Differential Privacy Workshop in London

There will be a differential privacy workshop in London in April, accepting submissions soon. If you have something you are working on, consider submitting here. Some highlights:

-- Although the workshop will not have proceedings (so you can as usual submit a talk and still submit it to a conference of your choosing), exceptional submissions will be invited to a special issue of the Journal of Privacy and Confidentiality.

-- The PC is pretty inter-disciplinary. This is certainly not just a Theory-A workshop. Work on privacy in all areas is on topic.

-- You get to hear Jon Ullman give a (presumably excellent) talk. 

CALL FOR PAPERS
TPDP 2015
First workshop on the Theory and Practice of Differential Privacy
18th April 2015, London, UK
Affiliated to ETAPS
http://tpdp.computing.dundee.ac.uk

Differential privacy is a promising approach to the privacy-preserving
release of data: it offers a strong guaranteed bound on the increase
in harm that a user incurs as a result of participating in a
differentially private data analysis.

Researchers in differential privacy come from several area of computer
science as algorithms, programming languages, security, databases,
machine learning, as well as from several areas of statistics and data
analysis. The workshop is intended to be an occasion for researchers
from these different research areas to discuss the recent developments
in the theory and practice of differential privacy.

**Submissions**

The overall goal of TPDP is to stimulate the discussion on the
relevance of differentially private data analyses in practice. For
this reason, we seek contributions from different research areas of
computer science and statistics.

Authors are invited to submit a short abstract (4-5 pages maximum) of
their work by January 23, 2015. Abstracts must be written in English
and be submitted as a single PDF file at the EasyChair page for TPDP:
https://easychair.org/conferences/?conf=3Dtpdp2015

Submissions will be judged on originality, relevance, interest and
clarity. Submission should describe novel works or works that have
already appeared elsewhere but that can stimulate the discussion
between the different communities. Accepted abstracts will
be presented at the workshop.

The workshop will not have formal proceedings, but we plan to have a
special issue of the Journal of Privacy and Confidentiality devoted to
TPDP. Authors presenting valuable contributions at the workshop will
be invited to submit a journal version of their work right after the
workshop.


**Important Dates**

-January 23, 2015 - Abstract Submission
-February 10, 2015 - Notification
-February 14, 2015 - Deadline early registration ETAPS
-April 18, 2015 - Workshop

-May 15, 2015 Deadline for journal special issue


**Topics**

Specific topics of interest for the workshop include (but are not limited t=
o):
theory of differential privacy,
verification techniques for differential privacy,
programming languages for differential privacy,
models for differential privacy,
trade-offs between privacy protection and analytic utility,
differential privacy and surveys,
relaxations of the differential privacy definition,
differential privacy vs other privacy notions and methods,
differential privacy and accuracy,
practical differential privacy,
implementations for differential privacy,
differential privacy and security,
applications of differential privacy.


**Invited Speakers**

Jonathan Ullman - Simons Fellow at Columbia University,

Another invited speaker joint with HotSpot'15 to be confirmed.


**Program Committee**

Gilles Barthe - IMDEA Software
Konstantinos Chatzikokolakis - CNRS and LIX, Ecole Polytechnique
Kamalika Chaudhuri - UC San Diego
Graham Cormode - University of Warwick
George Danezis - University College London
Marco Gaboardi - University of Dundee
Matteo Maffei - CISPA, Saarland University
Catuscia Palamidessi - INRIA and LIX, Ecole Polytechnique
Benjamin C. Pierce - University of Pennsylvania
Aaron Roth - University of Pennsylvania
David Sands - Chalmers University of Technology
Chris Skinner - London School of Economics
Adam Smith - Pennsylvania State University
Carmela Troncoso - Gradiant
Salil Vadhan - Harvard University

Differential Privacy Book is Here

After a much longer time than either of us thought it would take, my book with Cynthia Dwork, "The Algorithmic Foundations of Differential Privacy" is finally available.

You have 3 options for obtaining a copy! (I must admit to not quite understanding the pricing model of our publisher).

