ACM Transactions on

Economics and Computation (TEAC)

Latest Articles

Farewell Editorial

Computing Dominance-Based Solution Concepts

Two common criticisms of Nash equilibrium are its dependence on very demanding epistemic assumptions and its computational intractability. We study the computational properties of less demanding set-valued solution concepts that are based on varying notions of dominance. These concepts are intuitively appealing, always exist, and admit unique... (more)

An Antifolk Theorem for Large Repeated Games

In this article, we study infinitely repeated games in settings of imperfect monitoring. We first prove a family of theorems showing that when the signals observed by the players satisfy a condition known as (ε, γ)-differential privacy, the folk theorem has little bite: for values of ε and γ sufficiently small, for a fixed... (more)

Distributed Matching with Mixed Maximum-Minimum Utilities

In this article, we study distributed agent matching with search friction in environments characterized by costly exploration, where each... (more)

Provision-After-Wait with Common Preferences

In this article, we study the Provision-after-Wait problem in healthcare (Braverman, Chen, and Kannan, 2016). In this setting, patients seek a medical procedure that can be performed by different hospitals of different costs. Each patient has a value for each hospital and a budget-constrained government/planner pays for the expenses of the... (more)

Posting Prices with Unknown Distributions

We consider a dynamic auction model, where bidders sequentially arrive to the market. The values of the bidders for the item for sale are independently drawn from a distribution, but this distribution is unknown to the seller. The seller offers a personalized take-it-or-leave-it price for each arriving bidder and aims to maximize revenue. We study... (more)


New Editors-In-Chief

David Pennock and Ilya Segal took over as co-editors-in-chief in March 2017.

About TEAC

The ACM Transactions on Economics and Computation (TEAC) is a journal focusing on the intersection of computer science and economics. Of interest to the journal is any topic relevant to both economists and computer scientists, including but not limited to the following: algorithmic game theory, mechanism design, design and analysis of electronic markets, computation of equilibria, cost of strategic behavior and cost of decentralization, learning in games and markets, systems resilient against malicious agents, economics of computational advertising, paid search auctions, agents in networks, electronic commerce, computational social choice, recommendation/reputation/trust systems, and privacy.

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Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring

We study the design of service exchange platforms in which long-lived anonymous users exchange services with each other. The users are randomly matched into pairs of clients and servers repeatedly, and each server can choose whether to provide high-quality or low-quality services to the
client with whom it is matched. Since the users are anonymous and incur high costs (e.g. exert high effort) in providing high-quality services, it is crucial that the platform incentivizes users to provide high-quality services. Rating mechanisms have been shown to work effectively as incentive schemes in such platforms. A rating mechanism labels each user by a rating, which summarizes the user's past behaviors, recommends a desirable behavior to each server (e.g., provide higher-quality services for clients with higher ratings), and updates each server's rating based on the recommendation and its client's report on the service quality. Based on this recommendation, a low-rating user is less likely to obtain high-quality services, thereby providing users with incentives to obtain high ratings by providing high-quality services.

However, if monitoring or reporting is imperfect -- clients do not perfectly assess the quality or the reports are lost -- a user's rating may not be updated correctly. In the presence of such errors, existing rating mechanisms cannot achieve the social optimum. In this paper, we propose the first rating mechanism that does achieve the social optimum, even in the presence of monitoring or reporting errors. On one hand, the socially-optimal rating mechanism needs to be complicated enough, because the optimal recommended behavior depends not only on the current rating distribution, but
also (necessarily) on the history of past rating distributions in the platform. On the other hand, we prove that the social optimum can be achieved by "simple" rating mechanisms that use binary rating labels and a small set of (three) recommended behaviors. We provide design guidelines of socially-optimal rating mechanisms, and a low-complexity online algorithm for the rating mechanism to determine the optimal recommended behavior.

