Anand Deo | DJ Sanghvi (original) (raw)
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Papers by Anand Deo
2021 Winter Simulation Conference (WSC), 2021
This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined ... more This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixed integer linear optimization formulation. Assuming only black-box access to the loss and the distribution of the underlying random vector, the paper presents an efficient IS algorithm for estimating the Value at Risk and Conditional Value at Risk. The key challenge in any IS procedure, namely, identifying an appropriate change-of-measure, is automated with a self-structuring IS transformation that learns and replicates the concentration properties of the conditional excess from less rare samples. The resulting estimators enjoy asymptotically optimal variance reduction when viewed in the logarithmic scale. Simulation experiments highlight the efficacy and practicality of the proposed scheme.
Motivated by the increasing adoption of models which facilitate greater automation in risk manage... more Motivated by the increasing adoption of models which facilitate greater automation in risk management and decision-making, this paper presents a novel Importance Sampling (IS) scheme for measuring distribution tails of objectives modelled with enabling tools such as feature-based decision rules, mixed integer linear programs, deep neural networks, etc. Conventional efficient IS approaches suffer from feasibility and scalability concerns due to the need to intricately tailor the sampler to the underlying probability distribution and the objective. This challenge is overcome in the proposed black-box scheme by automating the selection of an effective IS distribution with a transformation that implicitly learns and replicates the concentration properties observed in less rare samples. This novel approach is guided by a large deviations principle that brings out the phenomenon of self-similarity of optimal IS distributions. The proposed sampler is the first to attain asymptotically opti...
The public health threat arising from the worldwide spread of COVID-19 led the Government of Indi... more The public health threat arising from the worldwide spread of COVID-19 led the Government of India to announce a nation-wide‘lockdown’ starting 25 March 2020, an extreme social distancing measure aimed at reducing contact rates in the population and slowing down the transmission of the virus. In this work, we present the outcomes of our city-scale simulation experiments that suggest how the disease may evolve once restrictions are lifted. The idea of modelling a large metropolis is appropriate since the spread in Maharashtra, NCR, Tamil Nadu, etc. is mostly in well connected large cities. We study the impact of case isolation, home quarantine, social distancing of the elderly, school and college closures, closure of offices, odd-even strategies, etc., as components of various post-lockdown restrictions that might remain in force for some time after the complete
ArXiv, 2021
Motivated by the increasing adoption of models which facilitate greater automation in risk manage... more Motivated by the increasing adoption of models which facilitate greater automation in risk management and decision-making, this paper presents a novel Importance Sampling (IS) scheme for measuring distribution tails of objectives modeled with enabling tools such as feature-based decision rules, mixed integer linear programs, deep neural networks, etc. Conventional efficient IS approaches suffer from feasibility and scalability concerns due to the need to intricately tailor the sampler to the underlying probability distribution and the objective. This challenge is overcome in the proposed black-box scheme by automating the selection of an effective IS distribution with a transformation that implicitly learns and replicates the concentration properties observed in less rare samples. This novel approach is guided by a large deviations principle that brings out the phenomenon of self-similarity of optimal IS distributions. The proposed sampler is the first to attain asymptotically optim...
2020 59th IEEE Conference on Decision and Control (CDC), 2020
Journal of the Indian Institute of Science, 2020
SSRN Electronic Journal, 2019
SSRN Electronic Journal, 2017
Operations Research, 2021
Interpretable, Computationally Tractable Approximate Parameter Estimation for Corporate Defaults
2021 Winter Simulation Conference (WSC), 2021
This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined ... more This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixed integer linear optimization formulation. Assuming only black-box access to the loss and the distribution of the underlying random vector, the paper presents an efficient IS algorithm for estimating the Value at Risk and Conditional Value at Risk. The key challenge in any IS procedure, namely, identifying an appropriate change-of-measure, is automated with a self-structuring IS transformation that learns and replicates the concentration properties of the conditional excess from less rare samples. The resulting estimators enjoy asymptotically optimal variance reduction when viewed in the logarithmic scale. Simulation experiments highlight the efficacy and practicality of the proposed scheme.
Motivated by the increasing adoption of models which facilitate greater automation in risk manage... more Motivated by the increasing adoption of models which facilitate greater automation in risk management and decision-making, this paper presents a novel Importance Sampling (IS) scheme for measuring distribution tails of objectives modelled with enabling tools such as feature-based decision rules, mixed integer linear programs, deep neural networks, etc. Conventional efficient IS approaches suffer from feasibility and scalability concerns due to the need to intricately tailor the sampler to the underlying probability distribution and the objective. This challenge is overcome in the proposed black-box scheme by automating the selection of an effective IS distribution with a transformation that implicitly learns and replicates the concentration properties observed in less rare samples. This novel approach is guided by a large deviations principle that brings out the phenomenon of self-similarity of optimal IS distributions. The proposed sampler is the first to attain asymptotically opti...
The public health threat arising from the worldwide spread of COVID-19 led the Government of Indi... more The public health threat arising from the worldwide spread of COVID-19 led the Government of India to announce a nation-wide‘lockdown’ starting 25 March 2020, an extreme social distancing measure aimed at reducing contact rates in the population and slowing down the transmission of the virus. In this work, we present the outcomes of our city-scale simulation experiments that suggest how the disease may evolve once restrictions are lifted. The idea of modelling a large metropolis is appropriate since the spread in Maharashtra, NCR, Tamil Nadu, etc. is mostly in well connected large cities. We study the impact of case isolation, home quarantine, social distancing of the elderly, school and college closures, closure of offices, odd-even strategies, etc., as components of various post-lockdown restrictions that might remain in force for some time after the complete
ArXiv, 2021
Motivated by the increasing adoption of models which facilitate greater automation in risk manage... more Motivated by the increasing adoption of models which facilitate greater automation in risk management and decision-making, this paper presents a novel Importance Sampling (IS) scheme for measuring distribution tails of objectives modeled with enabling tools such as feature-based decision rules, mixed integer linear programs, deep neural networks, etc. Conventional efficient IS approaches suffer from feasibility and scalability concerns due to the need to intricately tailor the sampler to the underlying probability distribution and the objective. This challenge is overcome in the proposed black-box scheme by automating the selection of an effective IS distribution with a transformation that implicitly learns and replicates the concentration properties observed in less rare samples. This novel approach is guided by a large deviations principle that brings out the phenomenon of self-similarity of optimal IS distributions. The proposed sampler is the first to attain asymptotically optim...
2020 59th IEEE Conference on Decision and Control (CDC), 2020
Journal of the Indian Institute of Science, 2020
SSRN Electronic Journal, 2019
SSRN Electronic Journal, 2017
Operations Research, 2021
Interpretable, Computationally Tractable Approximate Parameter Estimation for Corporate Defaults