprises: storing training data from a first party, storing a machine learning model, and storing criteria from the first party or from another party. The method comprises filtering the training data to select a first part of the training data to be used to train the machine learning model and select a second part of the training data. The selecting is done by computing a measure, using the criteria, of the contribution of the data to the performance of the machine learning model.Type: ApplicationFiled: May 31, 2022Publication date: October 27, 2022Inventors: Sebastian TSCHIATSCHEK, Olga OHRIMENKO, Shruti Shrikant TOPLE
-
Patent number: 11366980Abstract: A method of selecting data for privacy preserving machine learning comprises: storing training data from a first party, storing a machine learning model, and storing criteria from the first party or from another party. The method comprises filtering the training data to select a first part of the training data to be used to train the machine learning model and select a second part of the training data. The selecting is done by computing a measure, using the criteria, of the contribution of the data to the performance of the machine learning model.Type: GrantFiled: November 18, 2019Date of Patent: June 21, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Sebastian Tschiatschek, Olga Ohrimenko, Shruti Shrikant Tople
-
Publication number: 20220147818Abstract: A computer-implemented method of training an auxiliary machine learning model to predict a set of new parameters of a primary machine learning model, wherein the primary model is configured to transform from an observed subset of a set of real-world features to a predicted version of the set of real-world features.Type: ApplicationFiled: November 11, 2020Publication date: May 12, 2022Inventors: Cheng ZHANG, Angus LAMB, Evgeny Sergeevich SAVELIEV, Yingzhen LI, Camilla LONGDEN, Pashmina CAMERON, Sebastian TSCHIATSCHEK, Jose Miguel Hernández LOBATO, Richard TURNER
-
Publication number: 20210406765Abstract: A computer-implemented method of training a model comprising a sequence of stages, each stage in the sequence comprises: a VAE comprising a respective first encoder arranged to encode a respective subset of the real-world features into a respective latent space representation, and a respective first decoder arranged to decode from the respective latent space representation to a respective decoded version of the respective set of real-world features; at least each but the last stage in the sequence comprises: a respective second decoder arranged to decode from the respective latent space representation to predict one or more respective actions; and each successive stage in the sequence following the first stage, each succeeding a respective preceding stage in the sequence, further comprises: a sequential network arranged to transform from the latent representation from the preceding stage to the latent space representation of the successive stage.Type: ApplicationFiled: August 25, 2020Publication date: December 30, 2021Inventors: Cheng ZHANG, Yingzhen LI, Sebastian TSCHIATSCHEK, Haiyan YIN, Jooyeon KIM
-
Publication number: 20210358577Abstract: In a first stage, training each of a plurality of first variational auto encoders, VAEs, each comprising: a respective first encoder arranged to encode a respective subset of one or more features of a feature space into a respective first latent representation, and a respective first decoder arranged to decode from the respective latent representation back to a decoded version of the respective subset of the feature space, wherein different subsets comprise features of different types of data. In a second stage following the first stage, training a second VAE comprising: a second encoder arranged to encode a plurality of inputs into a second latent representation, and a second decoder arranged to decode the second latent representation into decoded versions of the first latent representations, wherein each of the plurality of inputs comprises a combination of a different respective one of feature subsets with the respective first latent representation.Type: ApplicationFiled: August 18, 2020Publication date: November 18, 2021Inventors: Cheng ZHANG, Chao MA, Richard Eric TURNER, José Miguel HERNÁNDEZ LOBATO, Sebastian TSCHIATSCHEK
-
Publication number: 20210097445Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.Type: ApplicationFiled: October 1, 2019Publication date: April 1, 2021Inventors: Sam Michael DEVLIN, Maximilian IGL, Kamil Andrzej CIOSEK, Yingzhen LI, Sebastian TSCHIATSCHEK, Cheng ZHANG, Katja HOFMANN
-
Publication number: 20210089819Abstract: A method of selecting data for privacy preserving machine learning comprises: storing training data from a first party, storing a machine learning model, and storing criteria from the first party or from another party. The method comprises filtering the training data to select a first part of the training data to be used to train the machine learning model and select a second part of the training data. The selecting is done by computing a measure, using the criteria, of the contribution of the data to the performance of the machine learning model.Type: ApplicationFiled: November 18, 2019Publication date: March 25, 2021Inventors: Sebastian TSCHIATSCHEK, Olga OHRIMENKO, Shruti Shrikant TOPLE
-
Publication number: 20200394559Abstract: A method of training a model comprising a generative network mapping a latent vector to a feature vector, wherein weights in the generative network are modelled as probabilistic distributions. The method comprises: a) obtaining one or more observed data points, each comprising an incomplete observation of the features in the feature vector; b) training the model based on the observed data points to learn values of the weights of the generative network which map the latent vector to the feature vector; c) from amongst a plurality of potential next features to observe, searching for a target feature of the feature vector which maximizes a measure of expected reduction in uncertainty in a distribution of said weights of the generative network given the observed data points so far; and d) outputting a request to collect a target data point comprising at least the target feature.Type: ApplicationFiled: July 9, 2019Publication date: December 17, 2020Inventors: Cheng ZHANG, Wenbo GONG, Richard Eric TURNER, Sebastian TSCHIATSCHEK, José Miguel HERNÁNDEZ LOBATO
-
Publication number: 20200104702Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.Type: ApplicationFiled: June 17, 2019Publication date: April 2, 2020Inventors: Cheng ZHANG, Reinhard Sebastian Bernhard NOWOZIN, Ameera PATEL, Danielle Charlotte Mary BELGRAVE, Konstantina PALLA, Anja THIEME, Iain Edward BUCHAN, Chao MA, Sebastian TSCHIATSCHEK, Jose Miguel HERNANDEZ LOBATO
-
Publication number: 20200105381Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.Type: ApplicationFiled: June 17, 2019Publication date: April 2, 2020Inventors: Cheng ZHANG, Reinhard Sebastian Bernhard NOWOZIN, Ameera PATEL, Danielle Charlotte Mary BELGRAVE, Konstantina PALLA, Anja THIEME, Iain Edward BUCHAN, Chao MA, Sebastian TSCHIATSCHEK, Jose Miguel HERNANDEZ LOBATO