Upcoming Webinars

Active Learning by Learning



Tue, Apr 20, 2021 9:30 PM - 10:30 PM EDT
Abstract: Active learning is an important technique that helps reduce labeling efforts in machine learning applications. Previously, most active learning strategies are constructed based on some human-designed philosophy; that is, they reflect what human beings assume to be "good labeling questions." However, given that a single human-designed philosophy is unlikely to work on all scenarios, choosing and blending those strategies under different scenarios is an important but challenging practical task. This paper tackles this task by letting the machines adaptively "learn" from the performance of a set of given strategies on a particular data set. More specifically, we design a learning algorithm that connects active learning with the well-known multi-armed bandit problem. Further, we postulate that, given an appropriate choice for the multi-armed bandit learner, it is possible to estimate the performance of different strategies on the fly. Extensive empirical studies of the resulting algorithm confirm that it performs better than strategies that are based on human-designed philosophy. We release an open-source package libact that includes the algorithm to make active learning more realistic for general users. Biography:  

Prof. Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008, and was promoted to an associate professor in 2012, and has been a professor since August 2017. Between 2016 and 2019, he worked as the Chief Data Scientist of Appier, a startup company that specializes in making AI easier in various domains, such as digital marketing and business intelligence. Currently, he keeps growing with Appier as its Chief Data Science Consultant. 


Understanding the Complexity of Financial Systems of Systems 

Registration: https://attendee.gotowebinar.com/register/6633012115896407311

Thu, May 20, 2021 11:00 AM - 12:00 PM EDT

Why are the financial markets so problematical? One reason is that financial markets consist of many systems – having other complex systems as components – that interact in complicated ways. The focus of this webinar is to explain the complexity of several financial systems in terms of several properties that are characteristic of large, diverse, complex systems. We distill this framework for financial systems of systems to include properties describing interactions among other system components; properties relating to interactions within the system environment; and properties relating to interactions with time. In this webinar, we discuss how these properties impact financial systems involved with high frequency trading (connectivity vs. latency arbitrage); market fragmentation (autonomy vs. regulatory-induced feedback); and credit (model diversity vs. copula model risk).
Biography: Roy S. Freedman founded Inductive Solutions, Inc., to develop commercial AI software for genetic algorithms, neural networks, and case-based reasoning. His primary consulting interests involve solving problems associated with trading, operations, risk management, and compliance by using mathematics, statistics, and computer technology. As a consultant, he worked with stock exchanges, buy-side and sell-side firms, technology companies, startups, and government institutions. During the third term of the Bloomberg mayoral administration, he was the principal AI consultant to the Chief Information Officer of New York City Health & Human Services. As an expert witness, he testified on the federal level concerning financial and regulatory technology related to high-frequency trading. Dr. Freedman teaches various courses related to financial systems (FinTech) and regulatory systems (RegTech) as adjunct professor in the department of Finance and Risk Engineering at the New York University Tandon School of Engineering (formerly Polytechnic University).

Webinars Calendar

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