1. Hard Copy: You can buy a hard copy directly from NOW for $99 (http://www.nowpublishers.com/articles/foundations-and-trends-in-theoretical-computer-science/TCS-042/book-details) or from Amazon for $101 and free shipping (http://www.amazon.com/Algorithmic-Foundations-Differential-Privacy/dp/1601988184/ )
2. "ebook" format (which I believe is just a downloadable pdf): If you don't have room on your book shelf, and are happy with a PDF, the book can be yours from NOW for only $240. This is a bargain -- coming in at 281 pages, this is less than 86 cents per digital "page".   (http://www.nowpublishers.com/articles/foundations-and-trends-in-theoretical-computer-science/TCS-042/book-details)
3. Free download: The PDF is also available for free on my web page: http://www.cis.upenn.edu/~aaroth/privacybook.html

Lecture 12 -- Privacy Yields an Anti-Folk Theorem in Repeated Games

Last week, Kobbi Nissim gave us an excellent guest lecture on differential privacy and machine learning. The semester has gone by fast -- this week is our last lecture in the privacy and mechanism design class. (But stop by next week to hear the students present their research projects!)

Today we'll talk about infinitely repeated games. In an infinitely repeated game, n players repeatedly, in an infinite number of stages, play actions and obtain payoffs based on some commonly known stage game. Since the game is infinitely repeated, in order to make sense of players total payoff, we employ a discount factor delta that specifies how much less valuable a dollar is tomorrow compared to a dollar today. (delta is some number in [0, 1) ). In games of perfect monitoring, players perfectly observe what actions each of their opponents have played in past rounds, but in large n player games, it is much more natural to think about games of imperfect monitoring, in which agents see only some noisy signal of what their opponents have played.

For example, one natural signal players might observe in an anonymous game is a noisy histogram estimating what fraction of the population has played each type of action. (This is the kind of signal you might get if you see a random subsample of what people play -- for example, you have an estimate of how many people drove on each road on the way to work today by looking at traffic reports). Alternately, there may be some low dimensional signal (like the market price of some good) that everyone observes that is computed as a randomized function of everyone's actions today (e.g. how much of the good each person produced).

A common theme in repeated games of all sorts are folk theorems. Informally, these theorems state that in repeated games, we should expect a huge multiplicity of equilibria, well beyond the equilibria we would see in the corresponding one-shot stage game. This is because players observe each other's past behavior, and so can threaten each other to behave in prescribed ways or else face punishment. Whether or not a folk theorem is a positive result or a negative result depends on whether you want to design behavior, or predict behavior. If you are a mechanism designer, a folk theorem might be good news -- you can try and encourage equilibrium behavior that has higher welfare than any equilibrium of the stage game. However, if you want to predict behavior, it is bad news -- there are now generically a huge multiplicity of very different equilibria, and some of them have much worse welfare than any equilibrium of the stage game.

In this lecture (following a paper joint with Mallesh Pai and Jon Ullman) we argue that:

1. In large games, many natural signaling structures produce signal distributions that are differentially private in the actions of the players, where the privacy parameters tends to 0 as the size of the game gets large, and
2. In any such game, for any discount factor delta, as the size of the game gets large, the set of equilibria of the repeated game collapse to the set of equilibria of the stage game. In other words, there are no "folk theorem equilibria" -- only the equilibria that already existed in the one shot game. 

This could be interpreted in a couple of ways. On the one hand, this means that in large games, it might be harder to sustain cooperation (which is a negative result). On the other hand, since it shrinks the set of equilibria, it means that adding noise to the signaling structure in a large game generically improves the price of anarchy over equilibria of the repeated game, which is a positive result. 

Lecture 10 -- Running Ascending Price Auctions that Make Sincere Bidding an Ex-Post Dominant Strategy

In the 10th lecture in our privacy and mechanism design class, we consider the problem of running an ascending price auction. An ascending price auction is just a generalization of what you normally see as an "auction" on TV -- rather than submitting your valuation in some kind of one-shot protocol, the prices of the goods gradually rise, and you take turns with other bidders making bids on the goods as a function of the current prices.