The VCG Mechanism for Bayesian Scheduling

Fast Convergence in the Double Oral Auction

A classical trading experiment consists of a set of unit demand buyers and unit supply sellers with identical items. Each agent's value or opportunity cost for the item is their private information and preferences are quasi-linear. Trade between agents employs a double oral auction (DOA) in which both buyers and sellers call out bids or offers which an auctioneer recognizes. Transactions resulting from accepted bids and offers are recorded. This continues until there are no more acceptable bids or offers. Remarkably, the experiment consistently terminates in a Walrasian price. The main result of this paper is a mechanism in the spirit of the DOA that converges to a Walrasian equilibrium in a polynomial number of steps, thus providing a theoretical basis for the above-described empirical phenomenon. It is well-known that computation of a Walrasian equilibrium for this market corresponds to solving a maximum weight bipartite matching problem. The uncoordinated but rational responses of agents thus solve in a distributed fashion a maximum weight bipartite matching problem that is encoded by their private valuations. We show, furthermore, that every Walrasian equilibrium is reachable by some sequence of responses. This is in contrast to the well known auction algorithms for this problem which only allow one side to make offers and thus essentially choose an equilibrium that maximizes the surplus for the side making offers. Our results extend to the setting where not every agent pair is allowed to trade with each other.

Query Complexity of Correlated Equilibrium

We study lower bounds on the query complexity of determining correlated equilibrium. In particular, we consider a query model in which an n-player game is specified via a black box that returns players' utilities at pure action profiles. In this model we establish that in order to compute a correlated equilibrium any deterministic algorithm must query the black box an exponential (in n) number of times.

Envy-Free Pricing in Large Markets: Approximating Revenue and Welfare

We study the classic setting of envy-free pricing, in which a single seller chooses prices for its many items, with the goal of maximizing revenue once the items are allocated. Despite the large body of work addressing such settings, most versions of this problem have resisted good approximation factors for maximizing revenue; this is true even for the classic unit-demand case. In this paper we study envy-free pricing with unit-demand buyers, but unlike previous work we focus on large markets: ones in which the demand of each buyer is infinitesimally small compared to the size of the overall market. We assume that the buyer valuations for the items they desire have a nice (although reasonable) structure, i.e., that the aggregate buyer demand has a monotone hazard rate and that the values of every buyer type come from the same support.

For such large markets, our main contribution is a 1.88 approximation algorithm for maximizing revenue, showing that good pricing schemes can be computed when the number of buyers is large. We also give a (e,2)-bicriteria algorithm that simultaneously approximates both maximum revenue and welfare, thus showing that it is possible to obtain both good revenue and welfare at the same time. We further generalize our results by relaxing some of our assumptions, and quantify the necessary tradeoffs between revenue and welfare in our setting. Our results are the first known approximations for large markets, and crucially rely on new lower bounds which we prove for the revenue-maximizing prices.

A Truthful Mechanism for the Generalized Assignment Problem

We propose a truthful-in-expectation, $(1-1/e)$-approximation mechanism for a strategic variant of the generalized assignment problem (GAP).
In GAP, a set of items has to be optimally assigned to a set of bins without exceeding the capacity of any singular bin.
In the strategic variant of the problem we study, values for assigning items to bins are the private information of bidders and the mechanism should provide bidders with incentives to truthfully report their values.
The approximation ratio of the mechanism is a significant improvement over the approximation ratio of the existing truthful mechanism for GAP.

The proposed mechanism comprises a novel convex optimization program as the allocation rule as well as an appropriate payment rule.
To implement the convex program in polynomial time, we propose a fractional local search algorithm which approximates the optimal solution within an arbitrarily small error leading to an approximately truthful-in-expectation mechanism.
The presented algorithm improves upon the existing optimization algorithms for GAP in terms of simplicity and runtime while the approximation ratio closely matches the best approximation ratio given for GAP when all inputs are publicly known.

Private Pareto Optimal Exchange

We consider the problem of implementing an individually rational,
asymptotically Pareto optimal allocation in a barter-exchange
economy where agents are endowed with goods and preferences over the goods of others, but may not use money as a medium of
exchange. Because one of the most important instantiations of such
economies is kidney exchange -- where the ``input'' to the problem
consists of sensitive patient medical records -- we ask to what
extent such exchanges can be carried out while providing formal
privacy guarantees to the participants. We show that individually
rational allocations cannot achieve any non-trivial approximation to
Pareto optimality if carried out under the constraint of
differential privacy -- or even the relaxation of
\emph{joint}-differential privacy, under which it is known that
asymptotically optimal allocations can be computed in two sided
markets [Hsu et al. STOC 2014]. We therefore consider a further
relaxation that we call \emph{marginal}-differential privacy --
which promises, informally, that the privacy of every agent $i$ is
protected from every other agent $j \neq i$ so long as $j$ does not
collude or share allocation information with other agents. We show
that under marginal differential privacy, it is possible to compute
an individually rational and asymptotically Pareto optimal
allocation in such exchange economies.

