24 citations · 24 across the 8 of their papers we have counts for
11 papers
On prediction from expert advice with more than five experts
Jeff Calder, Nadejda Drenska
We prove that no single rank ordered adversary strategy is globally optimal for the prediction with expert advice problem with six or more experts, in both the geometric stopping a…
The finite-horizon five-expert prediction problem
Jeff Calder, Nadejda Drenska
We give an explicit solution to the five expert prediction with expert advice partial differential equation (PDE) in the finite-time horizon setting. The solution formula establish…
A counterexample to the pointwise validity of an asymptotic mean value property for -harmonic functions
Ángel Arroyo, Jeff Calder, Mikko Parviainen
We give a counterexample showing that an asymptotic mean value property for -harmonic functions in does not necessarily hold in the pointwise sense for a…
An explicit solution of the five-expert prediction PDE and the exact optimality set of COMB
Jeff Calder, Nadejda Drenska
In this paper, we derive an explicit solution of the stationary prediction with expert advice PDE for five experts. The formula is given in three regions. In the first two regions,…
An Elementary Proof of a Minimax Theorem
Jeff Calder
Here, we give a self-contained and elementary proof of a minimax theorem due to Fan in a simplified setting that can be taught in an advanced undergraduate course. Our proof follow…
Dynamics Over Landscape: The Emergence of Linear Separability via Spectral Alignment in Contrastive Learning
Jeff Calder, Wonjun Lee
Contrastive learning effectively clusters data despite a loss landscape filled with poor solutions, a success that is heavily dependent on the choice of data augmentations. How opt…
GLL: A Differentiable Graph Learning Layer for Neural Networks
Jason Brown, Bohan Chen, Harris Hardiman-Mostow +2
Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods f…
Numerical solution of a PDE arising from prediction with expert advice
Jeff Calder, Nadejda Drenska, Drisana Mosaphir
This work investigates the online machine learning problem of prediction with expert advice in an adversarial setting through numerical analysis of, and experiments with, a related…
Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian
Jeff Calder, Nadejda Drenska
In this paper we give a broad overview of the intersection of partial differential equations (PDEs) and graph-based semi-supervised learning. The overview is focused on a large bod…
Monotone discretizations of levelset convex geometric PDEs
Jeff Calder, Wonjun Lee
We introduce a novel algorithm that converges to level-set convex viscosity solutions of high-dimensional Hamilton-Jacobi equations. The algorithm is applicable to a broad class of…
Pareto-depth for Multiple-query Image Retrieval
Ko-Jen Hsiao, Jeff Calder, Alfred O. Hero
Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this pa…