17 citations · 19 across the 8 of their papers we have counts for
12 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…
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,…
Target-Aware State-Adaptive -Dirichlet Graph Neural Regression for Non-Invasive Body-Composition Estimation
Nadejda Drenska, Matthew Lemoine, Gowri Priya Sunkara +3
Accurate estimation of body-composition outcomes, including body fat percentage (BFP), bone mineral density (BMD), and appendicular lean mass (ALM), is important for evaluating met…
Games on deBruijn Graphs and Cycle Means
Nadejda Drenska
deBruijn graphs are widely used in genomics and computer science. In this paper we present a novel approach to finding weights on edges of doubly weighted deBruijn graphs. Given an…
Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learning
Gyaneshwar Agrahari, Kiran Bist, Monika Pandey +7
Accurate prediction of anthropometric body composition variables, such as Appendicular Lean Mass (ALM), Body Fat Percentage (BFP), and Bone Mineral Density (BMD), is essential for…
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…
Asymptotically optimal strategies for online prediction with history-dependent experts
Jeff Calder, Nadejda Drenska
We establish sharp asymptotically optimal strategies for the problem of online prediction with history dependent experts. The prediction problem is played (in part) over a discrete…
Online Prediction With History-Dependent Experts: The General Case
Nadejda Drenska, Jeff Calder
We study the problem of prediction of binary sequences with expert advice in the online setting, which is a classic example of online machine learning. We interpret the binary sequ…
A PDE Approach to the Prediction of a Binary Sequence with Advice from Two History-Dependent Experts
Nadejda Drenska, Robert V. Kohn
The prediction of a binary sequence is a classic example of online machine learning. We like to call it the 'stock prediction problem,' viewing the sequence as the price history of…
Prediction with Expert Advice: a PDE Perspective
Nadejda Drenska, Robert V. Kohn
This work addresses a classic problem of online prediction with expert advice. We assume an adversarial opponent, and we consider both the finite-horizon and random-stopping versio…