International Business Machines Corporation

Yahoo! Inc.

Amazon Technologies, Inc.

Srujana Merugu

Sachindra Joshi

Ashish Verma

Shantanu Ravindra Godbole

Prasanta Kumar Ghosh

Sathiya Keerthi Selvaraj

Karthik Visweswariah

Philip Lewis Bohannon

Rajeev Ramnarain Rastogi

Rose Catherine Kanjirathinkal

Gowda Dayananda Anjaneyapura Range

Gourav Roy

Amit K Singh

Rushi P Bhatt

Amit Chandak

Aswin Natarajan

Bharath Venkatesh

Ashwin Kumar V Machanavajjhala

Jagannathan Srinivasan

Varnit Agnihotri

Prateek Gupta

Sathish Kumar Palanisamy

Deepak Agarwal

Naoki Abe

David L Jensen

Justin Wai-chow Wong

Arup Kumar Choudhury

Vipul Agarwal

Kedar Bellare

Pedro DeRose

Arun Shankar Iyer

A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.

Labeling of data for machine learning

A number of attributes of different attribute types, to be used to assign observation records of a data set to clusters, are identified. Attribute-type-specific distance metrics for the attributes, which can be combined to obtain a normalized aggregated distance of an observation record from a cluster representative, are selected. One or more iterations of a selected clustering methodology are implemented on the data set using resources of a machine learning service until targeted termination criteria are met. A given iteration includes assigning the observations to clusters of a current version of a clustering model based on the aggregated distances from the cluster representatives of the current version, and updating the cluster representatives to generate a new version of the clustering model.

Scalable clustering for mixed machine learning data

An approximate data structure to represent clusters of observation records of a data set is identified. A hierarchical representation of a plurality of clusters, including the targeted number of clusters among which the observation records are to be distributed, is generated. Each node of the hierarchy comprises an instance of the approximate data structure. Until a set of termination criteria are met, iterations of a selected clustering methodology are run. In a given iteration, distances of observation records from the cluster representatives of a current version of the model are computed using the hierarchical representation, and a new version of the model with modified cluster representatives is generated.

Clustering sparse high dimensional data using sketches

Data mining systems and methods are disclosed for associating users with items based on underlying personas. The system associates each user account with one or more underlying personas that contribute to the user's interactions with different items, and predicts an active persona for a user based on the user's recent interactions with items and make item related recommendations that are oriented to the active persona. Thus, for example, even though multiple individuals may share a computer and/or account, the content (e.g., item recommendations) presented during a browsing session may be based primarily or exclusively on the past browsing behaviors of the particular individual conducting the browsing session.

Persona based data mining system

Data mining systems and methods are disclosed for associating users with items based on underlying personas. The system associates each user account with one or more underlying personas that contribute to the user's interactions with different items, and models user-to-item associations in accordance with the underlying personas based on probabilistic matrix factorization. The system may further predict an active persona for a user based on the user's recent interactions with items and make item related recommendations that are oriented to the active persona.

Probabilistic matrix factorization system based on personas

A method for managing user-generated questions and answers across multiple social media data sources can begin with the receiving of query parameters, including a user-entered question, via the user interface of a social media Q&A manage. Social media data sources can be queried for knowledge related to the user-entered question. When knowledge related to the user-entered question exists, the existing related knowledge can be organized and presented in the user interface according to a determined answer quality. When knowledge related to the user-entered question does not exist or is deemed unsatisfactory by a user, the user-entered question can be automatically submitted to applicable social media data sources by the social media Q&A manager on behalf of the user. A status of the submitted user-entered question can be monitored. When the status of the submitted user-entered question changes, the method can be re-executed at the querying step.

User interface for managing questions and answers across multiple social media data sources

A system is disclosed for obtaining and aggregating opinions generated by multiple sources with respect to one or more objects. The disclosed system uses observed variables associated with an opinion and a probabilistic model to estimate latent properties of that opinion. With those latent properties, the disclosed system may enable publishers to reliably and comprehensively present object information to interested users.

Opinion aggregation system

A classifier development process seamlessly and intelligently integrates different forms of human feedback on instances and features into the data preparation, learning and evaluation stages. A query utility based active learning approach is applicable to different types of editorial feedback. A bi-clustering based technique may be used to further speed up the active learning process.

Rapid iterative development of classifiers

Methods and apparatus for performing computer-implemented extraction of temporal information for business entities and events are disclosed. In one embodiment, a sequence of text is obtained. A label is assigned to one or more of a plurality of segments of the text such that each of the one or more of the plurality of segments of the text is classified as temporal data in one of a plurality of classes of temporal data. One or more rules are applied to the one or more segments of the text that have been classified as temporal data to generate a structured representation of the temporal data, where the rules include one or more schematic rules. Each of the schematic rules pertains to one or more of the plurality of classes of temporal data and indicates a structure in which temporal data in the corresponding one or more of the plurality of classes is to be stored.

Extracting rich temporal context for business entities and events

A method (and system) for causal modeling includes modeling a data set. The modeling includes estimating a reverse Bayesian forest for the data set and detecting outliers in a separate data set. Detecting the outliers includes applying the reverse Bayesian forest to the separate data set to obtain a probability value assigned to data points in the separate data set and identifying outliers in the separate data set by evaluating the probability value given by the reverse Bayesian forest.

Method and system for causal modeling and outlier detection

A method for predicting future responses from large sets of dyadic data includes measuring a dyadic response variable associated with a dyad from two different sets of data; measuring a vector of covariates that captures the characteristics of the dyad; determining one or more latent, unmeasured characteristics that are not determined by the vector of covariates and which induce local structures in a dyadic space defined by the two different sets of data; and modeling a predictive response of the measurements as a function of both the vector of covariates and the one or more latent characteristics, wherein modeling includes employing a combination of regression and matrix co-clustering techniques, and wherein the one or more latent characteristics provide a smoothing effect to the function that produces a more accurate and interpretable predictive model of the dyadic space that predicts future dyadic interaction based on the two different sets of data.

Predictive discrete latent factor models for large scale dyadic data

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Bengaluru, India

Srujana Merugu