arXiv research

Study improves Bayesian optimisation with ensemble transfer learning.

Bayesian methods improve learning from related domains.

Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data. Learning reliable networks from limited data is difficult, therefore transfer learning can improve the robustness …

When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extens…

Unified view on GP transfer learning for Bayesian optimization.

Bayesian optimization improves with transfer learning for aircraft design.

A new method learns priors for Bayesian optimisation to improve performance.

MPHD transfers knowledge across different domains for Bayesian optimization.

Paper uses transfer learning and Bayesian optimization to reduce DNA sequence design experiments.

Proposes a multi-fidelity machine learning strategy integrating low-fidelity deterministic and high-fidelity Bayesian models.

Bayesian method transfers knowledge between brain tumor datasets.

Bayesian method mitigates negative transfer in unknown source data.

Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer…

We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The …

Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information…

SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.

Bayesian transfer learning improves predictive performance with limited source data.

Bayesian model transfers knowledge across different engineering fleets.

Transfer learning framework for fragility modeling under domain shift and class imbalance

Paper introduces a new method for error estimation in classification tasks with limited data.

New method improves adversarial transferability from Bayesian neural networks.

Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The…

Bayesian meta-learning improves health prediction models across similar diseases.

Bayesian method improves multivariate periodontal outcome modeling.

A cost-efficient method for hyperparameter tuning using multi-fidelity Bayesian optimization.

New PAC-Bayesian framework for flexible meta-learning.

FP-BMA improves generalization by encouraging flat posteriors in Bayesian Model Averaging.

Paper improves feature selection accuracy using transfer learning.

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning challenge from a causal theory perspective. Our agent is endowed with two basic yet general theories for transfer learning: (i) a task shares a c…

Enhances PCE surrogates using transfer learning for expensive simulations.

Unified framework for transfer learning regression without extra cost.

When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bayes framework. As fully Bayesian computations can be difficult and computationally demanding, it is often desirable to use posterior point es…

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typically designed to be universal optimizers and, therefore, often suboptimal for specific tasks. We propose a novel transfer learning method …

Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution over policies in a Bayesian deep reinforcement learning setup to propose a transfer strategy. Recent works have shown to induce diversity i…

CNP improves few-shot learning for docking scores in molecular datasets.

Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between disea…

Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, largely depends on accuracy and correctness of these priors. PAC-Bayesian methods overcome this problem by providing bounds that hold regardless …

CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.

Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task. Common approaches have taken the form of meta-learning: learning to learn on the new problem given the old. Following the reco…

Graph-coupled causal Bayesian optimization transfers information across related interventions.

HyperBO+ pre-trains a universal prior for Bayesian optimization across different domains.

DeltaBO accelerates Bayesian optimization with theoretical guarantees.

Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties like uncertainty estimation, model robustness and regularisation. We want to discuss here the application of Bayesian models to knowledge sha…

FunBO uses LLMs to discover effective acquisition functions for Bayesian optimization.

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic complexity is cubic in the number of evaluations. Hence, GP-based BO cannot leverage large amounts of past or related function evaluations, for e…

Meta-models predict model hyperparameters for NDT experiments.

Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in isolation, there has been recent interest in speeding up BO by transferring knowledge across multiple related black-box functions. In this …