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Found 12 papers, 8 papers with code
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Computing a Gaussian process (GP) posterior has a computational cost cubical in the number of historical points.
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Our analysis shows the benefit of the proposed approach, that we hence incorporate in a library for large scale kernel methods to derive adaptively tuned solutions.
Mean Nyström Embeddings for Adaptive Compressive Learning
Compressive learning is an approach to efficient large scale learning based on sketching an entire dataset to a single mean embedding (the sketch), i. e. a vector of generalized moments.
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
We introduce ParK, a new large-scale solver for kernel ridge regression.
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
In this paper, we introduce Ada-BKB (Adaptive Budgeted Kernelized Bandit), a no-regret Gaussian process optimization algorithm for functions on continuous domains, that provably runs in $O(T^2 d_\text{eff}^2)$, where $d_\text{eff}$ is the effective dimension of the explored space, and which is typically much smaller than $T$.
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems, since na\"ive implementations scale poorly with data size.
On Mixup Regularization
We show that Mixup can be interpreted as standard empirical risk minimization estimator subject to a combination of data transformation and random perturbation of the transformed data.
Ranked #75 on
Image Classification
on ObjectNet
(using extra training data)
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Gaussian processes (GP) are one of the most successful frameworks to model uncertainty.
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Moreover, we show that our procedure selects at most $\tilde{O}(d_{eff})$ points, where $d_{eff}$ is the effective dimension of the explored space, which is typically much smaller than both $d$ and $t$.
On Fast Leverage Score Sampling and Optimal Learning
Leverage score sampling provides an appealing way to perform approximate computations for large matrices.
Learning with SGD and Random Features
Sketching and stochastic gradient methods are arguably the most common techniques to derive efficient large scale learning algorithms.
FALKON: An Optimal Large Scale Kernel Method
In this paper, we take a substantial step in scaling up kernel methods, proposing FALKON, a novel algorithm that allows to efficiently process millions of points.
