2 citations · 3 across the 2 of their papers we have counts for
3 papers
stat.ML2021
Spectral risk-based learning using unbounded losses
Matthew J. Holland, El Mehdi Haress
In this work, we consider the setting of learning problems under a wide class of spectral risk (or "L-risk") functions, where a Lipschitz-continuous spectral density is used to fle…
stat.ML2020★ 2 cited
Learning with CVaR-based feedback under potentially heavy tails
Matthew J. Holland, El Mehdi Haress
We study learning algorithms that seek to minimize the conditional value-at-risk (CVaR), when all the learner knows is that the losses incurred may be heavy-tailed. We begin by stu…
math.ST2020★ 1 cited
Estimation Of all parameters in the Fractional Ornstein-Uhlenbeck model under discrete observations
El Mehdi Haress, Yaozhong Hu
Let the Ornstein-Uhlenbeck process driven by a fractional Brownian motion , described by be observed at discrete time instant…