Date: | Sat, 23 Feb 2019 01:57:07 -0600 |
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From: | Shuchi Chawla <shuchi@xxxxxxxxxxx> |
Subject: | [theory students] Fwd: Northwestern Workshop (QTW) on "Optimization with Uncertainty" on March 8th (Friday) |
This looks super interesting but it's in two weeks. Let me know if you're interested in going and I'll inform the organizers of how many to expect. In the past students have carpooled and done a 1-2 day trip to attend these workshops.
Shuchi ---------- Forwarded message --------- From: Aravindan Vijayaraghavan <v.aravindan@xxxxxxxxx> Date: Fri, Feb 22, 2019 at 4:48 PM Subject: Northwestern Workshop (QTW) on "Optimization with Uncertainty" on March 8th (Friday) To: Shuchi Chawla <shuchi@xxxxxxxxxxx> Dear Shuchi, We are having our next Quarterly Theory Workshop (QTW) at Northwestern on March 8th (Friday). This workshop topic is Optimization with Uncertainty, and will feature talks by Nikhil Devanur, Anupam Gupta and Robert Kleinberg. Full details and registration are available on the workshop webpage: https://theory.cs.northwestern.edu/events/optimization-w-uncertainty/ It would be great if you could attend, and also forward this announcement to the rest of your group. We hope to see you there! Thanks, Aravindan, Jason, Kostya and Samir. ================================================== About the Series The Quarterly Theory Workshop brings in three or four theoretical computer science experts present their perspective and research on a common theme. Chicago and Midwest area researchers with interest in theoretical computer science are invited to attend. The technical program is in the morning and includes coffee and lunch. The afternoon of the workshop will allow for continued discussion between attendees and the speakers. Synopsis The focus of this workshop will be on optimization with uncertainty. Uncertainty arises in optimization problem when all the information relevant to the optimization is not available at the time of the optimization. Such uncertainty arises in online algorithms, mechanism design, and stochastic optimization. Theoretical frameworks include worst-case, Bayesian, and learning theoretic analyses. The speakers for this workshop are Nikhil Devanur, Robert Kleinberg, and Anupam Gupta. Logistics Date: Friday, March 8, 2019. Location: Seeley Mudd 3514, (map), Northwestern U, 2211 Campus Dr, Evanston, IL 60208. Transit: Noyes St. Purple Line (map). Parking: Validation for North Campus Parking Garage (map) available at workshop. Recommended Hotel: Hilton Orrington. Registration: TBA. Please bring your own name badge from past conference. Tentative Schedule 9:00-9:30: Continental Breakfast 9:30-9:35: Opening Remarks 9:35-10:20: Nikhil Devanur Online Algorithms, learning and game theory: linear, convex and beyond. 10:20-10:40: Coffee Break 10:40-11:25: Robert Kleinberg Approximately Optimal Sequential Search: Variations on a Theme by Weitzman 11:25-11:45: Coffee Break 11:45-12:30: Anupam Gupta Robust Algorithms for Secretaries and Bandits 12:30-1:30: Lunch Titles and Abstracts Speaker: Nikhil Devanur Title: Online Algorithms, learning and game theory: linear, convex and beyond. Online Stochastic Optimization is a class of problems that capture decision making under uncertainty. The algorithm has to make real time decisions as it sees new input without knowing future arrival. The initial motivating applications were formulated as linear programs, but many extensions need a convex programming formulation. Some mild stochastic assumptions about the input such as random arrival order can give near optimal algorithms. One such algorithm is a primal-dual algorithm, where the dual update is done via no-regret online learning. Convexity not only captures a broader class of problems but also clarifies the primaI-dual aspect and the connection to online learning. Finally, I will show how game theoretic considerations lead to formulations that are beyond convexity. Speaker: Robert Kleinberg Title: Approximately Optimal Sequential Search: Variations on a Theme by Weitzman To optimize, one must often invest resources exploring the values of different alternatives. For example, firms devote time and money to researching potential acquisition targets or to interviewing job candidates; college applicants visit schools before selecting one. The âbox problemâ, introduced by Martin Weitzman in 1979, models this as a problem of choosing one of finitely many alternatives with independent random values, subject to a cost that must be invested to discover an alternativeâs value before it can be selected. The simplicity of the optimal solution, called âPandoraâs ruleâ, belies the fragility of the assumptions on which its optimality is based. I will present a new proof of the optimality of Pandoraâs rule that, unlike Weitzmanâs original proof, combines nicely with tools used to analyze approximation algorithms, game equilibria, and online selection rules. This enables us to design and analyze approximately optimal policies for environments similar to the box problem but with competing agents, exogenous order of inspection, or non-obligatory inspection. The first part of the talk is joint work with Bo Waggoner and Glen Weyl; the latter part is joint work with Hedyeh Beyhaghi. Speaker: Anupam Gupta Title: Robust Algorithms for Secretaries and Bandits In the secretary problem, a set of items are revealed to us in random order, and we want to maximize the probability of picking the best among them. In the (stochastic) multi-armed bandit problem, we perform pulls from a set of arms, each with a fixed but unknown payoff probability, and want to minimize the regret. Both these problems are well-studied in the sequential decision-making literature. We consider the semi-adversarial setting where an adversary is allowed to introduce some limited amount of corruptions. We show that classical algorithms fail badly in the presence of corruptions, and then we give algorithms that are more robust to corruptions. This is based on joint works with Domagoj Bradac, Sahil Singla, and Goran Zuzic, and with Tomer Koren and Kunal Talwar. |
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