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Found 11 papers, 7 papers with code
Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data.
Sequence Modeling with Multiresolution Convolutional Memory
Popular approaches in the space tradeoff between the memory burden of brute-force enumeration and comparison, as in transformers, the computational burden of complicated sequential dependencies, as in recurrent neural networks, or the parameter burden of convolutional networks with many or large filters.
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Reasoning about the physical world requires models that are endowed with the right inductive biases to learn the underlying dynamics.
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
Given such an $\mathcal{A}$, and a prior distribution over $f$, we refer to the problem of inferring the output of $\mathcal{A}$ using $T$ evaluations as Bayesian Algorithm Execution (BAX).
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
State-of-the-art methods for scalable Gaussian processes use iterative algorithms, requiring fast matrix vector multiplies (MVMs) with the covariance kernel.
Is Importance Weighting Incompatible with Interpolating Classifiers?
As a remedy, we show that polynomially-tailed losses restore the effects of importance reweighting in correcting distribution shift in overparameterized models.
Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild
Diabetes encompasses a complex landscape of glycemic control that varies widely among individuals.
$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering
Analysis on $DC^2$ when applied to spectral clustering shows that the loss in clustering accuracy due to data division and reduction is upper bounded by the data approximation error which would vanish with recursive random projections.
GOPHER: Categorical probabilistic forecasting withgraph structure via local continuous-time dynamics
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
In this paper, we propose to learn the effects of macronutrition content from glucose-insulin data and meal covariates.
