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Program Committee


APPROX

Anna Adamaszek (Copenhagen)
Shiri Chechik (Tel Aviv)
Anne Driemel (TUE)
Lee-Ad Gottlieb (Ariel)
Varun Kanade (Oxford)
Nitish Korula (Google)
Stefano Leonardi (Sapienza)
Daniel Lokshtanov (Bergen)
Claire Mathieu (ENS Paris, chair)
Nicole Megow (TUM)
Tobias Moemke (Saarland)
Shayan Oveis Gharan (Washington)
Debmalya Panigrahi (Duke)
Richard Peng (Georgia Tech)
Ely Porat (Bar-Ilan)
Adi Rosén (CNRS & Paris 7)
Adrian Vetta (McGill)
Rico Zenklusen (ETH Zurich)

RANDOM

Mahdi Cheraghchi (Imperial College, London)
Elena Grigorescu (Purdue)
Neeraj Kayal (MSR Bangalore)
Adam Klivans (Austin)
Swastik Kopparty (Rutgers)
Ravi Kumar (Google)
Dana Moshkovitz (MIT)
Ashwin Nayak (Waterloo)
Ryan O'Donnell (CMU)
Asaf Shapira (Tel Aviv)
Ronen Shaltiel (Haifa)
Alexander Sherstov (UCLA)
Thomas Thierauf (Aalen)
Chris Umans (Caltech, chair)
Eric Vigoda (Georgia Tech)

Program Chairs


APPROX
Claire Mathieu
CNRS and École Normale Supérieure Paris
cmathieu@di.ens.fr

RANDOM
Chris Umans
Caltech
umans@cs.caltech.edu

Workshop Chairs


José Rolim,
U. of Geneva
jose.rolim@unige.ch
Klaus Jansen,
U. of Kiel
kj@informatik.uni-kiel.de
                                                      


Local Organisation


Sophie Laplante
Frédéric Magniez
Marc Renault
Adi Rosén, chair                                            

Dakini
Important dates
Submission deadline
Tues, April 19, 2016
15:00 PDT


Notification to authors
By June 22, 2016

Camera ready
July 1, 2016

Conference
Sept 7-9, 2016
Call for papers
IRIF

CNRS

Université Paris Diderot - Paris 7

FILOFOCS

IHP

Submissions

Abstract Format: Electronic submissions are solicited. Please consult the following servers:

For submission of APPROX papers: https://www.easychair.org/conferences/?conf=approx2016

For submission of RANDOM papers: https://www.easychair.org/conferences/?conf=random2016

Note: You will be asked to login using an EasyChair account. Instructions on how to register for such an account are available at the submission servers (you may also have an old account from a previous conference submission).

The submission must be received by 15:00 (PDT) of April 19 for your submission to be considered.

Submission Format: Submissions should start with the title of the paper, each author's name, affiliation, and e-mail address, followed by a brief abstract of the results to be presented. This should then be followed by a technical exposition on single-spaced pages on letter-size paper, using reasonable margins and at least 11-point font. The first 10 pages should contain the main ideas and techniques used to achieve the results including motivation and a clear comparison with related work (not including the references). There is no page limit but any material beyond the first 10 pages will be read at the sole discretion of the program committee.

Simultaneous submission to other conferences with published proceedings is not allowed.


Proceedings

The online proceedings of the conference can be found at: http://www.dagstuhl.de/dagpub/978-3-95977-018-7

Topics

Papers are solicited in all research areas related to randomization and approximation, including, but not limited to:

APPROX
  • approximation algorithms
  • hardness of approximation
  • small space, sub-linear time and streaming algorithms
  • online algorithms
  • approaches that go beyond worst case analysis
  • distributed and parallel approximation​
  • embeddings and metric space methods
  • mathematical programming methods
  • spectral methods
  • combinatorial optimization in graphs and networks
  • algorithmic game theory, mechanism design and economics
  • computational geometric problems
  • approximate learning
RANDOM
  • design and analysis of randomized algorithms
  • randomized complexity theory
  • pseudorandomness and derandomization
  • random combinatorial structures
  • random walks/Markov chains
  • expander graphs and randomness extractors
  • probabilistic proof systems
  • random projections and embeddings
  • error-correcting codes
  • average-case analysis
  • property testing
  • computational learning theory





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