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October 2020

10/21: Information Session for Prospective IEOR MS & PhD Students

October 21 @ 3:30 pm - 5:00 pm
Zoom Webinar (Virtual), https://berkeley.zoom.us/meeting/94404355397 + Google Map

Learn more about the Berkeley IEOR MS & PhD Programs Interested in learning more about the University of California, Berkeley IEOR PhD and MS programs? At this information session, Professors Alper Atamturk, Candace Yano, Rajan Udwani, Paul Grigas and PhD students will give a brief overview of the program, its curriculum, and the diverse career possibilities within the field. .tribe-events-cal-links {display: none !important;}

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10/26: Noah Gans – Overbooking with Endogenous Demand

October 26 @ 3:30 pm - 4:30 pm
Zoom Webinar (Virtual), https://berkeley.zoom.us/meeting/94404355397

Abstract: Using airlines as a backdrop, we study optimal overbooking policies with endogenous customer demand, when customers internalize their expected cost of being bumped. We first consider the traditional setting in which compensation for bumped passengers is fixed and booking limits are the airline's only form of control. We provide sufficient conditions under which demand endogeneity leads to lower overbooking limits in this case. We then consider the broader problem of joint control of ticket price, bumping compensation, and booking…

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10/29: Innovation-X Roundtable – Advanced Manufacturing and Industry 4.0

October 29 @ 12:00 pm - 1:00 pm

The goal of the Innovation-X Roundtable series is to help industry adapt at this critical time. How can companies innovate to create the biggest impact for industry and society now and in the future? From these initial discussions, will emerge Discovery Projects, where we will leverage the resources of UC Berkeley faculty and students to create innovative solutions with Network members that guide the future of industry. Register

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November 2020

11/2: Jianqing Fan – Analysis of Deep Q-Learning

November 2 @ 3:30 pm - 4:30 pm
Zoom Webinar (Virtual), https://berkeley.zoom.us/meeting/94404355397

Abstract: Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Q-network (DQN) algorithm from both algorithmic and statistical perspectives. Specifically, we focus on a slight simplification of DQN that fully captures its key features. Under mild assumptions, we establish the algorithmic and statistical rates of convergence for the action-value functions of the iterative policy sequence obtained by DQN. In particular,…

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11/12: Innovation-X Roundtable – Future of Retail

November 12 @ 12:00 pm - 1:00 pm

The goal of the Innovation-X Roundtable series is to help industry adapt at this critical time. How can companies innovate to create the biggest impact for industry and society now and in the future? From these initial discussions, will emerge Discovery Projects, where we will leverage the resources of UC Berkeley faculty and students to create innovative solutions with Network members that guide the future of industry. Register

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