Why would you want to run such an auction when the VCG mechanism already can provide welfare optimal outcomes for every social choice function, while making truthful reporting a dominant strategy? People quote a couple of reasons:

1. It might be hard to actually report your full valuation: in principle, you need to figure out exactly your value for every bundle you might receive, and its difficult to pin down a number. In an ascending price auction, all you need to do is be able to point to your favorite good (or bundle of goods) that you would buy if the current prices were the final prices, which is often an easier task. 
2. An ascending price auction can end without you having to reveal your full type. For example, in a single item second price auction, the highest bidder never has to reveal (even to the auctioneer) his value for the good -- only that it is higher than that of the second highest bidder. Hence, people might prefer such auctions for "privacy" reasons. 

In an ascending price auction, "truthful" reporting doesn't make sense, since nobody ever asks you to report your type. But we can ask for "sincere bidding", in which bidders truthfully bid on the item at each round that is their favorite, given the current prices. But there is a problem: we typically can't implement sincere bidding as a dominant strategy, because of the problem of threats. Consider the following simple example:

Suppose we have two unit demand bidders 1 and 2, and two goods for sale a and b. We have v_{1,a} = 1, v_{1,b} = epsilon and v_{2,a} = 1/2, v_{2, b} = 1/2 - \epsilon. Suppose moreover that bidder 2 takes the following strategy: "Bid on good a. If bidder 1 bids on good a, then outbid him on whatever he bids on until the price is > 1.'' Against this strategy, bidder 1 cannot obtain non-negative utility if he bids on his favorite good (a), and so his best response is to place an insincere bid on good 2. Moreover, bidder 2 has a clear motivation to take this threatening position -- he obtains substantially higher payoff than if players followed sincere bidding, since he gets his most preferred good without any competition. As a result of instances like these, typically ascending price auctions can implement sincere bidding at best as an (ex-post) Nash equilibirum. 

In this lecture, we talk about how to implement an ascending auction such that the prices are differentially private in the bidding strategies of the players (and the allocation in the end is jointly differentially private). This fixes two of the problems above:

1. The privacy guaranteed by the ascending price auction is no longer hand-wavy and qualitative, but rather precise and quantitative. 
2. We get sincere bidding as an asymptotic ex-post dominant strategy for all players.

To get this result, we need only a mild large-market assumption: that the "supply" of each good is modestly large compared to the number of different types of goods -- but crucially we need to assume nothing about how bidder preferences are generated. 

The intuition, which we will appeal to again later, is that by running the auction privately, we have eliminated the possibility that players can distort incentives by threatening each other.

Lecture 9 -- Purchasing Private Data from Privacy Sensitive Individuals

Yesterday in our privacy and mechanism design course, we were fortunate to have a guest lecture by David Xiao. David told us his exciting recent paper, with Kobbi Nissim and Salil Vadhan, Redrawing the Boundaries on Purchasing Private Data from Privacy-Sensitive Individuals.

Consider the following scenario: An analyst wishes to conduct some medical study about an underlying population, but needs to obtain permission from each individual whose data he uses. On the one hand, he needs to buy data from a representative sample of the population so that his study is accurate. On the other hand, he needs to compensate individuals for their privacy costs, and would like to come up with a payment scheme that incentivizes them to report their true privacy costs, rather than inflating them for selfish gain. Finally, he wants the mechanism to be individually rational: that no rational agent should obtain negative utility by interacting with the analyst.

Because individual's costs for privacy are a function of the method by which their reports are used to compute the outcome of the mechanism, rather than just a function of the outcome itself, this takes us outside of a standard mechanism design setting. What makes the problem tricky is that individual's costs for privacy could quite plausibly be correlated with their private data. Suppose the analyst wishes to estimate the fraction of people in some population who have syphilis. It is reasonable to expect that syphilitics will on the whole want to be compensated more than healthy individuals for a loss of privacy. But this means that even computations on agents reported costs for privacy (and independent of agent's supposedly private data) can lead to privacy loss for those agents, and so must be compensated.

Some years ago Arpita Ghosh and I studied this problem, and showed an impossibility result when making some (unreasonably) strong assumptions. One might have hoped that our result could be circumvented with one of several tweaks to the model. But no. David and his coauthors extend this impossibility result to have much wider applicability, making fewer assumptions on what the analyst is able to observe, and far fewer assumptions about the form of the privacy loss function of the agents. Their result is quite robust: Under extremely general circumstances, no truthful individually rational mechanism which makes finite payments can distinguish between two populations, in one of which everyone has syphilis, and in the other of which nobody does. This result says that no mechanism can simultaneously enjoy truthfulness, individual rationality, and non-trivial accuracy properties, and so without drastically relaxing the model of how people might value privacy, you must always give up on one of these.

