Searching with known error probability
Theoretical Computer Science
Searching in the presence of linearly bounded errors
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Elements of information theory
Elements of information theory
Ulam's searching game with a fixed number of lies
Theoretical Computer Science
On playing “Twenty Questions” with a liar
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
Comparison-based search in the presence of errors
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
The Value of Knowing a Demand Curve: Bounds on Regret for Online Posted-Price Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Noisy sorting without resampling
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Characterizing truthful multi-armed bandit mechanisms: extended abstract
Proceedings of the 10th ACM conference on Electronic commerce
Sorting and Selection with Imprecise Comparisons
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
On active learning of record matching packages
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Fast error-tolerant quartet phylogeny algorithms
CPM'11 Proceedings of the 22nd annual conference on Combinatorial pattern matching
Towards a practical O(n log n) phylogeny algorithm
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
ESA'11 Proceedings of the 19th European conference on Algorithms
The K-armed dueling bandits problem
Journal of Computer and System Sciences
A Bayesian approach to stochastic root finding
Proceedings of the Winter Simulation Conference
Fast error-tolerant quartet phylogeny algorithms
Theoretical Computer Science
| Hi-index | 0.00 |
We study a noisy version of the classic binary search problem of inserting an element into its proper place within an ordered sequence by comparing it with elements of the sequence. In the noisy version we can not compare elements directly. Instead we are given a coin corresponding to each element of the sequence, such that as one goes through the ordered sequence the probability of observing heads when tossing the corresponding coin increases. We design online algorithms which adaptively choose a sequence of experiments, each consisting of tossing a single coin, with the goal of identifying the highest-numbered coin in the ordered sequence whose heads probability is less than some specified target value. Possible applications of such algorithms include investment planning, sponsored search advertising, admission control in queueing networks, college admissions, and admitting new members into an organization ranked by ability, such as a tennis ladder.