View source for 18099579. SYSTEMS AND METHODS FOR ADJUSTMENT-BASED CAUSALLY ROBUST PREDICTION simplified abstract (TOYOTA RESEARCH INSTITUTE, INC.)

=SYSTEMS AND METHODS FOR ADJUSTMENT-BASED CAUSALLY ROBUST PREDICTION=


==Organization Name==

[[:Category:TOYOTA RESEARCH INSTITUTE, INC.|TOYOTA RESEARCH INSTITUTE, INC.]]


[[Category:TOYOTA RESEARCH INSTITUTE, INC.]]

==Inventor(s)==

[[:Category:Guy Rosman of Newton MA (US)|Guy Rosman of Newton MA (US)]][[Category:Guy Rosman of Newton MA (US)]]

[[:Category:Igor Gilitschenski of Toronto (CA)|Igor Gilitschenski of Toronto (CA)]][[Category:Igor Gilitschenski of Toronto (CA)]]

[[:Category:Xiongyi Cui of Somerville MA (US)|Xiongyi Cui of Somerville MA (US)]][[Category:Xiongyi Cui of Somerville MA (US)]]

[[:Category:Stephen G. Mcgill of Broomall PA (US)|Stephen G. Mcgill of Broomall PA (US)]][[Category:Stephen G. Mcgill of Broomall PA (US)]]

==SYSTEMS AND METHODS FOR ADJUSTMENT-BASED CAUSALLY ROBUST PREDICTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18099579 titled 'SYSTEMS AND METHODS FOR ADJUSTMENT-BASED CAUSALLY ROBUST PREDICTION

'''Simplified Explanation:'''
Prediction training systems that use small variation data sets instead of large passive data sets to train models are described. These smaller variation data sets mimic interventions and are used to add loss terms to the model. The model may mimic interventions by training with variation data sets collected from real-world events.

'''Key Features and Innovation:'''
* Utilizes small variation data sets for training models instead of large passive data sets.
* Mimics interventions by adding loss terms to the model during training.
* Models may replace values in predictions during forward model computation to mimic interventions.

'''Potential Applications:'''
This technology could be applied in various fields such as finance, healthcare, and marketing for predictive modeling and decision-making processes.

'''Problems Solved:'''
This technology addresses the challenge of training prediction models with limited data by using small variation data sets that mimic interventions.

'''Benefits:'''
* Improved accuracy in prediction models.
* Better mimicry of real-world interventions.
* Enhanced decision-making capabilities in various industries.

'''Commercial Applications:'''
Predictive analytics software companies could utilize this technology to enhance their prediction models and offer more accurate and reliable solutions to their clients in industries such as finance, healthcare, and marketing.

'''Prior Art:'''
Readers interested in exploring prior art related to this technology could start by researching machine learning techniques for predictive modeling and intervention mimicking in training data sets.

'''Frequently Updated Research:'''
Stay updated on the latest advancements in machine learning techniques for predictive modeling and intervention mimicking to enhance the effectiveness of this technology.

'''Questions about Prediction Training Systems:'''
1. How does this technology improve the accuracy of prediction models compared to traditional training methods?
2. What are the potential limitations of using small variation data sets in training prediction models?


==Original Abstract Submitted==

Prediction training systems that rely on small variation data sets instead of training the model using large passive data sets are disclosed. The smaller variation data sets are used to add loss terms that may mimic intervention. One or more models may be included that mimic the intervention by training with variation datasets. The variation datasets may be collected from such interventions in real world events. The model may mimic an intervention by replacing values in the prediction during a forward model computation.



[[Category:G06N20/00]]
[[Category:CPC_G06N20/00]]