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#### What we do and why

The Electronic Colloquium on Computational Complexity is a new forum for the rapid and widespread interchange of ideas, techniques, and research in computational complexity. The purpose of this Colloquium is to use electronic media for scientific communication and discussions in the computational complexity community. The Electronic Colloquium on Computational Complexity (ECCC) welcomes papers, short notes and surveys with
• relevance to the theory of computation,
• clear mathematical profile and
• strictly mathematical format.

#### Central topics

• models of computation and their complexity,
• complexity bounds (with the emphasis on lower bounds).
Specific areas including complexity issues are
• combinatorics,
• communication complexity,
• cryptography,
• combinatorial optimization,
• complexity of learning algorithms,
• logic.

Here are some papers on the idea and concept of electronic colloquia and ECCC.
7th April 2014 13:36

#### ECCC Archive DVD 2013

191 reports have been published on ECCC in 2013. The collection of all these reports is now available on DVD. You can order the archive (and also the archive DVDs from earlier years) at the local office. Please email < href="mailto:eccc@eccc.hpi-web.de">eccc@eccc.hpi-web.de for ordering.

4th March 2013 09:03

#### ECCC Archive DVD 2012

In 2012 we had a total count of 186 published reports on ECCC. The collection of all the reports from 2012 is now available on DVD. You can order the archive (and also the archive DVDs from earlier years) at the local office. Please email to eccc@eccc.hpi-web.de for ordering.

6th March 2012 12:04

#### ECCC Archive DVD 2011

In 2011 we had a total count of 174 published reports on ECCC. The collection of all the reports from 2011 is now available on DVD. You can order the archive (and also the archive DVDs from earlier years) at the local office. Please email to eccc@eccc.hpi-web.de for ordering.

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TR16-019 | 5th February 2016
Ran Raz

#### Fast Learning Requires Good Memory: A Time-Space Lower Bound for Parity Learning

We prove that any algorithm for learning parities requires either a memory of quadratic size or an exponential number of samples. This proves a recent conjecture of Steinhardt, Valiant and Wager and shows that for some learning problems a large storage space is crucial.

More formally, in the problem of ... more >>>

TR16-018 | 3rd February 2016
Kuan Cheng, Xin Li

#### Randomness Extraction in $AC^0$ and with Small Locality

We study two variants of seeded randomness extractors. The first one, as studied by Goldreich et al. \cite{goldreich2015randomness}, is seeded extractors that can be computed by $AC^0$ circuits. The second one, as introduced by Bogdanov and Guo \cite{bogdanov2013sparse}, is (strong) extractor families that consist of sparse transformations, i.e., functions that ... more >>>

TR16-017 | 24th December 2015
Georgios Stamoulis

#### Limitations of Linear Programming Techniques for Bounded Color Matchings

Given a weighted graph $G = (V,E,w)$, with weight function $w: E \rightarrow \mathbb{Q^+}$, a \textit{matching} $M$ is a set of pairwise non-adjacent edges. In the optimization setting, one seeks to find a matching of \textit{maximum} weight. In the \textit{multi-criteria} (or \textit{multi-budgeted}) setting, we are also given $\ell$ length functions ... more >>>

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