Risk-based education based on M-location

Pawan Whig

Abstract


In this article, we investigate a novel class of hazards that go much beyond the traditional mean-variance risk functions by being described in terms of the position and deviation of the loss distribution. The class admits finite-sample stationarity guarantees for stochastic gradient methods, it is simple to interpret and adjust, closely linked to M-estimators of the loss location, and has a significant impact on the test loss distribution, giving us control over symmetry and deviations that are not possible under naive ERM. The class can be easily implemented as a wrapper around any smooth loss.

References


Whig, P., & Ahmad, S. N. (2014). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.

Gupta, K., & Jiwani, N. (2020). Effects of COVID-19 risk controls on the Global Supply Chain. Transactions on Latest Trends in Artificial Intelligence, 1(1).

Gessner, Guy H., Volonino, Linda (2005). Quick Response Improves Returns on Business Intelligence Investments, Information Systems Management, 22(3), 66 -74.

Whig, P. and Naseem Ahmad, S. (2014), "Simulation of linear dynamic macro model of photo catalytic sensor in SPICE", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 33 No. 1/2, pp. 611-629. https://doi.org/10.1108/COMPEL-09-2012-0160

Ghoshal, S., Kim, S. K. (1986). Building Effective Intelligence Systems for Competitive Advantage, Sloan Management Review, 28(1), 49–58.

Verma, T., Gupta, P., & Whig, P. (2015). Sensor Controlled Sanitizer Door Knob with Scan Technique. In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2 (pp. 261-266). Springer, Cham.


Refbacks

  • There are currently no refbacks.