Probabilistic ML improves healthcare data analysis.
problem Insufficient understanding and incomplete data in healthcare.
method Examination of probabilistic machine learning models for healthcare challenges.
result Probabilistic models enhance healthcare data analysis and model building.
Discusses new probabilistic morphisms and geometric methods in machine and statistical learning.
problem Addressing challenges in statistical, machine, and manifold learning.
method Introduces category of probabilistic morphisms and geometric methods.
result New insights and applications in various learning fields.
Probabilistic models can handle causal inference without special tools.
problem Confusion over necessary tools for causal inference.
method Demonstrated through concrete examples that causal questions can be answered using standard probabilistic models.
result Causal questions can be addressed using standard probabilistic modelling and inference.
Study introduces TeMoP model for better stock market predictions.
problem Decreasing prediction errors and robustness across datasets in machine learning models.
method Probabilistic multiple lag order model based on trend encoding.
result TeMoP model outperforms machine learning models in accuracy and stability across different stock indexes.
Paper quantifies uncertainty in probabilistic models using Gaussian Processes.
problem Assessing reliability of probabilistic machine learning predictions.
method Systematic framework for estimating epistemic and aleatoric uncertainty, using Gaussian Processes and Monte Carlo sampling.
result Effective approach for quantifying prediction confidence in probabilistic models.
Pymc-learn is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. It is inspired by scikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that…
Transfer learning improves sparse, interpretable probabilistic classification.
problem Sparse and interpretable models in transfer learning.
method Two transfer learning extensions integrated into sparse and interpretable probabilistic classification vector machine.
result Transfer learning extensions improve sparsity and performance.
Probabilistic programming is a powerful abstraction for statistical machine learning. Applying static analysis methods to probabilistic programs could serve to optimize the learning process, automatically verify properties of models, and improve the programming interface for users. This field of static analysis for pro…
Extends PCVM for multi-class classification with improved accuracy.
problem Lack of probabilistic outputs and contradictory predictions in multi-class classification.
method Proposes mPCVM with two learning algorithms: top-down and bottom-up.
result Superior performance, especially with many classes, validated on synthetic and benchmark data.
Paper introduces PRMs to learn non-Markovian stochastic rewards for reinforcement learning.
problem Lack of structured representation for non-Markovian stochastic rewards in reinforcement learning.
method Introduces probabilistic reward machines (PRMs) and presents an algorithm to learn them from decision processes.
result Algorithm proves correct and convergent for learning PRMs from decision processes.
PGBM creates probabilistic predictions efficiently.
problem Creating probabilistic predictions for large-scale data.
method Approximates leaf weights as random variables, learns moments via stochastic tree ensemble update equations.
result PGBM offers significant speedup and accuracy improvements over existing methods.
A deep learning method for probabilistic weather forecasting.
problem Probabilistic forecasting of weather.
method Two chained machine-learning steps: dimension reduction and density estimation using normalizing flows.
result The method produces accurate conditional forecast distributions for weather.
Develops probabilistic safety regions for scalable classifiers.
problem Minimizing misclassification errors in supervised classification.
method Introduces probabilistic safety regions and scalable classifiers.
result Probabilistic certifications for classifier performance.
The paper presents a probabilistic framework for SPD matrices in machine learning.
problem Machine learning on SPD matrices is fragmented; this paper aims to unify it.
method Unified probabilistic framework using Gaussian distributions and Bayes classifiers.
result Different SPD machine learning tools can be reinterpreted and extended using Gaussian distributions.
New framework uses conformal predictions for robust, scalable machine learning classification.
problem Developing robust and reliable machine learning models for classification.
method Introducing scalable classifiers linked to statistical order theory and probabilistic learning theory, defining a score function and conformal safety set.
result Demonstrated practical implications in cybersecurity for identifying DNS tunneling attacks.
This research simplifies verification of machine learning systems using reparameterization.
problem Reduce or eliminate serious bugs in machine learning systems.
method Use proof assistants to construct machine-checked proofs of correctness, leveraging reparameterization to handle probabilistic claims.
result Demonstrates broad applicability of reparameterization to verify different types of machine learning systems.
A new probabilistic mixup framework improves deep learning generalization.
problem Improving deep learning generalization performance.
method Introduces a novel probabilistic mixup framework for conditional density estimation.
result Empirical results show benefits over existing mixup variants.
New method uses backward SDEs for deep learning uncertainty.
problem Uncertainty quantification in deep learning models.
method Probabilistic machine learning with stochastic neural networks and stochastic optimal control.
result Effectiveness validated through numerical experiments.
PVI combines federated learning and variational inference for probabilistic model training.
problem Federated learning's lack of probabilistic model uncertainty estimation.
method Partitioned variational inference (PVI) framework for federated probabilistic model training.
result PVI unifies fragmented literature and demonstrates effectiveness in various federated settings.
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuni…
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in the…
We present a simple theoretical framework, and corresponding practical procedures, for comparing probabilistic models on real data in a traditional machine learning setting. This framework is based on the theory of proper scoring rules, but requires only basic algebra and probability theory to understand and verify. Th…
Paper proposes a unified framework for evaluating calibration of probabilistic models.
problem Evaluation of calibration for general probabilistic predictive models.
method Unified framework for calibration evaluation and tests for any probabilistic model.
result Generalization and reformulation of existing measures and tests.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
PNNs model aleatoric uncertainty in scientific machine learning with high accuracy.
problem Aleatoric uncertainty in scientific systems with unequal variance.
method Developed a probabilistic distance metric to optimize PNN architecture and used it in material science applications.
result PNNs yield remarkably accurate output mean estimates and high correlation in predicted intervals.