They do propose one such relaxation, which seems to reduce to something like the assumption that contracting syphilis can only ever cause your costs for privacy to increase, never to decrease. But this is probably not the last word. I think that convincing answers for what to do in the face of their impressive impossibility result are still to be proposed, and is a really interesting question.

Lecture 8 -- Implementing Correlated Equilibria Ex-Post in Incomplete Information Games

After a spring break hiatus, our class on privacy and mechanism design returns (with all of our students working on their course projects!)

In our third and final lecture on using mediators to implement equilibrium of complete information games in settings of incomplete information, we ask how far we can push the agenda of obtaining ex-post implementations via the technique of differentially private equilibrium computation. Recall that last lecture we saw how to do this in large congestion games. Can we get similar results in arbitrary large games?

One obstacle is that we do not know good algorithms for computing Nash equilibria in arbitrary games at all, let alone privately. However, we do know how to compute arbitrarily good approximations to correlated equilibria in arbitrary n player games! In this lecture, we explore the game theoretic implications of private computation of correlated equilibria, and then show how to do it.

The punchline is you can still implement (correlated) equilibria of the complete information game as an ex-post Nash equilibrium of the incomplete information game, but you need a slightly stronger mediator (which has the power to verify player types if they decide to use the mediator).

To accomplish this goal, we introduce a couple of interesting tools: A powerful composition theorem in differential privacy due to Dwork, Rothblum, and Vadhan, and the multiplicative weights (or weighted majority, or polynomial weights, or hedge, or...) algorithm, which is a natural learning dynamic and can be used to quickly compute (coarse) correlated equilibria in arbitrary games.

Next week we will have a guest lecture, combined with our theory seminar, by David Xiao, who will tell us about his exciting recent work on Redrawing the Boundaries on Purchasing Data from Privacy Sensitive Individuals. 

Lecture 7 -- Privacy Preserving Public Information for Sequential Games

Our seventh lecture was given by Jamie Morgenstern, about her very interesting paper joint with Avrim Blum, Ankit Sharma, and Adam Smith.

The birds-eye view is the following:

Suppose players (think financial institutions) take turns sequentially deciding which investments to make, from amongst a feasible set, which can be different for each player. In general, the profit that a player gets from an investment is a decreasing function of how many players previously have made the same investment. (Perhaps these investments are structured like pyramid schemes, where there is a big advantage in getting in early, or perhaps the market is somehow destabilized if there is too much capital invested in it).

We can study this interaction in various information models. In the complete information setting, each player sees exactly how much has been invested in each resource at the time that she arrives, and the unique dominant strategy solution of the game is for each player to invest greedily. They show that this solution achieves a good constant factor approximation to the optimal welfare.

But if the players are financial institutions, then their investments might represent sensitive trade secrets, and they may not want to share this information with others -- in which case the complete information setting seems unrealistic. This could be very bad news however -- if players have no information at all about what has gone on before their arrival, its not hard to cook up plausible sounding behaviors for them which result in disastrous welfare outcomes.

So the paper asks: can we introduce a differentially private signal (so one that necessarily reveals little actionable information about each agent, and therefore one whose introduction the agents have little reason to object to) that nevertheless allows the market to achieve social welfare that approximates OPT.

Skipping over some details, this paper shows that the answer is yes. Making public a differentially private count of how much has been invested in each resource as the game plays out is enough to guarantee that sequential play (studied either as the simple behavioral strategy in which players imagine that the noisy counts are exactly correct, or any solution in which players play only undominated strategies) results in an outcome that has a bounded competitive ratio with OPT.

This paper also contains an interesting technique that will probably be useful in other contexts: they develop a new method of privately maintaining the count of a set of numbers that achieves better additive error as compared to previous work, at the cost of introducing some small multiplicative error. In the application they need counters for in this paper, this modification gives improved overall bounds.

