Abstract: Embodiments for providing automated machine learning visualization. Machine learning tasks, transformers, and estimators may be received into one or more machine learning composition modules. The machine learning composition modules generate one or more machine learning models. A machine learning model pipeline is a sequence of transformers and estimators and an ensemble of machine learning pipelines are an ensemble of machine learning pipelines. A machine learning model pipeline, an ensemble of a plurality of machine learning model pipelines, or a combination thereof, along with corresponding metadata, may be generated using the machine learning composition modules. Metadata may be extracted from the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof.

Abstract: A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Abstract: In an approach to unsupervised feature learning for relational data, a computer trains one or more entity aware autoencoders on one or more tables in a relational database, where each of the one or more entity aware autoencoders corresponds to one of the one or more tables in the relational database, and where each of the one or more entity aware autoencoders are comprised of an encoder and a decoder. A computer transforms each of the one or more tables in the relational database with the encoder of the corresponding trained entity aware autoencoder. A computer joins a first transformed table of the one or more tables in the relational database with each remaining one or more transformed tables in the relational database to form one or more joined tables. A computer aggregates the one or more joined tables. A computer outputs one or more feature representations.

Abstract: Examples of techniques for interactive generation of labeled data and training instances are provided. According to one or more embodiments of the present invention, a computer-implemented method for interactive generation of labeled data and training instances includes presenting, by the processing device, control labeling options to a user. The method further includes selecting, by a user, one or more of the presented control labeling options. The method further includes selecting, by a processing device, a representative set of unlabeled data samples based at least in part on the control labeling options selected by the user. The method further includes generating, by a processing device, a set of suggested labels for each of the unlabeled data samples.

Abstract: A system and method for assessing Pay-As-You-Go (PAYG) Automatic machine learned (AutoML) model pipeline charge to a user on the basis of performance improvement achieved by configuring a model pipeline with performance enhancements relative to a performance obtained by a base model pipeline. The method performs a ranking of pipelines (customized models) based on a user-specified metric (for example, prediction accuracy, run time, F1 score) or combination of metrics. The price for ranked pipelines is specified based on a “surrogate” model where the surrogate model is fit to the base model price and the maximum price for a model. The base model price relates to use of a current cloud resource utilization-based pricing model. The pricing per model pipeline increments on the basis of performance metric(s) in a linear fashion, e.g., using a linear pricing model, or in an exponential fashion, e.g., using a fixed percentage hike price model.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to construct an encoded message using a message and a random element, construct a hash using a shared secret, and transmit the encoded message and the hash to a destination, through a network.

Abstract: In an approach to unsupervised feature learning for relational data, a computer trains one or more entity aware autoencoders on one or more tables in a relational database, where each of the one or more entity aware autoencoders corresponds to one of the one or more tables in the relational database, and where each of the one or more entity aware autoencoders are comprised of an encoder and a decoder. A computer transforms each of the one or more tables in the relational database with the encoder of the corresponding trained entity aware autoencoder. A computer joins a first transformed table of the one or more tables in the relational database with each remaining one or more transformed tables in the relational database to form one or more joined tables. A computer aggregates the one or more joined tables. A computer outputs one or more feature representations.

Abstract: A method to automatically assess data quality of data input into a machine learning model and remediate the data includes receiving input data for an automated machine learning model. Selections for a multiple data quality metrics are displayed. A selection for data quality metrics is received. The data quality metrics are determined according to the selection. Selections for data remediation strategies based on the selection of the data quality metrics are displayed. A selection for remediation recommendation strategies is received. The selected data remediation strategies are performed on the input data. Learning from the selection of the data quality metrics and the selection for the remediation strategies is performed. A new customized machine learning model is generated based on the learning.

Abstract: A method, a structure, and a computer system for predicting pipeline training requirements. The exemplary embodiments may include receiving one or more worker node features from one or more worker nodes, extracting one or more pipeline features from one or more pipelines to be trained, and extracting one or more dataset features from one or more datasets used to train the one or more pipelines. The exemplary embodiments may further include predicting an amount of one or more resources required for each of the one or more worker nodes to train the one or more pipelines using the one or more datasets based on one or more models that correlate the one or more worker node features, one or more pipeline features, and one or more dataset features with the one or more resources. Lastly, the exemplary embodiments may include identifying a worker node requiring a least amount of the one or more resources of the one or more worker nodes for training the one or more pipelines.

Abstract: Examples of techniques for adaptive model training are provided. According to one or more embodiments of the present invention, a computer-implemented method for adaptive model training includes generating, by a processing system, a training instance based at least in part on a plurality of images that match a contextual specification of a target visual domain. The method further includes extracting, by the processing system, objects from one of the plurality of images. The method further includes for each extracted object, generating, by the processing system, a plurality of machine learning model features and label recommendations for a user.

