AbstractIt is well known that probabilistic boolean decision trees cannot be much more powerful than deterministic ones (N. Nisan, SIAM J. Comput.20, No. 6 (1991), 999–1007). Motivated by a question if randomization can significantly speed up a nondeterministic computation via a boolean decision tree, we address structural properties of Arthur–Merlin games in this model and prove some lower bounds. We consider two cases of interest, the first when the length of communication between the players is limited and the second, if it is not. While in the first case we can carry over the relations between the corresponding Turing complexity classes, in the second case we observe in contrast with Turing complexity that a one-round Merlin–Arthur prot...
Relations between the decision tree complexity and various other complexity measures of Boolean func...
We introduce a new powerful method for proving lower bounds on randomized and deterministic analyti...
. It is known that if a Boolean function f in n variables has a DNF and a CNF of size N then f also...
AbstractIt is well known that probabilistic boolean decision trees cannot be much more powerful than...
AbstractWe examine the power of Boolean functions with low L1 norms in several settings. In a large ...
grantor: University of TorontoUniform complexity classes are typically defined in terms of...
We investigate the power of randomness in two-party communication complexity. In particular, we stud...
AbstractWe discuss several complexity measures for Boolean functions: certificate complexity, sensit...
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
We investigate the power of randomness in two-party communication complexity. In particular, we stud...
A classic result of Nisan [SICOMP '91] states that the deterministic decision tree∗depth∗complexity ...
AbstractThe parity decision tree model extends the decision tree model by allowing the computation o...
Combinational complexity and depth are the most important complexity measures for Boolean functions....
We give an algorithm that learns any monotone Boolean function f: {−1, 1}n → {−1, 1} to any constant...
AbstractAssume we want to show that (a) the cost of any randomized decision tree computing a given B...
Relations between the decision tree complexity and various other complexity measures of Boolean func...
We introduce a new powerful method for proving lower bounds on randomized and deterministic analyti...
. It is known that if a Boolean function f in n variables has a DNF and a CNF of size N then f also...
AbstractIt is well known that probabilistic boolean decision trees cannot be much more powerful than...
AbstractWe examine the power of Boolean functions with low L1 norms in several settings. In a large ...
grantor: University of TorontoUniform complexity classes are typically defined in terms of...
We investigate the power of randomness in two-party communication complexity. In particular, we stud...
AbstractWe discuss several complexity measures for Boolean functions: certificate complexity, sensit...
The central focus of computational complexity theory is to measure the "hardness" of computing diffe...
We investigate the power of randomness in two-party communication complexity. In particular, we stud...
A classic result of Nisan [SICOMP '91] states that the deterministic decision tree∗depth∗complexity ...
AbstractThe parity decision tree model extends the decision tree model by allowing the computation o...
Combinational complexity and depth are the most important complexity measures for Boolean functions....
We give an algorithm that learns any monotone Boolean function f: {−1, 1}n → {−1, 1} to any constant...
AbstractAssume we want to show that (a) the cost of any randomized decision tree computing a given B...
Relations between the decision tree complexity and various other complexity measures of Boolean func...
We introduce a new powerful method for proving lower bounds on randomized and deterministic analyti...
. It is known that if a Boolean function f in n variables has a DNF and a CNF of size N then f also...