Conference on Learning Theory(**COLT**):

TCS postdoc Xue Chen presented a joint paper “Active Regression via Linear-Sample Sparsification” with Eric Price (UT Austin). This paper gives an efficient algorithm with an optimal sample complexity for the classical problem of linear regression. Its techniques yield improved results for the non-linear sparse Fourier transform setting.

TCS Ph.D. student Yingkai Li presented a joint paper with Yining Wang (CMU) and Yuan Zhou (UIUC). The paper “Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits” obtained almost tight dependence on the time horizon for regret minimization in linear bandits. One of the main results is to show that there is an extra sqrt(log T) factor in the lower bound, revealing a regret scaling quite different from classical multi-armed bandits. Our proof techniques include variable confidence levels and a careful analysis of layer sizes of SupLinUCB on the upper bound side, and delicately constructed adversarial sequences showing the tightness of elliptical potential lemmas on the lower bound side.

Economics and Computation(**EC**):

TCS Ph.D. alumnus Sam Taggart (Assist. Prof. Oberlin College) presented a joint paper with TCS Prof. Jason Hartline in the joint session of EC and STOC.

TCS Prof. Jason Hartline presented a joint paper with TCS Ph.D. student Aleck Johnsen, Dennis Nekipelov (UVA), and Onno Zoeter (Booking.com).

TCS Ph.D. student Yingkai Li presented a joint paper with TCS Prof. Jason Hartline and TCS Ph.D. student Yiding Feng.

TCS Ph.D. student Chenhao Zhang had a joint paper with Nick Gravin (Shanghai University of Finance and Economics), Yaonan Jin (Columbia University) and Pinyan Lu (Shanghai University of Finance and Economics). The paper “Optimal Budget-Feasible Mechanisms for Additive Valuations” obtained tight approximation guarantee for budget-feasible mechanisms with an additive buyer. The paper proposes two-stage mechanisms that composite price-posting schemes with a pruning mechanism which greedily excludes the items with low value-per-cost ratios. A tight 2-approximation against the Knapsack and a tight 3-approximation against the Fractional-Knapsack are obtained by the proposed randomized and deterministic mechanisms respectively. Link to Video.

Symposium on Parallelism in Algorithms and Architectures(**SPAA**):

TCS Prof. Samir Khuller had a joint paper with visiting TCS PhD student Sheng Yang(UMD), Mosharaf Chowdhury(UMich), Manish Purohit(Google), and Jie You(UMich). The paper “Near Optimal Coflow Scheduling in Networks” focuses on the coflow scheduling problem which studies scheduling and data communication inter and intra datacenters. The main result is a randomized 2 approximation algorithm, significantly improving prior work both in theory and in practice.

Symposium on the Theory of Computing(**STOC**):

TCS Prof. Konstantin Makarychev had a paper with Yury Makarychev(TTIC) and Ilya Razenshteyn (Microsoft Research). This paper, “Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering“, shows that the cost of the optimal solution for Euclidean k-means or k-medians clustering is preserved up to a factor of (1+ε) under a projection onto a random O(log(k/ε)/ε2)-dimensional subspace. For k-means, this result resolves an open problem posed by Cohen, Elder, Musco, Musco, and Persu (STOC 2015); for k-medians, it answers a question raised by Kannan.

]]>TCS Postdoc Huck Bennett had a joint paper with Alexander Golovnev (Columbia and Yahoo Research) and Noah Stephens-Davidowitz (Princeton). The paper, “On The Quantitative Hardness of CVP,” initiates the study of the fine-grained complexity of *lattice problems*,* *a study which is important to the rapidly developing field of lattice-based cryptography. As its main result, the paper shows strong hardness of the Closest Vector Problem (CVP) with certain parameters assuming the Strong Exponential Time Hypothesis (SETH).

TCS Prof. Aravindan Vijayaraghavan had a joint paper with Oded Regev

(NYU). The paper, “Learning Mixtures of Well-Separated Gaussians,”

studies the classic problem of learning a mixture of k spherical

Gaussian distributions. The paper tries to characterize the

minimum amount of separation needed between the components to

estimate the parameters (means) of the Gaussians, and presents lower

bounds and upper bounds towards this end.

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Dr. Makarychev is a theoretical computer scientist working on approximation algorithms, beyond worst-case analysis, applications of high-dimension geometry to computer science, and combinatorial optimization for designing efficient algorithms for computationally hard problems.

Dr. Makarychev joins Northwestern from Microsoft Research in Redmond, WA (2012-2016) and IBM Research Labs in Yorktown Heights, NY (2007-2012). Further details of his background can be found on his personal webpage.

Please click here for details, and the announcement on Northwestern homepage.

]]>One of the priority areas for these positions is algorithms. The teaching component of this position would be the undergraduate algorithms or discrete math courses and an advanced elective in the fellow’s research area.