Abstract: Using locality sensitive hashing in federated machine learning. A server receives from clients locality sensitive hash (LSH) vectors. In one embodiment, the server groups the clients into clusters, based on the LSH vectors; the server selects a subset of the clients, by choosing at least one client from each of the clusters. In another embodiment, the server finds a subset of the clients, by minimize gradient divergence for the subset of the clients. The server receives from selected clients LSH vectors computed based on parameter vectors of updated models, and based on LSH vectors the server determines whether the updated models are sufficiently different from a model being trained; in response to determining that the updated models are sufficiently different from the model, the server requests the selected clients to send the parameter vectors to the server.

Abstract: Tensor comparison across a network by determining a first parameter of a first vector representation of a first tensor object associated with a first processor, receiving a second parameter associated with a second vector representation of a second tensor object associated with a second processor, determining a first difference between the first parameter and the second parameter, and sending the first vector representation from the first processor to the second processor, according to the first difference.

Abstract: A data mining method, system, and non-transitory computer readable medium include obtaining a subset of public records of data in a public domain and performing data mining, via private domain data, within the subset of the public records of data to find data in the public domain corresponding to a particular individual.

Abstract: Methods and systems for execution of distributed analytics include building a global linked structure that describes correspondences between dataset metadata structures, analytics metadata structures, and location metadata structures and that encodes compatibility between respective datasets, analytics, and locations. A set of analytics and compatible datasets for execution is determined based on the dataset metadata structures, analytics metadata structures, and global linked structure. An optimal execution location is determined based on the determined set of analytics and compatible datasets, the location metadata structures, and the global linked structure. The set of analytics and compatible datasets are deployed to the optimal location for execution.

Abstract: Examples of techniques for adaptive model training are provided. According to one or more embodiments of the present invention, a computer-implemented method for adaptive model training includes generating, by a processing system, a training instance based at least in part on a plurality of images that match a contextual specification of a target visual domain. The method further includes extracting, by the processing system, objects from one of the plurality of images. The method further includes for each extracted object, generating, by the processing system, a plurality of machine learning model features and label recommendations for a user.

Abstract: A computer-implemented method for training a global federated learning model using an aggregator server includes training multiple local models at respective local nodes. Each local node selects a set of attributes from its training dataset for training its local model. Each local node generates an anonymized training dataset by using a syntactic anonymization method, and by selecting quasi-identifying attributes from training attributes, and generalizing the quasi-identifying attributes using a syntactic algorithm. Further, each local node computes a syntactic mapping based on equivalence classes produced in the anonymized training dataset. The aggregator server computes a union of mappings received from all the local nodes. Further, federated learning includes training the global federated learning model by iteratively sending, by the local nodes to the aggregator server, parameter updates computed over the local models.

Abstract: Examples of techniques for adaptive object recognition for a target visual domain given a generic machine learning model are provided. According to one or more embodiments of the present invention, a computer-implemented method for adaptive object recognition for a target visual domain given a generic machine learning model includes creating, by a processing device, an adapted model and identifying classes of the target visual domain using the generic machine learning model. The method further includes creating, by the processing device, a domain-constrained machine learning model based at least in part on the generic machine learning model such that the domain-constrained machine learning model is restricted to recognize only the identified classes of the target visual domain. The method further includes computing, by the processing device, a recognition result based at least in part on combining predictions of the domain-constrained machine learning model and the adapted model.

Abstract: Methods and systems for execution of distributed analytics include building a global linked structure that describes correspondences between dataset metadata structures, analytics metadata structures, and location metadata structures and that encodes compatibility between respective datasets, analytics, and locations. A set of analytics and compatible datasets for execution is determined based on the dataset metadata structures, analytics metadata structures, and global linked structure. An optimal execution location is determined based on the determined set of analytics and compatible datasets, the location metadata structures, and the global linked structure. The set of analytics and compatible datasets are deployed to the optimal location for execution.

Abstract: Examples of techniques for adaptive object recognition for a target visual domain given a generic machine learning model are provided. According to one or more embodiments of the present invention, a computer-implemented method for adaptive object recognition for a target visual domain given a generic machine learning model includes creating, by a processing device, an adapted model and identifying classes of the target visual domain using the generic machine learning model. The method further includes creating, by the processing device, a domain-constrained machine learning model based at least in part on the generic machine learning model such that the domain-constrained machine learning model is restricted to recognize only the identified classes of the target visual domain. The method further includes computing, by the processing device, a recognition result based at least in part on combining predictions of the domain-constrained machine learning model and the adapted model.

Abstract: Examples of techniques for interactive generation of labeled data and training instances are provided. According to one or more embodiments of the present invention, a computer-implemented method for interactive generation of labeled data and training instances includes presenting, by the processing device, control labeling options to a user. The method further includes selecting, by a user, one or more of the presented control labeling options. The method further includes selecting, by a processing device, a representative set of unlabeled data samples based at least in part on the control labeling options selected by the user. The method further includes generating, by a processing device, a set of suggested labels for each of the unlabeled data samples.