Adobe, Inc.

Ayush Chauhan

Gaurav Sinha

Shiv Kumar Saini

Subrata Mitra

Sunav Choudhary

Ritwick Chaudhry

Aurghya Maiti

Siddharth Jain

Vineet Malik

Parth Gupta

Archiki Prasad

Amireddy Prashanth Reddy

Aayush Makharia

Sourav Suman

Fan Du

Vibhor Porwal

Kishalay Raj

Naman Poddar

Pulkit Goel

Xiao Xie

Ruchi Sandeep Pandya

Vishal Babu Bhavani

In implementations of systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups.

Systems for estimating terminal event likelihood

Introduced here are approaches to determining causal relationships in mixed datasets containing data related to continuous variables and discrete variables. To accomplish this, a marketing insight and intelligence platform may employ a multi-phase approach in which dependency is established before the data related to continuous variables is discretized. Such an approach ensures that information regarding dependence is not lost through discretization.

Discovering causal relationships in mixed datasets

A method performed by one or more processors that preserves a machine learning model comprises accessing model parameters associated with a machine learning model. The model parameters are determined responsive to training the machine learning model. The method comprises generating a plurality of model parameter sets, where each of the plurality of model parameter sets comprises a separate portion of the set of model parameters. The method comprises determining one or more parity sets comprising values calculated from the plurality of model parameter sets. The method comprises distributing the plurality of model parameter sets and the one or more parity sets among a plurality of computing devices, where each of the plurality of computing devices stores a model parameter set of the plurality of model parameter sets or a parity set of the one or more parity sets. The method comprises accessing, from the plurality of computing devices, a number of sets comprising model parameter sets and at least one parity set. The method comprises reconstructing the machine learning model from the number of sets accessed from the plurality of computing devices.

Managing machine learning model reconstruction

The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.

Determining feature contributions to data metrics utilizing a causal dependency model

Automated discovery of causal relationships in mixed datasets

A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

Key-value memory network for predicting time-series metrics of target entities

This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics. Further, the disclosed systems can provide interactive tools for generating and visualizing predictions or causal relationships in intuitive user interfaces, such as visualizations for dimension-specific (or dimension-value-specific) interventions and/or attribution determinations.

Generating visualizations of analytical causal graphs

Graphing services are provided to a device cooperative that includes data contributors, e.g., website hosts. Anonymized user data, provided by the data contributors, is accessed, via a blockchain, decrypted, and aggregated. A device graph is generated based on the aggregated user data. Contribution metrics are provided to the data contributors. A first contribution metric for a first data contributor indicates a contribution to the device graph of a first portion of the user data that was provided by the first data contributor. In response to receiving a request for a verification of the first contribution metric, a zero knowledge proof of the first contribution metric is generated and provided to the first data contributor. The first data contributor is enabled to evaluate the zero knowledge proof independent of access to a second portion of the user data that was provided by a second data contributor of the device cooperative.

Growing community of inventors

Bengaluru, India

Ayush Chauhan