The power of randomness for communication complexity
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Complexity classes without machines: on complete languages for UP
Theoretical Computer Science - Thirteenth International Colloquim on Automata, Languages and Programming, Renne
Private vs. common random bits in communication complexity
Information Processing Letters
SIAM Journal on Computing
Randomized versus nondeterministic communication complexity
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Elements of information theory
Elements of information theory
Monotone circuits for matching require linear depth
Journal of the ACM (JACM)
On read-once threshold formulae and their randomized decision tree complexity
Theoretical Computer Science - Special issue on structure in complexity theory
On the Monte Carlo Boolean decision tree complexity of read-once formulae
Random Structures & Algorithms
Theoretical Computer Science - Special issue on complexity theory and the theory of algorithms as developed in the CIS
Nondeterministic communication with a limited number of advice bits
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Communication complexity
Space lower bounds for distance approximation in the data stream model
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Complexity measures and decision tree complexity: a survey
Theoretical Computer Science - Complexity and logic
An Information Statistics Approach to Data Stream and Communication Complexity
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
The entropic limitations on VLSI computations(Extended Abstract)
STOC '81 Proceedings of the thirteenth annual ACM symposium on Theory of computing
Some complexity questions related to distributive computing(Preliminary Report)
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
On notions of information transfer in VLSI circuits
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Informational Complexity and the Direct Sum Problem for Simultaneous Message Complexity
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
An information statistics approach to data stream and communication complexity
Journal of Computer and System Sciences - Special issue on FOCS 2002
Balanced boolean functions that can be evaluated so that every input bit is unlikely to be read
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Span-program-based quantum algorithm for evaluating formulas
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Robust lower bounds for communication and stream computation
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Kolmogorov Complexity and Combinatorial Methods in Communication Complexity
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
Communication lower bounds via the chromatic number
FSTTCS'07 Proceedings of the 27th international conference on Foundations of software technology and theoretical computer science
A strong direct product theorem for disjointness
Proceedings of the forty-second ACM symposium on Theory of computing
Recognizing well-parenthesized expressions in the streaming model
Proceedings of the forty-second ACM symposium on Theory of computing
On directional vs. undirectional randomized decision tree complexity for read-once formulas
CATS '10 Proceedings of the Sixteenth Symposium on Computing: the Australasian Theory - Volume 109
Depth-independent lower bounds on the communication complexity of read-once Boolean formulas
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
Any AND-OR Formula of Size $N$ Can Be Evaluated in Time $N^{1/2+o(1)}$ on a Quantum Computer
SIAM Journal on Computing
Kolmogorov complexity and combinatorial methods in communication complexity
Theoretical Computer Science
On the competitive ratio of evaluating priced functions
Journal of the ACM (JACM)
Competitive Boolean function evaluation: Beyond monotonicity, and the symmetric case
Discrete Applied Mathematics
Memory lower bounds for XPath evaluation over XML streams
Journal of Computer and System Sciences
Improved bounds for the randomized decision tree complexity of recursive majority
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
A stronger LP bound for formula size lower bounds via clique constraints
Theoretical Computer Science
An improved lower bound for the randomized decision tree complexity of recursive majority,
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
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We show the following new lower bounds in two concrete complexity models:(1) In the two-party communication complexity model, we show that the tribes function on n inputs[6] has two-sided error randomized complexity Ω(n), while its nondeterminstic complexity and co-nondeterministic complexity are both Θ(√n). This separation between randomized and nondeterministic complexity is the best possible and it settles an open problem in Kushilevitz and Nisan[17], which was also posed by Beame and Lawry[5].(2) In the Boolean decision tree model, we show that the recursive majority-of-three function on 3h inputs has randomized complexity Ω((7/3)h). The deterministic complexity of this function is Θ(3h), and the nondeterministic complexity is Θ(2h). Our lower bound on the randomized complexity is a substantial improvement over any lower bound for this problem that can be obtained via the techniques of Saks and Wigderson [23], Heiman and Wigderson[14], and Heiman, Newman, and Wigderson[13]. Recursive majority is an important function for which a class of natural algorithms known as directional algorithms does not achieve the best randomized decision tree upper bound.information complexity, which quantifies the minimum amount of information that will have to be revealed about the inputs by every correct algorithm in a given model of computation.