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October 2021

10/28: Berkeley Master of Analytics Info Session at UCSC Grad & Professional School Fair (virtual)

October 28 @ 10:00 am - 1:00 pm
Zoom Webinar (Virtual)

The Master of Analytics program at UC Berkeley’s Industrial Engineering and Operations Research Department will be present at the UCSC Graduate & Professional School Fair. Staff will be available to answer your questions and discuss our curriculum, program structure, dedicated career services, and admissions criteria. *** About UC Berkeley Master of Analytics Program The 11-month Master of Analytics program trains students in data-driven analytical methods and tools for optimization, statistics, simulation, and risk management with relevant industry context so that the graduates are…

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November 2021

11/1: Aranyak Mehta – Google Research Scientist (in-person)

November 1 @ 3:30 pm - 4:30 pm
George B. Dantzig Auditorium – 1174 Etcheverry Hall, Etcheverry Hall
Berkeley, CA 94720 United States
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More Information to come.

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11/15: Daniela H Saban – Stanford University (in-person)

November 15 @ 3:30 pm - 4:30 pm
George B. Dantzig Auditorium – 1174 Etcheverry Hall, Etcheverry Hall
Berkeley, CA 94720 United States
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More Information to come.

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11/17: Master of Analytics Information Session (virtual)

November 17 @ 5:00 pm - 6:00 pm
Zoom Webinar (Virtual)

Join the Master of Analytics Chair and Graduate Advisor for an online information session. Please bring any program and admissions-related questions you may have. We look forward to providing you with insight and guidance. Registration Information: Register Online

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11/22: Ali Aouad – Algorithmic Collusion in Assortment Games (in-person)

November 22 @ 3:30 pm - 4:30 pm
George B. Dantzig Auditorium – 1174 Etcheverry Hall, Etcheverry Hall
Berkeley, CA 94720 United States

Joint work with Arnoud v. den Boer   Abstract: This paper contributes to the ongoing debate on the plausibility of tacit collusion between sellers in algorithmic marketplaces, which can be detrimental to customers and social welfare. We study a broad class of assortment decisions routinely made by sellers on online platforms, including which set of products is offered to customers, at what price, and how are they displayed. In this context, algorithmic decision-support tools are extensively studied in the operations literature and widely…

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11/29: Jeffrey Ichnowski – Accelerating Quadratic Optimization with Reinforcement Learning (in-person)

November 29 @ 3:30 pm - 4:30 pm
George B. Dantzig Auditorium – 1174 Etcheverry Hall, Etcheverry Hall
Berkeley, CA 94720 United States

Abstract: First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved. These methods face two persistent challenges: manual hyperparameter tuning and convergence time to high-accuracy solutions. To address these, we explore how Reinforcement Learning (RL) can learn a policy to tune parameters to accelerate convergence. In experiments with well-known QP benchmarks we find that our RL policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x.…

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11/29: Mahan Tajrobehkar – Perturbed Gradient Descent Adapted with Occupation Time (in-person)

November 29 @ 3:30 pm - 4:30 pm
George B. Dantzig Auditorium – 1174 Etcheverry Hall, Etcheverry Hall
Berkeley, CA 94720 United States

Abstract: We develop further the idea of perturbed gradient descent (PGD), by adapting perturbation with the history of states via the notion of occupation time. The proposed algorithm, perturbed gradient descent adapted with occupation time (PGDOT), is shown to converge at least as fast as the PGD algorithm and is guaranteed to avoid getting stuck at saddle points. The analysis is corroborated by empirical studies, in which a mini-batch version of PGDOT is shown to outperform alternatives such as mini-batch…

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