Lecture 6 -- Implementing Nash equilibria ex-post in incomplete information games (Part 2)

In Lecture 6 of our Privacy and Mechanism Design course, we continue our study of the use of differential privacy to implement equilibrium of complete information games -ex post- in settings of incomplete information augmented with an extremely weak "mediator".

Last class, we proved that to accomplish this task, it would be sufficient to design an algorithm that:

1. Satisfied (joint) differential privacy, and...
2. Computed an (asymptotic) Nash equilibrium of the complete information game induced by reported types. 

We also took a brief detour into private algorithmic techniques, and learned how to privately maintain a count on a stream of numbers. 

This class, we partially implement this agenda. We define large games and congestion games, and give an algorithm for privately computing Nash equilibrium in large congestion games, which uses our newly learned ability to count. We conclude that in such games, with the help of a weak mediator, we can enjoy the nice properties of the pure strategy Nash equilibria of these games in the complete information setting, even in incomplete information settings when player types can be arbitrary (i.e. not necessarily drawn from any prior distribution). 

There remains one more lecture in this series -- we will show that if we strengthen the power of our weak mediators somewhat, by giving them the ability to verify reported player types, then we can extend these results to arbitrary large games (i.e. not necessarily congestion games) by privately computing correlated equilibria. 

This last part will have to wait a couple of weeks though. Next week, we will have Jamie Morgenstern giving us a guest lecture on her very interesting recent work joint with Avrim Blum, Ankit Sharma, and Adam Smith. 

Lecture 4 -- The Empirical Implications of Privacy Aware Choice

Just barely out of order, the notes for Lecture 4 of our privacy and mechanism design class are now online.

In this lecture, Rachel Cummings told us about her very interesting work with Federico Echenique and Adam Wierman, studying what implications preferences for privacy might have in the revealed preferences setting.

The idea, in a nutshell, is this: suppose some entity is observing all of your purchase decisions online, and trying to deduce from them what your utility function over goods is. For example, if it observes you purchasing beer when you could have had milk for the same price, it will deduce that you strictly prefer beer to milk.

Now suppose that you understand that you are being observed, and you have preferences not only over the goods that you buy, but also over the set of deductions that the observer is going to make about you. Given a set of prices and a budget, you will choose which bundle of goods to buy to optimize this tradeoff between your utility for the bundle, and your cost for what is learned about you by the observer. As always, however, the observer only gets to see what you buy.

Can the observer tell whether or not you care about privacy? Can he deduce what your preferences over goods are? Can he prove that you are not buying goods totally at random?

One of the main results discussed in this lecture, is that for sufficiently general forms of the preferences that agents may hold, the answer to all of these questions is "no". Specifically, all sets of observations are rationalizable by some privacy-aware utility function.

Lecture 5 -- Implementing Nash equilibria ex-post in incomplete information games (Part 1)

Welcome back (after a brief hiatus) to the privacy and mechanism design course. Last week we had a guest lecture by Rachel Cummings about her work on the Empirical Implications of Privacy Aware Choice.  We will have notes and a blog post about that soon, but for now, welcome to Lecture 5!

In this lecture, we consider the following problem. Suppose we have an n player game. We might want to implement a Nash equilibrium of the complete information game (which often have nice properties), but naturally find ourselves in the common situation when the n players do not know each others types. Is there a modification of the game that can implement the complete-information game equilibrium as a simple ex-post Nash equilibrium, which does not require that players know anything about each others types, and which requires no coordination?

The modification of the game we consider is the introduction of a very weak "mediator", which essentially only has the power to listen and to give advice, but not to verify or to enforce. Players will have the option of reporting their type to the mediator, in which case it will suggest an action for them to play. However, they can:

1. Completely ignore the mediator and choose an action to play on their own
2. Lie to the mediator about their types, and/or
3. Use the mediator's suggestion however they like, not necessarily following it (i.e. the mediator can not enforce its suggestion)

What we would like is that players should all follow "good behavior", which involves

1. Truthfully reporting their type to the mediator, and then
2. Faithfully following the mediator's suggestion.

We prove that if the mediator offers its suggestions by computing a Nash equilibrium of the complete information game defined by players reported types, and satisfies (a variant of) differential privacy, then the mediator makes "good behavior" an asymptotic ex-post Nash equilibrium of the incomplete information game, and that in this ex-post equilibrium, players end up playing a Nash equilibrium of the original game of complete information (defined by the realized player types).