A new neural network model MDRBM improves noise-robustness in classification.
problem Improving noise-robustness in classification problems.
method Stacking a probabilistic-ELM layer on a discriminative restricted Boltzmann machine (DRBM).
result MDRBM outperforms other models, especially in noise-robustness.
New method stabilizes probabilistic ODE solvers for high accuracy.
problem Numerical instability in high-order ODE solvers.
method Accurate initialisation, coordinate change preconditioner, square-root implementation.
result Probabilistic ODE solvers can now achieve high order (up to 11) with stability.
Generative model for morphisms in free categories learns from wiring diagrams.
problem Learning and generating morphisms in free monoidal categories.
method Probabilistic generative model using variational inference and maximum likelihood.
result The model achieves competitive performance on the Omniglot dataset.
Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel methods, random forests, deep learning aka neural networks - being employed as a basis for decision making processes, it is crucial to underst…
The paper tackles fairness in machine learning by modeling latent unbiased labels.
problem Ensuring fairness in machine learning systems that use biased data.
method Explicitly models a latent variable representing a hidden, unbiased label to achieve demographic parity.
result The latent variable approach successfully retrieves fair labels from biased data.
A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution. The key challenge is to approximat…
Graph-EFM models weather uncertainty with graph-based ensembles.
problem Accurately capturing forecast uncertainty in chaotic weather.
method Flexible latent-variable formulation with hierarchical graph construction.
result Graph-EFM ensembles achieve equivalent or lower errors than deterministic models.
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data s…
Proposes a cost-sensitive method to generate probabilistic SVM outputs.
problem Generating probabilistic SVM outputs efficiently and cost-effectively.
method Cost-sensitive ensemble SVM with bootstrap probability estimation.
result Improves performance on imbalanced datasets and outperforms benchmarks.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
Study compares deterministic and probabilistic ML for precise AM component dimensions.
problem Accurately estimate dimensions of additively manufactured parts with variability.
method Employed models integrating continuous and categorical factors, tested deterministic and probabilistic ML methods.
result Gaussian Process Regression and Bayesian Neural Networks provide strong predictive performance and uncertainty quantification.
This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understandi…
Alzheimer's disease is a major cause of dementia. Its diagnosis requires accurate biomarkers that are sensitive to disease stages. In this respect, we regard probabilistic classification as a method of designing a probabilistic biomarker for disease staging. Probabilistic biomarkers naturally support the interpretation…
The paper presents a machine learning framework to combine weather forecasts from multiple models.
problem Combining forecasts from different NWP models with varying biases and limitations.
method Three-stage framework using Quantile Regression Forests and quantile averaging.
result The framework generates well-calibrated probabilistic weather forecasts suitable for decision support.
Optimizes active learning for machine learning models with Bayesian approach.
problem Efficiently allocate labeling resources to train machine learning models.
method Directly optimizes misclassification error using a Bayesian approach with conjugate prior.
result Superior performance compared to state-of-the-art selection strategies.
Paper shows how gradient concentration helps in learning from inexact data.
problem Learning from inexact and stochastic training data.
method Combines probabilistic gradient concentration with inexact optimization techniques.
result Derives sharp test error guarantees for learning.
AI can learn true probabilities if data and assumptions align.
problem Understanding when AI models can accurately represent true objective probabilities.
method Proved conditions under which AI can learn true probabilities.
result Conditions for learning true probabilities are identified.
S4 learns new self-supervision automatically, improving accuracy with less human effort.
problem Lack of direct supervision in machine learning.
method Combines deep learning and probabilistic logic to automatically generate and verify new self-supervision.
result S4 can automatically propose accurate self-supervision, matching supervised methods with less human effort.
New method classifies manifold-valued data using Riemannian geometry.
problem Classifying data on curved Riemannian manifolds.
method Probabilistic Learning Vector Quantization on Symmetric Positive Definite Matrices.
result The method outperforms traditional Euclidean methods on manifold-valued data.
Simplified explanation of DDPMs for machine learning.
problem Complexity in explaining DDPMs and missing rationale.
method Six simple steps to explain DDPMs.
result Clear rationale for each step of DDPM formulation.
The choice of constellations largely affects the performance of communication systems. When designing constellations, both the locations and probability of occurrence of the points can be optimized. These approaches are referred to as geometric and probabilistic shaping, respectively. Usually, the geometry of the const…
COLEP improves robustness of conformal prediction via probabilistic circuits.
problem Adversarial perturbations can undermine the coverage guarantees of conformal prediction.
method COLEP uses probabilistic circuits to learn and reason about different semantic concepts, providing certifiable coverage guarantees.
result COLEP achieves higher prediction coverage and accuracy than a single model, especially with non-trivial knowledge models.
This review covers predictive uncertainty estimation in machine learning.
problem Improving the communication of uncertainty in machine learning predictions.
method A comprehensive review of probabilistic prediction methods from early statistical models to recent machine learning algorithms.
result The review highlights the importance of consistent scoring functions and proper scoring rules for assessing probabilistic predictions.