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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART;TZID=Asia/Singapore:20210305T100000
DTEND;TZID=Asia/Singapore:20210305T120000
DTSTAMP:20260426T001845
CREATED:20210304T080714Z
LAST-MODIFIED:20210304T084313Z
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SUMMARY:The Dao of Robustness
DESCRIPTION:We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution.  Unlike data-driven robust optimization approaches\, the decision maker does not have to size the  ambiguity set\, but specifies an acceptable target\, or loss of optimality compared to the baseline optimization model\, as a trade off for the model’s ability to withstand greater uncertainty.  We axiomatize the decision criterion associated with robustness optimization\, termed as the  fragility measure  and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints\, adaptive and dynamic optimization models. Similar to robust optimization\, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long. \n  \n\n\n\nName of Speaker\nProf Melvyn Sim\n\n\nSchedule \nFriday 5 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/84738208342?pwd=UTlkN3FqRTBRbFZ6dEJvcVpVYUlkdz09\n\n\nID\n847 3820 8342\n\n\nPassword\n008617\n\n\nTitle \nThe Dao of Robustness\n\n\nAbstract \nWe present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution.  Unlike data-driven robust optimization approaches\, the decision maker does not have to size the  ambiguity set\, but specifies an acceptable target\, or loss of optimality compared to the baseline optimization model\, as a trade off for the model’s ability to withstand greater uncertainty.  We axiomatize the decision criterion associated with robustness optimization\, termed as the  fragility measure  and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints\, adaptive and dynamic optimization models. Similar to robust optimization\, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long.\n\n\nAbout the Speaker\nDr Melvyn Sim is a professor at the Department of Analytics and Operations\, National University of Singapore. For the past twenty years\, he has been working the the area of optimization and decision making under uncertainty. \nFind out more about Prof Sim and Robust Optimization: https://youtu.be/eGXBd7KxjEY
URL:https://iora.nus.edu.sg/events/the-dao-of-robustness/
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DTSTART;TZID=Asia/Singapore:20210312T100000
DTEND;TZID=Asia/Singapore:20210312T113000
DTSTAMP:20260426T001845
CREATED:20210305T090935Z
LAST-MODIFIED:20210305T091145Z
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SUMMARY:Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
DESCRIPTION:Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary\, high frequency and large dimensional complex data such as cryptocurrency\, limit order book\, and renewable energy. She also works on business intelligence\, forecasting\, text mining and sentiment analysis\, and network analysis. Dr. Chen is Associate Professor in Department of Mathematics and Joint Appointee in Risk Management Institute (1 July 2019 to 30 June 2021)\, National University of Singapore. Dr. Chen is Associate Editor of 5 journals including Statistica Sinica (August 1\, 2017 to July 31\, 2023)\, Statistics and Its Interface\, Computational Statistics\, Digital Finance\, and Journal of Operations Research and Decisions. She is ISI Elected Member since March 2016. She is regular member of the Advisory Board of Institute of Statistical Mathematics\, Japan from 1 April 2018 to 31 March 2020. \n\n\n\nName of Speaker\nA/P Chen Ying\n\n\nSchedule \nFriday 12 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/87277632379?pwd=MElkRGxnd2x2QUg4VnFwMUp6WE9iZz09\n\n\nID\n872 7763 2379\n\n\nPassword\n247866\n\n\nTitle \nProbabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression\n\n\nAbstract \nProbabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. We propose a novel approach to construct probabilistic predictors for curves (PPC)\, which leads to a natural and new definition of quantiles in the context of curve-to-curve linear regression. There are three types of PPC: a predict set\, a predictive band and a predictive quantile\, and all of them are defined at a pre-specified nominal probability level. In the simulation study\, the PPC achieve promising coverage probabilities under a variety of data generating mechanisms. When applying to one day ahead forecasting for the French daily electricity load curves\, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy\, coverage rate and average length of the predictive bands. For example\, PPC achieve up to 2.8-fold of the coverage rate with much smaller average length of the predictive bands. The predictive quantile curves provide insightful information which is highly relevant to hedging risks in electricity supply management. (Joint work with Xiuqin Xu\, Yannig Goude and Qiwei Yao. Available at https://arxiv.org/abs/2009.01595)
URL:https://iora.nus.edu.sg/events/probabilistic-forecasting-for-daily-electricity-loads-and-quantiles-for-curve-to-curve-regression/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210326T100000
DTEND;TZID=Asia/Singapore:20210326T110000
DTSTAMP:20260426T001845
CREATED:20210329T060811Z
LAST-MODIFIED:20210329T061339Z
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SUMMARY:
DESCRIPTION:Dr. Swati Gupta is an Assistant Professor and Fouts Family Early Career Professor in the H. Milton Stewart School of Industrial & Systems Engineering\, and School of Computer Science (by courtesy) at Georgia Institute of Technology. She received a Ph.D. in Operations Research from MIT in 2017 and a joint Bachelors and Masters in CS from IIT\, Delhi in 2011. Dr. Gupta’s research interests are in optimization\, machine learning and algorithmic fairness. Her work spans various application domains such as revenue management\, energy and quantum computation. She received the NSF CISE Research Initiation Initiative (CRII) Award in 2019. She was also awarded the prestigious Simons-Berkeley Research Fellowship in 2017-2018\, where she was selected as the Microsoft Research Fellow in 2018. Dr. Gupta received the Google Women in Engineering Award in India in 2011. Dr. Gupta’s research is partially funded by the NSF and DARPA. \n\n\n\nName of Speaker\nDr Swati Gupta\n\n\nSchedule \nFriday 26 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/84784213927?pwd=eGs5U0Mwcm9XY1htcGlNQ3J5aEhCdz09\n\n\nID\n847 8421 3927\n\n\nPassword\n329485\n\n\nTitle \nMitigating the Impact of Bias in Selection Algorithms\n\n\nAbstract \nThe introduction of automation into the hiring process has put a spotlight on a persistent problem: discrimination in hiring on the basis of protected-class status. Left unchecked\, algorithmic applicant-screening can exacerbate pre-existing societal inequalities and even introduce new sources of bias; if designed with bias-mitigation in mind\, however\, automated methods have the potential to produce fairer decisions than non-automated methods. In this work\, we focus on selection algorithms used in the hiring process (e.g.\, resume-filtering algorithms) given access to a “biased evaluation metric”. That is\, we assume that the method for numerically scoring applications is inaccurate in a way that adversely impacts certain demographic groups. \nWe analyze the classical online secretary algorithms under two models of bias or inaccuracy in evaluations: (i) first\, we assume that the candidates belong to disjoint groups (e.g.\, race\, gender\, nationality\, age)\, with unknown true utility Z\, and “observed” utility Z/\beta for some unknown \beta that is group-dependent\, (ii) second\, we propose a “poset” model of bias\, wherein certain pairs of candidates can be declared incomparable.  We show that in the biased setting\, group-agnostic algorithms for online secretary problem are suboptimal\, often causing starvation of jobs for groups with \beta>1. We bring in techniques from matroid secretary literature and order theory to develop group-aware algorithms that are able to achieve certain “fair” properties\, while obtaining near-optimal competitive ratios for maximizing true utility of hired candidates in a variety of adversarial and stochastic settings. Keeping in mind the requirements of U.S. anti-discrimination law\, however\, certain group-aware interventions can be construed as illegal\, and we will conclude the talk by partially addressing tensions with the law and ways to argue legal feasibility of our proposed interventions. This talk is based on work with Jad Salem and Deven R. Desai.
URL:https://iora.nus.edu.sg/events/12506/
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