Bayesian optimisation outperforms standard ML-II in small samples.
problem Standard ML-II fails in small-sample trials.
method Adopting fully Bayesian optimisation (FBO) as an alternative.
result FBO is more robust and practical than ML-II.
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48 results for “Bayesian ML”
SIP corrects model bias in Bayesian ML.
problem Model selection biases predictions in Bayesian ML.
method Sparse Implicit Processes (SIP) for flexible, trainable predictions.
result SIP provides better predictive distributions than initial models.
New method quantifies uncertainty for near-optimal ML algorithms.
problem Uncertainty quantification for near-Bayes optimal ML algorithms.
method Developed a martingale posterior to recover Bayesian posterior from ML algorithms.
result Proved practical uncertainty quantification method applicable to general ML algorithms.
Fair Bayesian Optimizationstat.ML
Bayesian optimization framework for fair machine learning models.
problem Bias in machine learning models and lack of adaptability of fairness techniques.
method General constrained Bayesian optimization framework.
result BO can optimize ML models for fairness without model-specific constraints.
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand…
Bayesian methods improve adversarial machine learning robustness.
problem Improving machine learning systems' security against adversarial manipulation.
method Integrating Bayesian perspectives to relax unrealistic assumptions in game-theoretic models.
result Bayesian approach provides more robust inferences about opponent's beliefs and interests.
This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypot…
Bayesian networks and ML improve COVID-19 symptom classification and severity analysis.
problem Understanding the relationship between COVID-19 symptoms and demographic variables.
method Bayesian network structure learning followed by unsupervised clustering and demographic symptom identification.
result 99.99% testing accuracy compared to 41.15% for a heuristic method.
Bayesian framework improves ML classification models' uncertainty estimates.
problem Ensuring trustworthy AI predictions with explicit uncertainty quantification.
method Proposes a Bayesian framework for generative ML classification models that accounts for input measurement uncertainty.
result The BQDA model outperforms other models in terms of interpretability, explicit uncertainty modeling, and computational efficiency.
Develops robust ML systems for predictive uncertainties and adversarial examples.
problem Uncertainties and vulnerabilities in ML predictions and adversarial examples.
method Bayesian structural time series models, SG-MCMC, SVGD, Markov chain samplers, reinforcement learning.
result Improved robustness and efficiency in Bayesian inference and adversarial machine learning.
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Marketsecon.EM
Bayesian model predicts mid-price dynamics in financial markets.
problem Challenges in predicting financial markets using traditional methods.
method Bayesian bilinear neural network with temporal attention.
result Feasibility and advantages of Bayesian deep-learning approach.
Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete e…
On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniquesstat.ML
Combines physics-based ML with hierarchical Bayesian techniques for better model performance.
problem Lack of physical knowledge in black-box machine learning models.
method Embeds physics-based models into Gaussian Process mean function and uses kernel machines to characterize discrepancies.
result Improved model performance under blind conditions through integration of physics-based knowledge.
Optimizes VAE hyperparameters for efficient training and manifold discovery.
problem Efficiently optimizing hyperparameters in VAEs for complex data.
method Latent Bayesian Optimization (zBO) for hyperparameter trajectory optimization.
result Demonstrated improved performance in finding joint rotationally invariant representations.
Unified taxonomy for ML uncertainty in physics, validated.
problem Uncertainty quantification in machine learning for physics.
method Unified taxonomy, principled validation tools.
result Illustrated validation tools with examples.
This paper uses dynamical systems to analyze and ensure convergence of the Bayesian EM algorithm.
problem Ensuring convergence of the Bayesian EM algorithm in incomplete-data scenarios.
method Applying Lyapunov stability theory to discrete-time dynamical systems.
result Conditions for convergence and potential for fast convergence of MAP-EM are established.
Maximum Entropy competes with Maximum Likelihoodphysics.data-an
MAXENT method outperforms ML in sparse data with specific prior correlations.
problem Evaluating MAXENT method's validity limits and comparing it with ML.
method Bayesian decision theory, Dirichlet density, KL distance, regularized maximum likelihood.
result MAXENT can outperform ML in sparse data with specific prior correlations.
Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper intro…
Bayesian neural networks predict stress fields and uncertainty in materials.
problem Uncertainty in stress field predictions for complex materials.
method Modified Bayesian U-net architecture with three inference algorithms.
result High accuracy predictions and interpretable uncertainty estimates.
COVID-19 mortality analysis from soft-data multivariate curve regression and machine learningstat.ML
Study uses regression and ML for COVID-19 mortality forecasting.
problem Forecasting COVID-19 mortality during the first wave in Spain.
method Cyclical curve log-regression, multivariate time series spatial residual correlation analysis, Bayesian approach, machine learning.
result Empirical analysis shows ML regression models perform better than traditional methods.
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
New CNN approach reduces overconfidence in object classification predictions.
problem Overconfident predictions from deep models, especially SoftMax layer.
method Introduces CNN probabilistic approach using Logit layer for Bayesian inference.
result Proposed approach shows promising performance compared to SoftMax.
Bayesian model compares ML algorithms on various datasets.
problem Comparing multiple machine learning algorithms across multiple datasets.
method Bayesian Bradley-Terry model, defining regions of practical equivalence (ROPE).
result Allows nuanced statements and ROPE definitions for algorithm comparison.
