7,244 research outputs found
Cooperative Online Learning: Keeping your Neighbors Updated
We study an asynchronous online learning setting with a network of agents. At
each time step, some of the agents are activated, requested to make a
prediction, and pay the corresponding loss. The loss function is then revealed
to these agents and also to their neighbors in the network. Our results
characterize how much knowing the network structure affects the regret as a
function of the model of agent activations. When activations are stochastic,
the optimal regret (up to constant factors) is shown to be of order
, where is the horizon and is the independence
number of the network. We prove that the upper bound is achieved even when
agents have no information about the network structure. When activations are
adversarial the situation changes dramatically: if agents ignore the network
structure, a lower bound on the regret can be proven, showing that
learning is impossible. However, when agents can choose to ignore some of their
neighbors based on the knowledge of the network structure, we prove a
sublinear regret bound, where is the clique-covering number of the network
Dueling Bandits with Adversarial Sleeping
We introduce the problem of sleeping dueling bandits with stochastic
preferences and adversarial availabilities (DB-SPAA). In almost all dueling
bandit applications, the decision space often changes over time; eg, retail
store management, online shopping, restaurant recommendation, search engine
optimization, etc. Surprisingly, this `sleeping aspect' of dueling bandits has
never been studied in the literature. Like dueling bandits, the goal is to
compete with the best arm by sequentially querying the preference feedback of
item pairs. The non-triviality however results due to the non-stationary item
spaces that allow any arbitrary subsets items to go unavailable every round.
The goal is to find an optimal `no-regret' policy that can identify the best
available item at each round, as opposed to the standard `fixed best-arm regret
objective' of dueling bandits. We first derive an instance-specific lower bound
for DB-SPAA , where is the number of items and is the
gap between items and . This indicates that the sleeping problem with
preference feedback is inherently more difficult than that for classical
multi-armed bandits (MAB). We then propose two algorithms, with near optimal
regret guarantees. Our results are corroborated empirically
Jamming-Resistant Learning in Wireless Networks
We consider capacity maximization in wireless networks under adversarial
interference conditions. There are n links, each consisting of a sender and a
receiver, which repeatedly try to perform a successful transmission. In each
time step, the success of attempted transmissions depends on interference
conditions, which are captured by an interference model (e.g. the SINR model).
Additionally, an adversarial jammer can render a (1-delta)-fraction of time
steps unsuccessful. For this scenario, we analyze a framework for distributed
learning algorithms to maximize the number of successful transmissions. Our
main result is an algorithm based on no-regret learning converging to an
O(1/delta)-approximation. It provides even a constant-factor approximation when
the jammer exactly blocks a (1-delta)-fraction of time steps. In addition, we
consider a stochastic jammer, for which we obtain a constant-factor
approximation after a polynomial number of time steps. We also consider more
general settings, in which links arrive and depart dynamically, and where each
sender tries to reach multiple receivers. Our algorithms perform favorably in
simulations.Comment: 22 pages, 2 figures, typos remove
One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits
We address the problem of \emph{`Internal Regret'} in \emph{Sleeping Bandits}
in the fully adversarial setup, as well as draw connections between different
existing notions of sleeping regrets in the multiarmed bandits (MAB) literature
and consequently analyze the implications: Our first contribution is to propose
the new notion of \emph{Internal Regret} for sleeping MAB. We then proposed an
algorithm that yields sublinear regret in that measure, even for a completely
adversarial sequence of losses and availabilities. We further show that a low
sleeping internal regret always implies a low external regret, and as well as a
low policy regret for iid sequence of losses. The main contribution of this
work precisely lies in unifying different notions of existing regret in
sleeping bandits and understand the implication of one to another. Finally, we
also extend our results to the setting of \emph{Dueling Bandits} (DB)--a
preference feedback variant of MAB, and proposed a reduction to MAB idea to
design a low regret algorithm for sleeping dueling bandits with stochastic
preferences and adversarial availabilities. The efficacy of our algorithms is
justified through empirical evaluations
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