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Seminar
Speaker
Kartyek Murthy (Singapore University of Design and Technology)
Date & Time
Tue, 25 April 2023, 09:00 to 10:00
Venue
Online Seminar
Resources
Abstract

The ability to estimate and control tail risks, besides being an integral part of quantitative risk management, is central to running operations requiring high service levels and ML-driven cyber- physical systems with high-reliability specifications. Despite this significance, scalable algorithmic approaches have remained elusive: This is due to the rarity with which relevant risky samples get observed, and the critical role experts play in devising variance reduction techniques based on instance-specific large deviations studies. Our goal in this talk is to examine if such tailored variance reduction benefits can be instead achieved by instance-agnostic algorithms capable of scaling well across multitude of tail estimation and optimisation tasks. To this end, we identify an elementary transformation whose push-forward automatically induces efficient importance sampling distributions across a variety of models by replicating the concentration properties observed in less rare samples. This obviates the need to explicitly identify a good change of measure, thereby overcoming the primary bottleneck in the use of importance sampling beyond highly stylized models. Our novel approach is guided by a large deviations principle which brings out the phenomenon of self-similarity of zero variance distributions. Being a nonparametric phenomenon, this self-similarity is manifest in a rich set of objectives modeled with tools such as linear programs, piecewise linear/quadratic objectives, feature maps specified in terms of neural networks, etc., together with a spectrum of light and heavy-tailed multivariate distributions.

Zoom link: https://us02web.zoom.us/j/81379290349 

Meeting ID: 813 7929 0349

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