International Business Machines Corporation

Thomson Licensing

Maplebear Inc.

University System of Maryland

Theodoros Salonidis

Dinesh C Verma

Bong Jun Ko

Rahul Urgaonkar

Nirmit V Desai

Swati Rallapalli

Jorge J Ortiz

Seraphin Bernard Calo

Henrik Lundgren

Dawei Li

Ting Wang

Kang-Won Lee

Jiyong Jang

Xin Hu

Christian Makaya

Xiping Wang

Keith William Grueneberg

Marc Ph Stoecklin

Wentao Huang

Supriyo Chakraborty

Bongjun Ko

Shiqiang Wang

Roch Guerin

Gregory Bramble

Georgios Sotiropoulos

Ramesh Govindan

Murtaza Zafer

Long H Vu

David Alvra Wood, Iii

Don Towsley

Georgios Kollias

Abm Musa

Anand Prabhu Subramanian

Tae Hyun Kim

Aris Gkoulalas-Divanis

Dakuo Wang

Thanh Lam Hoang

Martin G Kienzle

Christophe Diot

Shahrokh Daijavad

Saket Sathe

Olivia Choudhury

Richard Orville LaMaire

Pravin Bhagwat

Alex Swain

Lin Ju

Issa Sylla

Pascal Le Guyadec

Marianna Carrera

John Dillon Eversman

Voranouth Supadulya

Tiffany Tuor

Leandros Tassiulas

Rafael Laufer

Dimitrios Koutsonikolas

Nicholas Mazzitelli

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.

Recommendations based on private data using a dynamically deployed pre-filter

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.

Linking, deploying, and executing distributed analytics with distributed datasets

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.

System, method, and recording medium for data mining between private and public domains

Methods are provided. A method includes announcing to a network meta information describing each of a plurality of distributed data sources. The method further includes propagating the meta information amongst routing elements in the network. The method also includes inserting into the network a description of distributed datasets that match a set of requirements of the analytics task. The method additionally includes delivering, by the routing elements, a copy of the analytics task to locations of respective ones of the plurality of distributed data sources that include the distributed datasets that match the set of requirements of the analytics task.

Content-based distribution and execution of analytics applications on distributed datasets

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.

Tensor comparison across a distributed machine learning environment

A training process of a machine learning model is executed at the edge node for a number of iterations to generate a model parameter based at least in part on a local dataset and a global model parameter. A resource parameter set indicative of resources available at the edge node is estimated. The model parameter and the resource parameter set are sent to a synchronization node. Updates to the global model parameter and the number of iterations are received from the synchronization node based at least in part on the model parameter and the resource parameter set of edge nodes. The training process of the machine learning model is repeated at the edge node to determine an update to the model parameter based at least in part on the local dataset and updates to the global model parameter and the number of iterations from the synchronization node.

Distributed machine learning at edge nodes

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.

Distributed resource-aware training of machine learning pipelines

A data mining method, system, and non-transitory computer readable medium, include defining a set of filter constraints as a filter function for clustering users' private records of data of a private domain, selecting a subset of users' public records of data from a filtered set of data from a public domain that is common with the users' private records of data, and creating a data file including the matched user of the private domain to the public records of the user of the private domain, where the set of the filter constraints comprises a function that captures the subset of the users' public records of data who are of interest to the private domain, and only performs data mining with that set of information from the public domain.

An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

Method for efficient and practical key distribution in network coding systems

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.

Federated machine learning using locality sensitive hashing

A model requester node, which is an edge node of a cloud computing network, generates a specification of a machine learning model, distributes the specification to a plurality of other edge nodes, and receives replies to the specification from the plurality of other edge nodes. In response to the replies, the model requester node identifies a set of participating edge nodes based on a learning utility and a cost estimate of each of the plurality of other edge nodes. The model requester node then trains the machine learning model, without exchanging training data among the model requester node and the participating edge nodes, by repeatedly: distributing most recent parameters of the machine learning model to the participating edge nodes; receiving updates to the most recent parameters from the participating edge nodes; and establishing new parameters for the machine learning model by aggregating the updates from the participating edge nodes.

Collaborative distributed machine learning

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. An interactive visualization graphical user interface of the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof, and the extracted metadata may be generated.

Automated artificial intelligence radial visualization

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Boston, MA, United States of America

Theodoros Salonidis