773 research outputs found

### Finding a most biased coin with fewest flips

We study the problem of learning a most biased coin among a set of coins by
tossing the coins adaptively. The goal is to minimize the number of tosses
until we identify a coin i* whose posterior probability of being most biased is
at least 1-delta for a given delta. Under a particular probabilistic model, we
give an optimal algorithm, i.e., an algorithm that minimizes the expected
number of future tosses. The problem is closely related to finding the best arm
in the multi-armed bandit problem using adaptive strategies. Our algorithm
employs an optimal adaptive strategy -- a strategy that performs the best
possible action at each step after observing the outcomes of all previous coin
tosses. Consequently, our algorithm is also optimal for any starting history of
outcomes. To our knowledge, this is the first algorithm that employs an optimal
adaptive strategy under a Bayesian setting for this problem. Our proof of
optimality employs tools from the field of Markov games

### Parallel Load Balancing on Constrained Client-Server Topologies

We study parallel \emph{Load Balancing} protocols for a client-server
distributed model defined as follows.
There is a set \sC of $n$ clients and a set \sS of $n$ servers where each
client has
(at most) a constant number $d \geq 1$ of requests that must be assigned to
some server. The client set and the server one are connected to each other via
a fixed bipartite graph: the requests of client $v$ can only be sent to the
servers in its neighborhood $N(v)$. The goal is to assign every client request
so as to minimize the maximum load of the servers.
In this setting, efficient parallel protocols are available only for dense
topolgies. In particular, a simple symmetric, non-adaptive protocol achieving
constant maximum load has been recently introduced by Becchetti et al
\cite{BCNPT18} for regular dense bipartite graphs. The parallel completion time
is \bigO(\log n) and the overall work is \bigO(n), w.h.p.
Motivated by proximity constraints arising in some client-server systems, we
devise a simple variant of Becchetti et al's protocol \cite{BCNPT18} and we
analyse it over almost-regular bipartite graphs where nodes may have
neighborhoods of small size. In detail, we prove that, w.h.p., this new version
has a cost equivalent to that of Becchetti et al's protocol (in terms of
maximum load, completion time, and work complexity, respectively) on every
almost-regular bipartite graph with degree $\Omega(\log^2n)$.
Our analysis significantly departs from that in \cite{BCNPT18} for the
original protocol and requires to cope with non-trivial stochastic-dependence
issues on the random choices of the algorithmic process which are due to the
worst-case, sparse topology of the underlying graph

### The minimum-entropy set cover problem

AbstractWe consider the minimum entropy principle for learning data generated by a random source and observed with random noise.In our setting we have a sequence of observations of objects drawn uniformly at random from a population. Each object in the population belongs to one class. We perform an observation for each object which determines that it belongs to one of a given set of classes. Given these observations, we are interested in assigning the most likely class to each of the objects.This scenario is a very natural one that appears in many real life situations. We show that under reasonable assumptions finding the most likely assignment is equivalent to the following variant of the set cover problem. Given a universe U and a collection S=(S1,âŠ,St) of subsets of U, we wish to find an assignment f:UâS such that uâf(u) and the entropy of the distribution defined by the values |f-1(Si)| is minimized.We show that this problem is NP-hard and that the greedy algorithm for set cover s with an additive constant error with respect to the optimal cover. This sheds a new light on the behavior of the greedy set cover algorithm. We further enhance the greedy algorithm and show that the problem admits a polynomial time approximation scheme (PTAS).Finally, we demonstrate how this model and the greedy algorithm can be useful in real life scenarios, and in particular, in problems arising naturally in computational biology

### Universal Protocols for Information Dissemination Using Emergent Signals

We consider a population of $n$ agents which communicate with each other in a
decentralized manner, through random pairwise interactions. One or more agents
in the population may act as authoritative sources of information, and the
objective of the remaining agents is to obtain information from or about these
source agents. We study two basic tasks: broadcasting, in which the agents are
to learn the bit-state of an authoritative source which is present in the
population, and source detection, in which the agents are required to decide if
at least one source agent is present in the population or not.We focus on
designing protocols which meet two natural conditions: (1) universality, i.e.,
independence of population size, and (2) rapid convergence to a correct global
state after a reconfiguration, such as a change in the state of a source agent.
Our main positive result is to show that both of these constraints can be met.
For both the broadcasting problem and the source detection problem, we obtain
solutions with a convergence time of $O(\log^2 n)$ rounds, w.h.p., from any
starting configuration. The solution to broadcasting is exact, which means that
all agents reach the state broadcast by the source, while the solution to
source detection admits one-sided error on a $\varepsilon$-fraction of the
population (which is unavoidable for this problem). Both protocols are easy to
implement in practice and have a compact formulation.Our protocols exploit the
properties of self-organizing oscillatory dynamics. On the hardness side, our
main structural insight is to prove that any protocol which meets the
constraints of universality and of rapid convergence after reconfiguration must
display a form of non-stationary behavior (of which oscillatory dynamics are an
example). We also observe that the periodicity of the oscillatory behavior of
the protocol, when present, must necessarily depend on the number ^\\# X of
source agents present in the population. For instance, our protocols inherently
rely on the emergence of a signal passing through the population, whose period
is \Theta(\log \frac{n}{^\\# X}) rounds for most starting configurations. The
design of clocks with tunable frequency may be of independent interest, notably
in modeling biological networks

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