Dynamics at the Boundary of Game Theory and Distributed Computing

Impartial Selection and the Power of Up to Two Choices

We study mechanisms that select members of a set of agents based on nominations by other members and that are impartial in the sense that agents cannot influence their own chance of selection. Prior work has shown that deterministic mechanisms for selecting any fixed number k of agents are severely limited and cannot extract a constant fraction of the nominations of the k most highly nominated agents. We prove here that this impossibility result can be circumvented by allowing the mechanism to sometimes but not always select fewer than k agents.
This added flexibility also improves the performance of randomized mechanisms, for which we show a separation between mechanisms that make exactly two or up to two choices and give upper and lower bounds for mechanisms allowed more than two choices.

Online Allocation with Traffic Spikes: Mixing Adversarial and Stochastic Models

Motivated by Internet advertising applications, online allocation problems have been studied extensively in various adversarial and stochastic models. While the adversarial arrival models are too pessimistic, many of the stochastic (such as i.i.d or random-order) arrival models do not realistically capture uncertainty in predictions. A significant cause for such uncertainty is the presence of unpredictable traffic spikes, often due to breaking news or similar events. To address this issue, a simultaneous approximation framework has been proposed to develop algorithms that work well both in the adversarial and stochastic models; however, this framework does not enable algorithms that make good use of partially accurate forecasts when making online decisions. In this paper, we propose a robust online stochastic model that captures the nature of traffic spikes in online advertising. In our model, in addition to the stochastic input for which we have good forecasts, an unknown number of impressions arrive that are adversarially chosen.

We design algorithms that combine a stochastic algorithm with an online algorithm to adaptively react to inaccurate predictions. We provide provable bounds for our new algorithms in this framework. We accompany our positive results with a set of hardness results showing that that our algorithms are not far from optimal in this framework. As a byproduct of our results, we also present improved online algorithms for a slight variant of the simultaneous approximation framework.

Bounded-Distance Network Creation Games

A \emph{network creation game} simulates a decentralized and non-cooperative construction of a communication network. Informally, there are $n$ players
sitting on the network nodes, which attempt to establish a reciprocal communication by activating, incurring a certain cost,
any of their incident links. The goal of each player is to have all the other nodes as close as possible in the resulting network, while buying as few links as possible. According to this intuition, any model of the game must then appropriately address a balance between these two conflicting objectives. Motivated by the fact that a player might have a strong requirement about her centrality in the network, in this paper we introduce a new setting in which if a player maintains her (either \emph{maximum} or \emph{average}) distance to the other nodes within a given \emph{bound}, then her cost is simply equal to the \emph{number} of activated edges, otherwise her cost is unbounded.
We study the problem of understanding the structure of pure Nash equilibria of the resulting games, that we call \textsc{MaxBD} and
\textsc{SumBD}, respectively. For both games, we show that when distance bounds associated with
players are \emph{non-uniform}, then equilibria can be arbitrarily bad.
On the other hand, for \textsc{MaxBD}, we show that when nodes have a \emph{uniform} bound $D \geq 3$ on the maximum distance, then the \emph{Price of Anarchy} (PoA)
is lower and upper bounded by $2$ and $O\left(n^{\frac{1}{\lfloor\log_3
D \rfloor+1}}\right)$, respectively (i.e., the PoA is constant as soon as $D$ is $\Omega(n^{\epsilon})$, for some $\epsilon>0$), while for the interesting case $D=2$, we are able to prove that the PoA is $\Omega(\sqrt{n})$ and $O(\sqrt{n \log n} )$. For the uniform \textsc{SumBD} we obtain similar (asymptotically) results, and moreover we show that the PoA becomes constant as soon as the bound on the average distance is $2^{\omega\big({\sqrt{\log n}}\big)}$.

