AbstractA new setting for analyzing problems in information-based complexity is formulated and discussed. By using concepts from two-person zero-sum game theory, a randomized setting results from defining the nth minimal radius as an infimum over “mixed” strategies of information operators and algorithms. After presenting an example, some general results are developed on the randomized radius and its relationship to average and worst case radii
Computational complexity has two goals: finding the inherent cost of some problem, and finding optim...
This lecture explains some applications of communication complexity to proving lower bounds in algor...
Relations between average case ϵ-complexity and Bayesian statistics are discussed. An algorithm corr...
AbstractA new setting for analyzing problems in information-based complexity is formulated and discu...
AbstractWe survey some recent results in information-based complexity. We focus on the worst case se...
Computational complexity studies the intrinsic difficulty of solving mathematically posed problems. ...
. We present a brief introduction to information-based complexity. An example of zero finding is cho...
We survey some recent results in information-based complexity, We focus on the worst case setting an...
AbstractInformation-based complexity studies problems where only partial and contaminated informatio...
Computational complexity has two goals: finding the inherent cost of some problem, and finding optim...
Two main concepts studied in machine learning theory are generalization gap (difference between trai...
There has been a recent surge of interest in the role of information in strategic interactions. Much...
A few cases are known where the computational analogue of some basic infor-mation theoretical result...
There are many open problems in the field of complexity. This means that, when analyzing the comple...
Information theory is a well developed field, but does not capture the essence of what information ...
Computational complexity has two goals: finding the inherent cost of some problem, and finding optim...
This lecture explains some applications of communication complexity to proving lower bounds in algor...
Relations between average case ϵ-complexity and Bayesian statistics are discussed. An algorithm corr...
AbstractA new setting for analyzing problems in information-based complexity is formulated and discu...
AbstractWe survey some recent results in information-based complexity. We focus on the worst case se...
Computational complexity studies the intrinsic difficulty of solving mathematically posed problems. ...
. We present a brief introduction to information-based complexity. An example of zero finding is cho...
We survey some recent results in information-based complexity, We focus on the worst case setting an...
AbstractInformation-based complexity studies problems where only partial and contaminated informatio...
Computational complexity has two goals: finding the inherent cost of some problem, and finding optim...
Two main concepts studied in machine learning theory are generalization gap (difference between trai...
There has been a recent surge of interest in the role of information in strategic interactions. Much...
A few cases are known where the computational analogue of some basic infor-mation theoretical result...
There are many open problems in the field of complexity. This means that, when analyzing the comple...
Information theory is a well developed field, but does not capture the essence of what information ...
Computational complexity has two goals: finding the inherent cost of some problem, and finding optim...
This lecture explains some applications of communication complexity to proving lower bounds in algor...
Relations between average case ϵ-complexity and Bayesian statistics are discussed. An algorithm corr...