This leaves us with the minor task of actually figuring out how to compute a Nash equilibrium privately. Here, we realize that thus far in the class, we have been skating by knowing nothing about private algorithm design except for the exponential mechanism. So we take a detour to learn some more basics. We get as far as learning how to count, which turns out to be enough. This will set us up for next lecture, when we can actually implement this agenda and design a mediator that satisfies the hypotheses we need.

(The idea of designing a mediator by privately computing an equilibrium comes from joint work with Michael Kearns, Mallesh Pai, and Jon Ullman, which shows how to privately compute correlated equilibria in arbitrary large games. We will cover this later. The implication in this lecture about mediators that privately compute Nash equilibria comes from joint work with Ryan Rogers, which shows how to privately compute Nash equilibria in large congestion games. )

Lecture 3 -- Mechanism Design for arbitrary objective functions, and without money!

In the third lecture of our course on privacy and mechanism design, we go back to using differential privacy as a tool in mechanism design, and cover a remarkable result of Nissim, Smorodinsky, and Tennenholtz.

Traditionally in mechanism design, social welfare holds a special place in the pantheon of objective functions: this is because in any social choice setting, we can use the VCG mechanism to exactly optimize for social welfare while guaranteeing dominant strategy truthfulness. In general, it is not possible to truthfully optimize most other objective functions -- and even for social welfare, the VCG mechanism requires the ability to charge payments. It is the rare setting when we can achieve some objective truthfully without payments, and even rarer when that objective is not social welfare.

In todays lecture, we give a general mechanism that in any social choice setting (satisfying certain technical conditions) approximately optimizes any low-sensitivity objective function (social welfare satisfies this condition, but so do many others). Moreover, this mechanism is strictly dominant strategy truthful, and crucially does not require the use of payments. Of course, we can't get around classical impossibility results for free -- the tradeoff, as always, is that we are only optimizing our objective approximately up to some additive loss. However, in large economies, this loss will often become negligible as the size of the population grows.

After class, Rachel Cummings will give our theory seminar on her own work in privacy and mechanism design: The Empirical Implications of Privacy Aware Choice, which is joint work with Federico Echenique and Adam Wierman, both from Caltech. Be there or be square!

Lecture 2: Privacy as a Desideratum in Mechanism Design -- Making the Exponential Mechanism Truthful

For those of you following the privacy and mechanism design course at home, tomorrow's Lecture 2 is now online.

This lecture explores a different aspect of privacy and mechanism design. Last week, we considered the use of differential privacy purely as a tool, to design a prior free asymptotically truthful and revenue optimal mechanism for digital goods auctions. In this lecture, we motivate differential privacy as a desideratum for its own sake, and argue that it gives mechanisms (often at very little cost) a natural notion of robustness against the possibility of extra unmodeled parts of agents utility functions.

Finally, we derive a private version of the VCG mechanism, that for general social choice problems is simultaneously:

1. (Exactly) dominant strategy truthful
2. Differentially private
3. (Approximately) welfare optimal

This result is from Huang and Kannan 2012: "The Exponential Mechanism for Social Welfare: Private, Truthful, and Nearly Optimal". Although this is a recent paper, it is a basic, important, foundational result that "should" have been discovered earlier. It fits in very nicely at the beginning of the semester.

(And for those of you actually taking this class and looking for inspiration to do a course project, this paper started as Zhiyi Huang's course project in the 2011 privacy course I taught here at Penn)

Privacy + Mechanism Design course begins today

Today several students will endure the first (3 hour!) lecture in an experimental course I am teaching on privacy and mechanism design. Its a very new area -- whether or not there is enough material to cover a whole semester may depend on how many new theorems are proven during the semester that the course is running. (I already know of the existence of several papers I will want to teach, but haven't read yet since they aren't yet available!)

A secondary goal that I have is to produce a nice set of lecture notes, that will be accessible to people curious about getting into the field. I encourage people to read along, starting with today's Lecture 1. We're starting from basics -- I don't want to assume that readers know anything about either privacy, or mechanism design. In the first lecture, we introduce differential privacy from the perspective of a game theorist, and use it to solve an interesting problem: prior-free revenue maximization in a digital goods auction. Along the way we learn about a useful tool, "The exponential mechanism", that we will encounter again.

