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Found 10 papers, 2 papers with code
Sparse Gaussian Processes with Spherical Harmonic Features Revisited
Further, we introduce sparseness in the eigenbasis by variational learning of the spherical harmonic phases.
Double Deep Q Networks for Sensor Management in Space Situational Awareness
In this paper, we demonstrate the use of reinforcement learning to develop a sensor management policy for SSA.
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
Methods for learning from data depend on various types of tuning parameters, such as penalization strength or step size.
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
We revisit on-average algorithmic stability of GD for training overparameterised shallow neural networks and prove new generalisation and excess risk bounds without the NTK or PL assumptions.
Learning with Gradient Descent and Weakly Convex Losses
Out of sample guarantees are then achieved by decomposing the test error into generalisation, optimisation and approximation errors, each of which can be bounded and traded off with respect to algorithmic parameters, sample size and magnitude of this eigenvalue.
Decentralised Learning with Random Features and Distributed Gradient Descent
Under standard source and capacity assumptions, we establish high probability bounds on the predictive performance for each agent as a function of the step size, number of iterations, inverse spectral gap of the communication matrix and number of Random Features.
Asymptotics of Ridge (less) Regression under General Source Condition
We analyze the prediction error of ridge regression in an asymptotic regime where the sample size and dimension go to infinity at a proportional rate.
Distributed Machine Learning with Sparse Heterogeneous Data
Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneous data sets whose similarity structure is encoded by a graph topology.
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
We show that if agents hold sufficiently many samples with respect to the network size, then Distributed Gradient Descent achieves optimal statistical rates with a number of iterations that scales, up to a threshold, with the inverse of the spectral gap of the gossip matrix divided by the number of samples owned by each agent raised to a problem-dependent power.
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent
We propose graph-dependent implicit regularisation strategies for distributed stochastic subgradient descent (Distributed SGD) for convex problems in multi-agent learning.
