24 citations · 57 across the 9 of their papers we have counts for
10 papers
100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models
Yeounoh Chung, Rushabh Desai, Jian He +9
Several data warehouse and database providers have recently introduced extensions to SQL called AI Queries, enabling users to specify functions and conditions in SQL that are evalu…
LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects
Yves-Laurent Kom Samo
We introduce the first application of the lean methodology to machine learning projects. Similar to lean startups and lean manufacturing, we argue that lean machine learning (LeanM…
MIND: Inductive Mutual Information Estimation, A Convex Maximum-Entropy Copula Approach
Yves-Laurent Kom Samo
We propose a novel estimator of the mutual information between two ordinal vectors and . Our approach is inductive (as opposed to deductive) in that it depends on the data g…
What Makes An Asset Useful?
Yves-Laurent Kom Samo, Dieter Hendricks
Given a new candidate asset represented as a time series of returns, how should a quantitative investment manager be thinking about assessing its usefulness? This is a key qualitat…
Stochastic Portfolio Theory: A Machine Learning Perspective
Yves-Laurent Kom Samo, Alexander Vervuurt
In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation. Our approach is deeply rooted in Stochastic Portfolio Theory (SPT), a stocha…
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting
Yves-Laurent Kom Samo, Stephen J. Roberts
In this paper we introduce a novel online time series forecasting model we refer to as the pM-GP filter. We show that our model is equivalent to Gaussian process regression, with t…
String and Membrane Gaussian Processes
Yves-Laurent Kom Samo, Stephen Roberts
In this paper we introduce a novel framework for making exact nonparametric Bayesian inference on latent functions, that is particularly suitable for Big Data tasks. Firstly, we in…
String Gaussian Process Kernels
Yves-Laurent Kom Samo, Stephen Roberts
We introduce a new class of nonstationary kernels, which we derive as covariance functions of a novel family of stochastic processes we refer to as string Gaussian processes (strin…
Generalized Spectral Kernels
Yves-Laurent Kom Samo, Stephen Roberts
In this paper we propose a family of tractable kernels that is dense in the family of bounded positive semi-definite functions (i.e. can approximate any bounded kernel with arbitra…
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Yves-Laurent Kom Samo, Stephen Roberts
In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or t…