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Stochastic Linear Optimization under Bandit Feedback
TLDR
A nearly complete characterization of the classical stochastic k-armed bandit problem in terms of both upper and lower bounds for the regret is given, and two variants of an algorithm based on the idea of “upper confidence bounds” are presented.
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Allocating indivisible goods
TLDR
This work studies a fairness criterion, called the Max-Min Fairness problem, for k players who want to allocate among themselves m indivisible goods, and presents a simple 1/(m - k + 1) approximation algorithm which allocates to every player at least 1/k fraction of the value of all but the k - 1 heaviest items.
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The Price of Bandit Information for Online Optimization
TLDR
This paper presents an algorithm which achieves O*(n3/2 √T) regret and presents lower bounds showing that this gap is at least √n, which is conjecture to be the correct order.
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High-Probability Regret Bounds for Bandit Online Linear Optimization
TLDR
This paper eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings, and improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary.
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Robbing the bandit: less regret in online geometric optimization against an adaptive adversary
TLDR
It is proved that, for a large class of full-information online optimization problems, the optimal regret against an adaptive adversary is the same as against a non-adaptive adversary.
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Error limiting reductions between classification tasks
We introduce a reduction-based model for analyzing supervised learning tasks. We use this model to devise a new reduction from multi-class cost-sensitive classification to binary classification with…
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An Empirical Comparison of Algorithms for Aggregating Expert Predictions
TLDR
It is found that it is difficult to improve over simple averaging of the predictions in terms of prediction accuracy, but that there is room for improvement in quadratic loss.
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The Energy Complexity of Broadcast
TLDR
This paper investigates the energy complexity of fundamental communication primitives such as Broadcast in multi-hop radio networks, and shows that it is possible to achieve near optimality in time complexity with only poly logn energy cost.
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Brief announcement: breaking the O(nm) bit barrier, secure multiparty computation with a static adversary
TLDR
A Nash equilibrium protocol is described that solves SMPC and requires each player to send Õ(⁄<i>n</i>+<i-m</i><i-mn</i>) messages and perform €(â‚n, m) computations to compute any function <i>f</i, where <i*m> is the size of a circuit to compute <i-i>.
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Independent Sets in Random Graphs from the Weighted Second Moment Method
We prove new lower bounds on the likely size of a maximum independent set in a random graph with a given average degree. Our method is a weighted version of the second moment method, where we give…
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