Near-Optimum Online Ad Allocation for Targeted Advertising

Motivated by Internet targeted advertising, we address several ad allocation problems. Prior work has established these problems admit no randomized online algorithm better than $(1-\frac{1}{e})$-competitive (see \cite{karp1990optimal,mehta2007adwords}), yet simple heuristics have been observed to perform much better in practice. We explain this phenomenon by studying a generalization of the bounded-degree inputs considered by \cite{buchbinder2007online}, graphs which we call $(k,d)-bounded$. In such graphs the maximal degree on the online side is at most $d$ and the minimal degree on the offline side is at least $k$. We prove that for such graphs, these problems' natural greedy algorithms attain competitive ratio $1-\frac{d-1}{k+d-1}$, tending to \emph{one} as $d/k$ tends to zero. We prove this bound is tight for these algorithms.

Next, we develop deterministic primal-dual algorithms for the above problems achieving competitive ratio $1-(1-\frac{1}{d})^k>1-\frac{1}{e^{k/d}}$, or \emph{exponentially} better loss as a function of $k/d$, and strictly better than $1-\frac{1}{e}$ whenever $k\geq d$. We complement our lower bounds with matching upper bounds for the vertex-weighted problem. Finally, we use our deterministic algorithms to prove by dual-fitting that simple randomized algorithms achieve the same bounds in expectation. Our algorithms and analysis differ from previous ad allocation algorithms, which largely scale bids based on the spent fraction of their bidder's budget, whereas we scale bids according to the number of times the bidder could have spent as much as her current bid. Our algorithms differ from previous online primal-dual algorithms, as they do not maintain dual feasibility, but only primal-to-dual ratio, and only attain dual feasibility upon termination. We believe our techniques could find applications to other well-behaved online packing problems.

Computation of Stackelberg Equilibria of Finite Sequential Games

The Stackelberg equilibrium is a solution concept that describes optimal strategies to commit to: Player 1 (the leader) first commits to a strategy that is publicly announced, then Player 2 (the follower) plays a best response to the leader's choice. We study the problem of computing Stackelberg equilibria in finite sequential (i.e., extensive-form) games and provide new exact algorithms, approximate algorithms, and hardness results for finding equilibria for several classes of such two-player games.

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Publication Years 2013-2017
Publication Count 104
Citation Count 161
Available for Download 104
Downloads (6 weeks) 668
Downloads (12 Months) 4767
Downloads (cumulative) 16938
Average downloads per article 163
Average citations per article 2
First Name Last Name Award
Vincent Conitzer ACM Doctoral Dissertation Award
Honorable Mention (2007) ACM Doctoral Dissertation Award
Honorable Mention (2007)
Joseph Halpern ACM AAAI Allen Newell Award (2008)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM Prize in Computing (2008)
Silvio Micali ACM A. M. Turing Award (2012)
Noam Nissan ACM Doctoral Dissertation Award
Series Winner (1990) ACM Doctoral Dissertation Award
Series Winner (1990)
David M Pennock ACM Senior Member (2006)
Tim Roughgarden ACM Grace Murray Hopper Award (2009)
ACM Doctoral Dissertation Award
Honorable Mention (2002)
Moshe Tennenholtz ACM AAAI Allen Newell Award (2012)
Salil P Vadhan ACM Doctoral Dissertation Award (2000)
Ingmar Weber ACM Senior Member (2017)