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The ACM-ICPC (Association for Computing Machinery – International Collegiate Programming Contest) is a multi-tier, team-based, programming competition. Headquartered at Baylor University, Texas, it operates according to the rules and regulations formulated by the ACM. The contest participants come from over 2,000 universities that are spread across 80 countries and six continents.

Details can be found here.

]]>Ph.D. student Manolis Pountourakis presented two papers. At GAMES, he presented his paper Optimal Auctions vs. Anonymous Pricing (joint with with coauthors Saeed Alaei, Northwestern Prof. Jason Hartline, Rad Niazadeh, and Yang Yuan). The paper proves that eBay’s Auctions and Buy-It-Now (posted pricings) are within at least an *e = 2.718* factor of the revenue optimal auction (which may be asymmetric and very complicated). This is a worst-case bound that is quite difficult to prove; in practice the eBay’s sales mechanisms likely do much better. This paper previously appeared in FOCS 2015 and with this video of the talk. At EC, he presented Procrastination with Variable Present Bias (joint with coauthors Nick Gravin, Nicole Immorlica, and Brendan Lucier). Individuals attempting to complete a task often procrastinate, making plans and then failing to follow through. One well-known model of such behavioral anomalies is present-bias discounting: individuals over-weight present costs by a bias factor. This paper introduces a variant of this model where the bias factor varies across time, identifies a connection between the planning problem with variable present bias and optimal pricing, and uses this connection to bound the cost of procrastination.

Nima Haghpanah, a former Ph.D. student and now faculty member in the Economics Department at Penn State, presented two papers. At GAMES he presented Multi-dimensional Virtual Values and Second-degree Price Discrimination (joint with Northwestern Prof. Jason Hartline). This paper studies multi-dimensional mechanism design. It develops a method for solving for multi-dimensional virtual values, the pointwise optimization of which gives the optimal mechanism. It uses these virtual values to characterize the family of distributions where second-degree price discrimination (e.g., with a high- and low-quality version of a product) is not profitable. At EC he presented Sequential Mechanisms with ex-post Participation Guarantees (joint with Profs. Itai Ashlagi of Stanford and Constantinos Daskalakis). The paper identifies optimal and approximately optimal mechanisms in dynamic setting in which a seller and a buyer interact repeatedly. In contrast to standard settings, in this paper the outcomes of a mechanism must be acceptable to the buyer even after all uncertainty is resolved.

At the second Workshop on Algorithmic Game Theory and Data Science (a part of the EC program), Ph.D. student Sam Taggart presented Non-revelation Mechanism Design (joint with Northwestern Prof. Jason Hartline). This paper shows how to better design Bayes-Nash mechanisms, e.g., with winner-pays-bid or all-pay semantics, in a repeated environment by “linking decisions” across the repetitions. The designed mechanisms are asymptotically optimal with the number of repetitions that are linked.

At EC, Prof. Jason Hartline presented A/B Testing of Auctions (joint with Shuchi Chawla and Denis Nekipelov). This paper shows how the counter factual revenue of a mechanism B can be estimated from the equilibrium bids in a mechanism C which is the convex combination of mechanism A and B. For example, an auctioneer with multiple units can determine whether selling one unit or two units gives higher revenue by running the auction that probabilistically sells one or two units. The methods applies generally to position auctions to both winner-pays-bid and all-pay semantics.

Adjunct Northwestern Prof. Nicole Immorlica presented two papers. At the Workshop on Economics of Cloud Computing she presented On-demand or Spot? Selling the Cloud to Risk-averse Customers (joint with former Ph.D. student Darrell Hoy and Nikhil Devanur). In Amazon EC2, cloud resources are sold through a combination of the on-demand market, in which customers buy resources at a fixed price, and the spot market, in which customers bid for an uncertain supply of excess resources. This paper showed that such a dual market system improves upon key objectives when customers have heterogeneous risk attitudes: the welfare and revenue of its unique equilibrium is larger than that of spot markets alone and the efficiency is larger than only on-demand markets.

At a GAMES affiliated evening soirée with the Brightlands Young Professionals network Prof. Immorlica gave a Roy-Lichtenstein-themed public lecture entitled “Market Design without Money”. As a market-designer, a primary goal is to allocate resources to individuals in a way that maximizes the total value of the community for the allocation. Selling the resources is a great way to achieve this: if someone is willing to pay a high price for something, then they must have a high value for it as well. However, in many settings of interest, payments are infeasible. This could be due to repugnance, as in the assignment of public schools to children or cadaver kidneys to patients. Or perhaps the market technologies can not support financial transactions, e.g., users of an online review system like TripAdvisor may not have bank accounts linked to their user accounts. This talk explored using alternative incentives including risk, waiting time, and social status, in place of money to help optimize markets in such scenarios.

Great work to the presenters and coauthors of these papers!

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