Kasper Green Larsen

Approximate) uncertain skylines

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each points uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, near-linear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline.

The Johnson-Lindenstrauss Lemma Is Optimal for Linear Dimensionality Reduction

For any

Faster online matrix-vector multiplication

n>1

The Query Complexity of Finding a Hidden Permutation

and

How to Prove Knowledge of Small Secrets

0<\varepsilon<1/2

On Hardness of Several String Indexing Problems

, we show the existence of an

Succinct sampling from discrete distributions

n^{O(1)}

A Dichotomy for Regular Expression Membership Testing

-point subset

New Unconditional Hardness Results for Dynamic and Online Problems

X

Optimality of the Johnson-Lindenstrauss Lemma

of