First Name Last Name Paper Counts
Tim Roughgarden 5
Randolph McAfee 5
Ian Kash 4
Avinatan Hassidim 3
Robert Kleinberg 3
Martin Hoefer 3
Ariel Procaccia 3
Moshe Babaioff 3
Paul Dütting 3
Monika Henzinger 3
Yonatan Aumann 3
Moshe Tennenholtz 3
David Parkes 3
Yishay Mansour 3
Dimitris Fotakis 2
Arpita Ghosh 2
Rahul Savani 2
David Sarne 2
Nicholas Jennings 2
Alexander Skopalik 2
Yiling Chen 2
Maria Balcan 2
Aaron Roth 2
George Christodoulou 2
Georgios Piliouras 2
Thomas Keßelheim 2
Michal Feldman 2
Balasubramanian Sivan 2
Martin Starnberger 2
Sergei Vassilvitskii 2
Anna Karlin 2
Asuman Ozdaglar 2
David Easley 2
Vahab Mirrokni 2
Paul Goldberg 2
Rann Smorodinsky 2
Tüomas Sandholm 2
Nisarg Shah 2
Martin Gairing 2
Christos, Papadimitriou 2
Yair Dombb 2
Nikhil Devanur 2
Nima Haghpanah 2
Abraham Othman 2
Shaddin Dughmi 2
Nicole Immorlica 2
Guido Schäfer 1
Tomasz Michalak 1
Agata Chrobak 1
Bo Tang 1
Gowtham Srinivasan 1
Kamesh Munagala 1
Amos Azaria 1
Mallesh Pai 1
Yaron Singer 1
Ruggiero Cavallo 1
Peter Troyan 1
Omer Tamuz 1
Arunava Sen 1
Konstantinos Kollias 1
Siddharth Barman 1
Dan Tsafrir 1
Arpita Ghosh 1
Haim Kaplan 1
Carola Doerr 1
Majid Khonji 1
Ioannis Giotis 1
Scott Kominers 1
Ioannis Caragiannis 1
Talal Rahwan 1
Nitish Korula 1
Éva Tardos 1
Jason Hartline 1
Moran Feldman 1
Felix Brandt 1
Eric Friedman 1
Joseph Halpern 1
Guido Proietti 1
Ingmar Weber 1
Jiehua Chen 1
Laurens Cherchye 1
Gleb Polevoy 1
Stanko Dimitrov 1
Mihaela Schaar 1
Muli Ben-Yehuda 1
Daniel Reeves 1
Shaili Jain 1
Yiling Chen 1
Iftah Gamzu 1
Jugal Garg 1
László Végh 1
Benjamin Doerr 1
Deepayan Chakrabarti 1
Victor Shnayder 1
Weidong Ma 1
Pranav Dandekar 1
Matthew Cary 1
Kurtis Heimerl 1
Poan Chen 1
Pablo Azar 1
Simina Brânzei 1
Stephen Chong 1
Jon Kleinberg 1
Pablo Parrilo 1
Aleksandrs Slivkins 1
Amin Saberi 1
Susanne Albers 1
Frits Spieksma 1
Alexander Peysakhovich 1
Noam Livne 1
Mike Ruberry 1
Sam Ganzfried 1
Noam Nisan 1
Xujin Chen 1
Allan Borodin 1
Saeed Alaei 1
David Kempe 1
Jinwoo Shin 1
Sara Krehbiel 1
Kshipra Bhawalkar 1
Tal Moran 1
Annamária Kovács 1
Alkmini Sgouritsa 1
George Pierrakos 1
Jacob Abernethy 1
Hau Chan 1
Ioannis Caragiannis 1
Pichayut Jirapinyo 1
Riccardo Colini-Baldeschi 1
Noga Alon 1
Eyal Even-Dar 1
Liam Roditty 1
Emmanouil Zampetakis 1
Rahul Sami 1
Morteza Zadimoghaddam 1
Avrim Blum 1
Renato PaesLeme 1
Jeff Shamma 1
Daniel Goldstein 1
Davide Bilò 1
Luciano Gualà 1
Rolf Niedermeier 1
Daron Acemoğlu 1
Marina Epelman 1
Orna Agmon Ben-Yehuda 1
Drew Fudenberg 1
Assaf Schuster 1
Yuval Emek 1
Siddharth Suri 1
Evdokia Nikolova 1
Brendan Lucier 1
Azarakhsh Malekian 1
Nadia Fawaz 1
Aparna Das 1
Silvio Micali 1
Bem Roberts 1
Peter Key 1
Zhiyi Huang 1
Qiqi Yan 1
Berthold Vöcking 1
Nick Gravin 1
Liad Blumrosen 1
Atsushi Iwasaki 1
Makoto Yokoo 1
Stefano Leonardi 1
Yu Zhang 1
Aviad Rubinstein 1
Hadi Minooei 1
MohammadHossein Bateni 1
Maria Polukarov 1
Alon Rosen 1
Moshe Tennenholtz 1
Rakefet Rozen 1
Tobias Harks 1
Rob Van Stee 1
Ofir Geri 1
Chikin Chau 1
Khaled Elbassioni 1
Aravind Srinivasan 1
Stratis Ioannidis 1
Dinan Gunawardena 1
Salil Vadhan 1
Bach Ha 1
Ozan Candogan 1
Jennifer Vaughan 1
Felix Fischer 1
Ilya Segal 1
Suguru Ueda 1
Robert Bredereck 1
Ercan Yildiz 1
Yishay Mansour 1
Yakov Babichenko 1
David Pennock 1
Vincent Conitzer 1
Erik Vee 1
Michael Schwarz 1
Bart Keijzer 1
Christos Tzamos 1
Michael Wooldridge 1
Lawrence Blume 1
Yuval Peres 1
Okke Schrijvers 1
Markus Brill 1
Jing Chen 1
Mohammad Mahdian 1
Daniel Fragiadakis 1
Stefan Kratsch 1
Elchanan Mossel 1
Gerhard Woeginger 1
Anna Scaglione 1
Jaeok Park 1
Mihaela Van Der Schaar 1
Stefan Eilts 1
Bart Smeulders 1
Bram De Rock 1
Shai Vardi 1
Swaprava Nath 1
Chaitanya Swamy 1
John Fearnley 1
Victor Naroditskiy 1
Yuanzhang Xiao 1
Ronen Gradwohl 1
Mohammad Mahdian 1
Patrick Hummel 1
Philipp Von Falkenhausen 1
Xiaodong Hu 1
Benjamin Edelman 1
Christopher Wilkens 1
Claire Mathieu 1
Piotr Szczepański 1
Inbal Talgam-Cohen 1
Cam Nguyen 1
Elias Koutsoupias 1
Angelo Fanelli 1
John Lai 1
Benjamin Lubin 1
David Pennock 1
Jonathan Ullman 1

