Online computation and competitive analysis
Online computation and competitive analysis
Online decision problems with large strategy sets
Online decision problems with large strategy sets
Property Testing: A Learning Theory Perspective
Foundations and Trends® in Machine Learning
Concentration of Measure for the Analysis of Randomized Algorithms
Concentration of Measure for the Analysis of Randomized Algorithms
Algorithmic and Analysis Techniques in Property Testing
Foundations and Trends® in Theoretical Computer Science
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
| Hi-index | 5.23 |
We formulate and study a new computational model for dynamic data. In this model, the data changes gradually and the goal of an algorithm is to compute the solution to some problem on the data at each time step, under the constraint that it only has limited access to the data each time. As the data is constantly changing and the algorithm might be unaware of these changes, it cannot be expected to always output the exact right solution; we are interested in algorithms that guarantee to output an approximate solution. In particular, we focus on the fundamental problems of sorting and selection, where the true ordering of the elements changes slowly. We provide algorithms with performance close to the optimal in expectation and with high probability.