Adobe, Inc.

Sunny Dhamnani

Ritwik Sinha

Vishwa Vinay

Lilly Kumari

Georgios Theocharous

Shiv Kumar Saini

Dhruv Singal

David Meier Weinstein

Margarita Savova

Atanu R Sinha

Moumita Sinha

Navita Goyal

Raja Kumar Dubey

Prithviraj Abasaheb Chavan

A S Akil Arif Ibrahim

Tanay Asija

Aakash Srinivasan

Kaarthik Raja Meenakshi Viswanathan

The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.

Efficiently determining local machine learning model feature contributions

This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.

Graph data structure for using inter-feature dependencies in machine-learning

An IDS generator determines multiple classes for electronic data items. The IDS generator determines, for each class, a class-specific candidate ruleset. The IDS generator performs a differential analysis of each class-specific candidate ruleset. The differential analysis is based on differences between result values of a scoring objective function. In some cases, the differential analysis determines at least one of the differences based on additional data structures, such as an augmented frequent-pattern tree. A probability function based on the differences is compared to a threshold probability At least one testing ruleset is modified based on the comparison. The IDS generator determines, for each class, a class-specific optimized ruleset based on the differential analysis of each class-specific candidate ruleset. The IDS generator creates an optimized interpretable decision set based on combined class-specific optimized rulesets for the multiple classes.

Fast and accurate rule selection for interpretable decision sets

The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

Detecting cognitive biases in interactions with analytics data

The present disclosure relates to systems, methods, and non-transitory computer readable media for training and utilizing a generative machine learning model to select one or more treatments for a client device from a set of treatments based on digital characteristics corresponding to the client device. In particular, the disclosed systems can train and apply a variational autoencoder with a task embedding layer that generates estimated effects for treatment combinations. For example, the disclosed systems receive, as input, digital characteristics corresponding to the client device and various treatment combinations. The disclosed systems apply the trained generative machine learning model with the task embedding layer to the digital characteristics to generate effect estimations for the various treatment combinations. Based on the effect estimations for the treatment combinations, the disclosed systems select one or more treatments to provide to the client device.

Utilizing a deep generative model with task embedding for personalized targeting of digital content through multiple channels across client devices

Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.

Detecting robotic internet activity across domains utilizing one-class and domain adaptation machine-learning models

A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.

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Bengaluru, India

Sunny Dhamnani