The digital goods auction setting is not only a nice introduction to many of the ideas underlying the privacy/mechanism design connection, it is also historically faithful: all of the results in Lecture 1 are from the seminal paper of McSherry and Talwar which introduced the connection between differential privacy and game theory: Mechanism Design via Differential Privacy.

COLT call for papers

Nina Balcan asked me to post the COLT 2014 call for papers. A couple of interesting things in the call: privacy is specifically mentioned as a constraint of interest on learning, and there is a call for papers on "Learning in other settings: e.g. social, economic, and game-theoretic".

The 27th Annual Conference on Learning Theory (COLT 2014) will take
place in Barcelona, Spain, on June 13-15, 2014

We invite submissions of papers addressing theoretical aspects of
machine learning and related topics. Submissions by authors who are
new to COLT are encouraged. We strongly support a broad definition of
learning theory, including, but not limited to:

• Design and analysis of learning algorithms and their generalization ability
• Computational complexity of learning
• Optimization procedures for learning
• Unsupervised, semi-supervised learning, and clustering
• Online learning
• Interactive learning
• Kernel Methods
• High dimensional and non-parametric empirical inference, including
sparsity methods
• Planning and control, including reinforcement learning
• Learning with additional constraints: E.g. privacy, time or memory
budget, communication
• Learning in other settings: E.g. social, economic, and game-theoretic
• Analysis of learning in related fields: natural language processing,
neuroscience, bioinformatics, privacy and security, machine vision,
data mining, information retrieval.

We are also interested in papers that include viewpoints that are new
to the COLT community. We welcome experimental and algorithmic papers
provided they are relevant to the focus of the conference by
elucidating theoretical results. Also, while the primary focus of the
conference is theoretical, papers can be strengthened by the inclusion
of relevant experimental results.

COLT will award both best paper and best student paper awards. Best
student papers must be authored or coauthored by a student. Authors
must indicate (using a footnote on the first page of the paper) at
submission time if they wish their paper to be eligible for a student
award. This does not preclude the paper to be eligible for the best
paper award. The program committee may decline to make these awards,
or may split them among several papers.

Papers that have previously appeared in journals or at other
conferences, or that are being submitted to other conferences, are not
appropriate for COLT. Papers that include work that has already been
submitted for journal publication may be submitted to COLT, as long as
the papers have not been accepted for publication by the COLT
submission deadline (conditionally or otherwise) and that the paper is
not expected to be published before the COLT conference (June 2014).

Accepted papers will be published electronically, in the JMLR Workshop
and Conference Proceedings series.

Rebuttal Phase
As in the previous years, we will have a rebuttal phase during the
review process. Initial reviews will be sent to authors before final
decisions have been made. Authors will have the opportunity to provide
a short response on the PC's initial evaluation. Final
acceptance/rejection decision will be made a week later.

Open Problems Session:
We also invite submission of open problems. A separate call for open
problems will be available at the conference website:
http://orfe.princeton.edu/conferences/colt2014/

Submission Instructions:
Submissions are limited to 12 JMLR-formatted pages, plus additional
pages for references and appendices. All details, proofs and
derivations required to substantiate the results must be included in
the submission, possibly in the appendices. However, the contribution,
novelty and significance of submissions will be judged primarily based
on the main text (without appendices), and so enough details,
including proof details, must be provided in the main text to convince
the reviewers of the submissions' merits.

Important Dates:
• Paper submission deadline: February 7th, 2014, 11:00 PM EST
• Author response period: April 5-10, 2014
• Author notification: April 19, 2014
• Conference: June 13-15, 2014

On behalf of the COLT 2014 organizers,

Program Chairs
• Maria-Florina Balcan (Georgia Institute of Technology)
• Csaba Szepesvári (University of Alberta)

Local Arrangements Chairs
•  Sébastien Bubeck (Princeton University)
•  Gábor Lugosi (ICREA and Pompeu Fabra University)

Publication Chair
• Vitaly Feldman (IBM Almaden Research Center)