As machine learning is applied more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" - brittle, ad hoc ML systems. To address thes…
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline c…
IFT reformulates AI and ML tasks using field theory.
problem Signal reconstruction and non-parametric inverse problems.
method Reformulate inference in IFT as GNN training.
result IFT-based GNNs can operate without pre-training.
Study on reducing forgetting in neural networks using compression theory.
problem Catastrophic forgetting in neural networks.
method Defined forgetting as increased description lengths, compared variational posterior approaches to prequential coding methods.
result Proposed a new continual learning method combining ML plug-in and Bayesian mixture codes.
Study builds ML models to predict fuel properties accurately.
problem Accurate prediction of liquid fuel properties over a wide range of conditions.
method Used Gaussian Processes and probabilistic conditional generative learning to train ML models on fuel density data.
result ML models can predict fuel properties accurately across various pressure and temperature conditions.
Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques togeth…
We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for their machine learning needs. With ease.ml, a user defines the high-level schema of a machine learning application and submits the task via a W…
Framework improves health by planning actionable treatment processes.
problem Developing objective treatment processes in clinical settings.
method Surrogate Bayesian model combined with ML for personalized health improvement.
result Computed treatment processes are actionable and consistent with clinical knowledge.
We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which …
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
problem Lack of reliable inference for ML-discovered nonlinearities and interactions in epidemiological data.
method Combines Bayesian sparse regression, tree ensembles, and Shapley values.
result Valid uncertainty quantification for feature effects at the individual level.
POCA optimizes hyperparameters with adaptive allocation for faster convergence.
problem Optimizing hyperparameters for machine learning models.
method Adaptive allocation of computational budget using Bayesian sampling.
result POCA finds strong configurations faster than its competitors.
A new framework tackles CASH problem with alternating optimization and Rising Bandits.
problem Efficiently solving the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem.
method Alternating optimization framework using BO for HPO and Rising Bandits for algorithm selection.
result Demonstrated superiority over competitive baselines in extensive experiments.
A new method improves active learning for large batch sizes.
problem Challenges in scaling Bayesian active learning to large batch sizes.
method Derives Partial Batch Label Sampling (ParBaLS) for EPIG algorithm.
result ParBaLS EPIG outperforms top- selection and BatchBALD.
Gaussian processes help in modeling complex, nonlinear relationships in signal processing.
problem Modeling complex, nonlinear relationships in signal processing.
method Sequential inference for Gaussian processes.
result Gaussian processes enable efficient and accurate modeling of complex relationships.
Bayesian neural networks improve uncertainty in data-driven VFMs for oil and gas wells.
problem Uncertainty and robustness in data-driven VFMs for oil and gas wells.
method Bayesian neural networks with variational inference for uncertainty quantification.
result Variational inference provides more robust predictions on future data.
Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size is not enough to provide a robust ML fit for a relatively large data feature dimension . Deploying a massively-parallel ML analysis of generic classificatio…
πBO augments BO with user beliefs for better hyperparameter optimization.
problem BO ignores user beliefs, reducing its appeal to practitioners.
method Proposes BO, an acquisition function that incorporates user-provided prior beliefs.
result BO outperforms competing approaches and deep learning tasks.
Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.
problem Understanding and quantifying uncertainty in ML and DL for risk-sensitive applications.
method Structured review of literature, categorizing uncertainty, assessing uncertainty quantification techniques.
result Broadened scope of uncertainty discussion and updated DL uncertainty quantification methods.
TFB simplifies Bayesian LLM uncertainty estimation without extra training.
problem Estimating uncertainty in LLM responses remains challenging.
method Training-Free Bayesianization (TFB) that transforms low-rank adapters into Bayesian ones without additional training.
result TFB achieves superior uncertainty estimation and generalization compared to existing methods.
ML surrogates speed up Bayesian inverse problem solving.
problem Infer source location from noisy acoustic wave equation data.
method Use neural network as surrogate for PDE, apply MCMC to posterior.
result Accurately infers source location from noisy data.
Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for data…
Proposes ML methods for robust price-sensitivity estimation in dynamic pricing.
problem Estimating price elasticities robustly in the presence of feature-dependent sensitivity.
method Poisson semi-parametric model with two-stage estimation: first-stage ML for observed purchases, second-stage Bayesian GLM for price-sensitivity.
result Reduces estimation error in price-sensitivity parameters from 25% to 4%.
Exact learning improves naive Bayes classifier performance for small samples.
problem Improving naive Bayes classifier performance with small sample sizes.
method Proposes an exact learning augmented naive Bayes classifier (ANB) that ensures a class variable with no parents.
result The proposed ANB method outperforms other methods in comparison experiments.
Bayesian Predictive Coding improves deep learning uncertainty quantification.
problem Limitations of maximum a posteriori and maximum likelihood estimates in predictive coding.
method Developed Bayesian Predictive Coding (BPC) that estimates a posterior distribution over network parameters.
result BPC offers comparable uncertainty quantification to existing methods in Bayesian deep learning and improves convergence properties.
On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learningcs.LG
Clarifies challenges in machine learning uncertainty quantification.
problem Inconsistent terminology and diverse technical requirements for trustworthy uncertainties.
method Examines estimation targets, uncertainty constructs, and problematic mappings.
result Advocates for alignment between intent and implementation in UQ.