36 research outputs found
Entropy Samplers and Strong Generic Lower Bounds For Space Bounded Learning
With any hypothesis class one can associate a bipartite graph whose vertices are the hypotheses H on one side and all possible labeled examples X on the other side, and an hypothesis is connected to all the labeled examples that are consistent with it. We call this graph the hypotheses graph. We prove that any hypothesis class whose hypotheses graph is mixing cannot be learned using less than Omega(log^2 |H|) memory bits unless the learner uses at least a large number |H|^Omega(1) labeled examples. Our work builds on a combinatorial framework that we suggested in a previous work for proving lower bounds on space bounded learning. The strong lower bound is obtained by defining a new notion of pseudorandomness, the entropy sampler. Raz obtained a similar result using different ideas
Approximating Dense Max 2-CSPs
In this paper, we present a polynomial-time algorithm that approximates
sufficiently high-value Max 2-CSPs on sufficiently dense graphs to within
approximation ratio for any constant .
Using this algorithm, we also achieve similar results for free games,
projection games on sufficiently dense random graphs, and the Densest
-Subgraph problem with sufficiently dense optimal solution. Note, however,
that algorithms with similar guarantees to the last algorithm were in fact
discovered prior to our work by Feige et al. and Suzuki and Tokuyama.
In addition, our idea for the above algorithms yields the following
by-product: a quasi-polynomial time approximation scheme (QPTAS) for
satisfiable dense Max 2-CSPs with better running time than the known
algorithms
Strong Parallel Repetition for Unique Games on Small Set Expanders
Strong Parallel Repetition for Unique Games on Small Set Expanders
The strong parallel repetition problem for unique games is to efficiently
reduce the 1-delta vs. 1-C*delta gap problem of Boolean unique games (where C>1
is a sufficiently large constant) to the 1-epsilon vs. epsilon gap problem of
unique games over large alphabet. Due to its importance to the Unique Games
Conjecture, this problem garnered a great deal of interest from the research
community. There are positive results for certain easy unique games (e.g.,
unique games on expanders), and an impossibility result for hard unique games.
In this paper we show how to bypass the impossibility result by enlarging the
alphabet sufficiently before repetition. We consider the case of unique games
on small set expanders for two setups: (i) Strong small set expanders that
yield easy unique games. (ii) Weaker small set expanders underlying possibly
hard unique games as long as the game is mildly fortified. We show how to
fortify unique games in both cases, i.e., how to transform the game so
sufficiently large induced sub-games have bounded value. We then prove strong
parallel repetition for the fortified games. Prior to this work fortification
was known for projection games but seemed hopeless for unique games
A No-Go Theorem for Derandomized Parallel Repetition: Beyond Feige-Kilian
In this work we show a barrier towards proving a randomness-efficient
parallel repetition, a promising avenue for achieving many tight
inapproximability results. Feige and Kilian (STOC'95) proved an impossibility
result for randomness-efficient parallel repetition for two prover games with
small degree, i.e., when each prover has only few possibilities for the
question of the other prover. In recent years, there have been indications that
randomness-efficient parallel repetition (also called derandomized parallel
repetition) might be possible for games with large degree, circumventing the
impossibility result of Feige and Kilian. In particular, Dinur and Meir
(CCC'11) construct games with large degree whose repetition can be derandomized
using a theorem of Impagliazzo, Kabanets and Wigderson (SICOMP'12). However,
obtaining derandomized parallel repetition theorems that would yield optimal
inapproximability results has remained elusive.
This paper presents an explanation for the current impasse in progress, by
proving a limitation on derandomized parallel repetition. We formalize two
properties which we call "fortification-friendliness" and "yields robust
embeddings." We show that any proof of derandomized parallel repetition
achieving almost-linear blow-up cannot both (a) be fortification-friendly and
(b) yield robust embeddings. Unlike Feige and Kilian, we do not require the
small degree assumption.
