4 citations · 10 across the 4 of their papers we have counts for
10 papers
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…
Better scalability under potentially heavy-tailed feedback
Matthew J. Holland
We study scalable alternatives to robust gradient descent (RGD) techniques that can be used when the losses and/or gradients can be heavy-tailed, though this will be unknown to the…
Making learning more transparent using conformalized performance prediction
Matthew J. Holland
In this work, we study some novel applications of conformal inference techniques to the problem of providing machine learning procedures with more transparent, accurate, and practi…
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…
Improved scalability under heavy tails, without strong convexity
Matthew J. Holland
Real-world data is laden with outlying values. The challenge for machine learning is that the learner typically has no prior knowledge of whether the feedback it receives (losses,…
Better scalability under potentially heavy-tailed gradients
Matthew J. Holland
We study a scalable alternative to robust gradient descent (RGD) techniques that can be used when the gradients can be heavy-tailed, though this will be unknown to the learner. The…