International Business Machines Corporation

Kanthi Sarpatwar

Nalini Kanta Ratha

Roman Vaculin

Hong Min

Sharathchandra Umapathirao Pankanti

Karthikeyan Shanmugam

Lam Nguyen

Saket Sathe

Venkata Sitaramagiridharganesh Ganapavarapu

Stephane Michel

Yuan Yuan Jia

Surya Shravan Kumar Sajja

Karthik Nandakumar

A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

Optimizer agnostic explanation system for large scale schedules

A method, apparatus and computer program product that provides multi-phase privacy-preserving inferencing in a high throughput data environment, e.g., to facilitate fraud prediction, detection and prevention. In one embodiment, two (2) machine learning models are used, a first model that is trained in the clear on first transaction data, and a second model that is trained in the clear but on the first transaction data, and user data. The first model is used to perform inferencing in the clear on the high throughput received data. In this manner, the first model provides a first level evaluation of whether a particular transaction might be fraudulent. If a transaction is flagged in this first phase, a second more secure inference is then carried out using the second model. The inferencing performed by the second model is done on homomorphically encrypted data. Thus, only those transactions marked by the first model are passed to the second model for secure evaluation.

Multi-phase privacy-preserving inferencing in a high volume data environment

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Elmsford, NY, United States of America

Kanthi Sarpatwar