Given that virtually all existing proofs of parallel repetition, including
the derandomized parallel repetition result of Dinur and Meir, share these two
properties, our no-go theorem highlights a major barrier to achieving
almost-linear derandomized parallel repetition
AM with Multiple Merlins
We introduce and study a new model of interactive proofs: AM(k), or
Arthur-Merlin with k non-communicating Merlins. Unlike with the better-known
MIP, here the assumption is that each Merlin receives an independent random
challenge from Arthur. One motivation for this model (which we explore in
detail) comes from the close analogies between it and the quantum complexity
class QMA(k), but the AM(k) model is also natural in its own right.
We illustrate the power of multiple Merlins by giving an AM(2) protocol for
3SAT, in which the Merlins' challenges and responses consist of only
n^{1/2+o(1)} bits each. Our protocol has the consequence that, assuming the
Exponential Time Hypothesis (ETH), any algorithm for approximating a dense CSP
with a polynomial-size alphabet must take n^{(log n)^{1-o(1)}} time. Algorithms
nearly matching this lower bound are known, but their running times had never
been previously explained. Brandao and Harrow have also recently used our 3SAT
protocol to show quasipolynomial hardness for approximating the values of
certain entangled games.
In the other direction, we give a simple quasipolynomial-time approximation
algorithm for free games, and use it to prove that, assuming the ETH, our 3SAT
protocol is essentially optimal. More generally, we show that multiple Merlins
never provide more than a polynomial advantage over one: that is, AM(k)=AM for
all k=poly(n). The key to this result is a subsampling theorem for free games,
which follows from powerful results by Alon et al. and Barak et al. on
subsampling dense CSPs, and which says that the value of any free game can be
closely approximated by the value of a logarithmic-sized random subgame.Comment: 48 page
The Projection Games Conjecture and the NP-Hardness of ln n-Approximating Set-Cover
We suggest the research agenda of establishing new hardness of approximation results based on the “projection games conjecture”, i.e., an instantiation of the Sliding Scale Conjecture of Bellare, Goldwasser, Lund and Russell to projection games.
We pursue this line of research by establishing a tight NP-hardness result for the Set-Cover problem. Specifically, we show that under the projection games conjecture (in fact, under a quantitative version of the conjecture that is only slightly beyond the reach of current techniques), it is NP-hard to approximate Set-Cover on instances of size N to within (1 − α)ln N for arbitrarily small α > 0. Our reduction establishes a tight trade-off between the approximation accuracy α and the time required for the approximation 2[superscript NΩ(α)], assuming Sat requires exponential time.
The reduction is obtained by modifying Feige’s reduction. The latter only provides a lower bound of 2[superscript NΩ(α/loglogN)] on the time required for (1 − α)ln N-approximating Set-Cover assuming Sat requires exponential time (note that N[superscript 1/loglogN] = N[superscript o(1)]). The modification uses a combinatorial construction of a bipartite graph in which any coloring of the first side that does not use a color for more than a small fraction of the vertices, makes most vertices on the other side have their neighbors all colored in different colors
Parallel Repetition From Fortification
The Parallel Repetition Theorem upper-bounds the value of a repeated (tensored) two prover game in terms of the value of the base game and the number of repetitions. In this work we give a simple transformation on games – “fortification” – and show that for fortified games, the value of the repeated game decreases perfectly exponentially with the number of repetitions, up to an arbitrarily small additive error. Our proof is combinatorial and short. As corollaries, we obtain: (1) Starting from a PCP Theorem with soundness error bounded away from 1, we get a PCP with arbitrarily small constant soundness error. In particular, starting with the combinatorial PCP of Dinur, we get a combinatorial PCP with low error. The latter can be used for hardness of approximation as in the work of Hastad. (2) Starting from the work of the author and Raz, we get a projection PCP theorem with the smallest soundness error known today. The theorem yields nearly a quadratic improvement in the size compared to previous work. We then discuss the problem of derandomizing parallel repetition, and the limitations of the fortification idea in this setting. We point out a connection between the problem of derandomizing parallel repetition and the problem of composition. This connection could shed light on the so-called Projection Games Conjecture, which asks for projection PCP with minimal error.National Science Foundation (U.S.) (Grant 1218547