SIAM Journal on Computing
Property testing in bounded degree graphs
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Property testing and its connection to learning and approximation
Journal of the ACM (JACM)
Sampling algorithms: lower bounds and applications
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Foundations of Cryptography: Basic Tools
Foundations of Cryptography: Basic Tools
Complexity measures and decision tree complexity: a survey
Theoretical Computer Science - Complexity and logic
Randomness vs. Time: De-Randomization under a Uniform Assumption
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Three theorems regarding testing graph properties
Random Structures & Algorithms
Probabilistic Boolean decision trees and the complexity of evaluating game trees
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Hierarchy Theorems for Property Testing
Computational Complexity - 13th International Workshop on Randomization and Computation Random 09
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We study the possibilities and limitations of pseudodeterministic algorithms, algorithms, a notion put forward by Gat and Goldwasser (2011). These are probabilistic algorithms that solve search problems such that on each input, with high probability, they output the same solution, which may be thought of as a canonical solution. We consider both the standard setting of (probabilistic) polynomial-time algorithms and the setting of (probabilistic) sublinear-time algorithms. Some of our results are outlined next. In the standard setting, we show that pseudodeterministic algorithms are more powerful than deterministic algorithms if and only if \cP\neq\BPP, but are weaker than general probabilistic algorithms. In the sublinear-time setting, we show that if a search problem has a pseudodeterministic algorithm of query complexity q, then this problem can be solved deterministically making O(q4) queries. This refers to total search problems. In contrast, for several natural promise search problems, we present pseudodeterministic algorithms that are much more efficient than their deterministic counterparts.