Affiliation Paper Counts
Universite Libre de Bruxelles 1
Ecole Normale Superieure 1
University of Illinois at Urbana-Champaign 1
Humboldt University of Berlin 1
University of Michigan 1
Warsaw University of Technology 1
Yale University 1
Universite Paris 7- Denis Diderot 1
The University of Hong Kong 1
Universitat Politecnica de Catalunya 1
Hebrew University of Jerusalem 1
CNRS Centre National de la Recherche Scientifique 1
Vrije Universiteit Amsterdam 1
University of Sassari 1
Le Moyne College 1
Center for Mathematics and Computer Science - Amsterdam 1
National Chiao Tung University Taiwan 1
The Interdisciplinary Center Herzliya 1
University of California, Davis 1
University of Augsburg 1
RWTH Aachen University 1
University of Toronto 1
Yonsei University 1
University of L'Aquila 1
Harvard Business School 1
Boston University 1
Technical University of Munich 1
Texas A and M University 1
University of Virginia 1
Trinity University 1
University of Freiburg 1
Swiss Federal Institute of Technology, Zurich 1
University of Roma Tor Vergata 1
Swiss Federal Institute of Technology, Lausanne 1
University of Athens 1
University of Aarhus 1
Columbia University 1
TECH Lab 1
University of Cambridge 1
Korea Advanced Institute of Science & Technology 1
University of Wisconsin Madison 1
University of Electro-Communications 1
Eindhoven University of Technology 1
Yahoo Inc. 1
King Abdullah University of Science and Technology 1
Singapore University of Technology and Design 1
Qatar Computing Research institute 1
Indian Statistical Institute (Delhi Centre) 2
California Institute of Technology 2
Weizmann Institute of Science Israel 2
Microsoft Research Cambridge 2
University of Roma La Sapienza 2
University of Warsaw 2
Stony Brook University 2
University of Patras 2
National Technical University of Athens 2
Duke University 2
University of Paderborn 2
Kyushu University 2
Yahoo Research Labs 2
University of Texas at Austin 2
Chinese Academy of Sciences 3
University of Waterloo 3
Microsoft Corporation 3
University of Oxford 3
University of Southern California 3
London School of Economics and Political Science 3
Catholic University of Leuven, Leuven 3
University of Maryland 3
Georgia Institute of Technology 4
University of Washington, Seattle 4
Masdar Institute of Science and Technology 4
University of Southampton 4
Technical University of Berlin 5
University of Pennsylvania 5
Northwestern University 5
University of Vienna 5
University of California, Los Angeles 5
Max Planck Institute for Informatics 7
Technion - Israel Institute of Technology 8
University of California, Berkeley 8
Google Inc. 9
Tel Aviv University 9
Carnegie Mellon University 10
University of Liverpool 10
Massachusetts Institute of Technology 10
Bar-Ilan University 11
Stanford University 12
Cornell University 12
Harvard University 16
Microsoft Research 25
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