arXiv research

Optimal algorithms identify non-dominated arms in multi-output linear bandit models.

The paper develops exact and approximate conformal inference methods for multi-output regression.

Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for …

MO-GP models fill gaps in biophysical data with across-domain info transfer.

The paper introduces a non-linear version of the process convolution formalism for building covariance functions for multi-output Gaussian processes. The non-linearity is introduced via Volterra series, one series per each output. We provide closed-form expressions for the mean function and the covariance function of t…

We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valued Gaussian process prior to jointly model the parameters in all likelihoods as latent functions. Our multi-output Gaussian process uses a c…

Brenier isotonic regression extends multi-output isotonic regression using optimal transport.

Develops nonstationary MOGP kernels for better performance.

Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making in real-world applications. Inspired by big data, the 4Vs characteristics of multi-output imposes a set of challenges to multi-output learn…

DeepICMGP surrogate models multiple outputs efficiently.

A scalable MOGP model with stochastic variational inference for many outputs.

Safe active learning for multi-output Gaussian processes reduces data acquisition costs and ensures safety.

This study compares multivariate vs univariate machine learning for multi-output regression.

Optimistic bounds for multi-output learning using self-bounding Lipschitz condition.

Paper introduces conformal prediction for reliable uncertainty quantification in landmark localization.

A recent novel extension of multi-output Gaussian processes handles heterogeneous outputs assuming that each output has its own likelihood function. It uses a vector-valued Gaussian process prior to jointly model all likelihoods' parameters as latent functions drawn from a Gaussian process with a linear model of coregi…

We construct flexible likelihoods for multi-output Gaussian process models that leverage neural networks as components. We make use of sparse variational inference methods to enable scalable approximate inference for the resulting class of models. An attractive feature of these models is that they can admit analytic pr…

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

Optimal algorithm for identifying best arm in stochastic linear bandits with fixed confidence.

Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other. A primary issue that arises in the context of MOD learning is that for any given input pattern there can be multiple correct output patterns. This changes the…

A new algorithm balances global reward and group constraints in federated multi-armed bandits.

Unified study of nine multi-output conformal methods with generalized scores.

Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued rando…

Enhances robustness of MOGP regression for multiple correlated outputs.

Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In thi…

Paper introduces vector-valued variation spaces for multi-output neural networks.

New algorithms tackle RKHS bandits with reduced complexity and improved performance.

Paper tackles LDP bandits learning with improved results and sub-linear regret.

First robust bandit algorithm for contextual bandits with sub-linear regret.

New algorithms improve linear bandit performance with low computation.

This paper tackles federated learning for automatic latent variable selection in multi-output Gaussian processes.

Study non-linear combinatorial bandits with polynomial rewards, finding significant differences from linear cases.

Unified approach for non-stationary linear bandits with dynamic regret.

Bayesian bandit algorithms with approximate inference improve regret bounds in stochastic linear bandits.

Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental assumption is that the output/group membership labels for all observations are known. This assumption is often violated in real application…

A new algorithm improves stochastic linear bandit performance using residual bootstrap.

Optimizes pure exploration in linear bandits with a new algorithm.

New approach reduces unconstrained linear bandits to simpler optimization problems.

This paper achieves optimal regret bounds for locally private linear contextual bandit.

Randomized exploration in linear bandits achieves optimal regret bounds.

The paper presents a novel approach to multi-output regression using probabilistic circuits.

New kernel models multi-output Gaussian processes accurately.

New algorithm for partially observable contexts in finance.

Algorithm balances online and offline data for linear bandits.

A new algorithm for conversational recommendation systems using dueling bandits in GLMs.

The paper tightens the regret rate for linear bandit problems.

Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling. The key problem with MOGPs is their computational scaling $O(n^3 p^3)$, which is cubic in the number of both inputs $n$ (e…

Multi-output Gaussian processes (MOGPs) are an extension of Gaussian Processes (GPs) for predicting multiple output variables (also called channels, tasks) simultaneously. In this paper we use the convolution theorem to design a new kernel for MOGPs, by modeling cross channel dependencies through cross convolution of t…