🏢 Aarhus University

A new, surprisingly simple boosting algorithm achieves provably optimal sample complexity and outperforms existing algorithms on large datasets.

This paper introduces proportionally fair non-centroid clustering, achieving fairness guarantees via novel algorithms and auditing methods, demonstrating significant improvements over traditional meth…

This paper closes the performance gap in parallel boosting algorithms by presenting improved lower bounds and a novel algorithm matching these bounds, settling the parallel complexity of sample-optima…

Derandomizing multi-distribution learning is computationally hard, but a structural condition allows efficient black-box conversion of randomized predictors to deterministic ones.