Bayesian neural networks incorporate domain knowledge through variational inference.
problem Specifying priors for Bayesian neural networks that capture domain knowledge is challenging.
method Proposes a framework for integrating domain knowledge into BNN priors through variational inference.
result BNNs with proposed domain knowledge priors outperform those with standard priors, achieving better predictive performance.
Framework evaluates the impact of prior knowledge in deep learning models.
problem Mitigating data-driven model shortcomings like data dependence and generalization ability.
method Model-agnostic framework inspired by interpretable machine learning, assessing data volume and estimation range effects.
result Complex relationship between data and knowledge, including dependence, synergistic, and substitution effects.
Develops a simulation-based method to translate expert knowledge into prior distributions for Bayesian models.
problem Effective incorporation of expert knowledge into prior distributions for diverse model structures.
method Simulation-based stochastic gradient descent to learn hyperparameters of parametric priors from expert knowledge.
result Method is adaptable to various elicitation techniques and independent of model structure.
Two methods factor out prior knowledge from low-dimensional embeddings.
problem Visualizing data without considering background knowledge.
method JEDI for tSNE and CONFETTI for any embedding.
result Embeddings reveal meaningful structure hidden by prior knowledge.
Improves transparency and incorporates prior knowledge in Gaussian Process models.
problem Challenges in understanding and expressing prior assumptions in complex Bayesian models.
method Introduces self-explaining variational posterior distributions for Gaussian Processes.
result Allows incorporation of both general and feature-specific prior knowledge.
Framework incorporates prior knowledge into Bayesian models for data streams.
problem Effective use of prior knowledge in learning Bayesian models from streaming data.
method Proposes a novel framework that subsumes existing models for time-series data.
result Framework outperforms existing methods with a large margin.
Proposes a method to integrate prior knowledge into trajectory prediction models.
problem Improving accuracy and robustness in trajectory prediction models.
method Continual learning approach that allows integration of arbitrary prior knowledge and probabilistic predictions.
result Outperforms non-informed and informed learning methods, using half as many observation examples.
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
Cluster-DAGs improve causal discovery with prior knowledge.
problem Finding cause-effect relationships from high-dimensional data.
method Cluster-DAGs as prior knowledge framework, modified constraint-based algorithms Cluster-PC and Cluster-FCI.
result Cluster-PC and Cluster-FCI outperform baselines without prior knowledge.
Adversarial meta-learning computes Gamma-minimax estimators for vague prior knowledge.
problem Estimating parameters with vague prior knowledge.
method Adversarial meta-learning algorithms for Gamma-minimax estimators.
result Convergence guarantees and neural network class for selection.
There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior knowledge include (a) beliefs about which hypotheses are null, modeled by non-uniform prior weights; (b) differing importances of hypotheses, m…
Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only availab…
Bayesian priors improve neural network performance on weak signals.
problem Challenges in encoding domain knowledge for weak signals in neural networks.
method Proposed a new joint prior over local scale parameters for feature sparsity and signal-to-noise ratio, optimized with Stein gradient.
result Improved prediction accuracy on various datasets, including genetics applications with weak and sparse signals.
Bayesian framework integrates prior and data knowledge for nonlinear dynamical systems.
problem Fusing diverse prior knowledge with data for accurate model learning.
method General-purpose Bayesian inference and learning framework combining explicit and implicit prior knowledge.
result Efficient parameter marginalization and closed-form densities for online and offline inference.
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths …
Paper proposes methods to discover causal models with unobserved variables.
problem Discovering causal relationships in data with unobserved variables.
method Two methods leveraging prior knowledge for causal discovery in CAM-UV models.
result Accuracy of causal discovery improves with more prior knowledge.
K-priors enable quick adaptation with minimal retraining.
problem Machine learning models struggle to adapt to changes efficiently.
method Combines weight and function-space priors to reconstruct past gradients.
result Adaptation with K-priors achieves similar performance to full retraining with less data.
FAB-PPI uses prior knowledge to improve prediction-powered inference.
problem Improving statistical inference with machine learning predictions.
method Informing PPI with prior knowledge on prediction quality.
result FAB-PPI improves inference accuracy and confidence intervals.
Paper presents a robust transfer learning method for active level set estimation.
problem Efficiently identifying regions of a black-box function with limited function evaluations.
method Incorporates prior knowledge from a related function while locally adapting it.
result The method achieves better convergence of level sets compared to standard transfer learning.
b-LOAD extends local causal discovery with prior knowledge, improving causal effect estimation.
problem Local causal discovery struggles in data-scarce settings due to uncertainty and incomplete neighborhoods.
method b-LOAD incorporates prior knowledge directly into local structure learning, using Meek's rules to refine discovery.
result b-LOAD refines the admissible equivalence class and enlarges identifiable causal queries, improving causal effect estimation.
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 …
Prior domain knowledge can greatly help to learn generative models. However, it is often too costly to hard-code prior knowledge as a specific model architecture, so we often have to use general-purpose models. In this paper, we propose a method to incorporate prior knowledge of feature relations into the learning of g…
Proposes a method to identify causal relationships using background knowledge.
problem Identifying causal relationships in the presence of background knowledge.
method Learning local structure using all types of causal background knowledge (direct, non-ancestral, ancestral). Criteria for identifying causal relationships based on local structure.
result Effective and efficient method for local structure learning and causal relationship identification.
Transformer-based method for causal discovery with prior knowledge integration.
problem Complex nonlinear dependencies and spurious correlations in time series data.
method Multi-layer Transformer forecaster with gradient-based causal structure extraction and attention masking for prior knowledge integration.
result Significant improvement in causal discovery and causal lag estimation compared to state-of-the-art methods.
Q-SAVI model improves drug discovery accuracy with prior knowledge of chemical space.
problem Challenges in drug discovery due to covariate shift and limited labeled data.
method Probabilistic model with domain-informed prior distributions over functions.
result Q-SAVI outperforms state-of-the-art techniques in predictive accuracy and calibration.
Symbolic regression improved by incorporating prior knowledge.
problem Insufficient guidance from training data alone for model accuracy.
method Multi-objective symbolic regression combining training data and prior constraints.
result Models that fit training data well and comply with prior knowledge.
One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations in the so…
Bayesian Cox model identifies biomarkers from multi-omics data.
problem Produce interpretable survival prognosis from multi-omics data.
method Penalized semiparametric Bayesian Cox model with graph-structured selection priors.
result Model identifies new biomarkers and improves survival prediction.
Optimizes expensive experiments by incorporating expert knowledge.
problem Expensive experiments require minimizing the number of trials.
method Bayesian optimization with posterior sampling of expert knowledge.
result Demonstrates significant efficiency gains in experiments and hyperparameter tuning.
This paper distills financial indicators into neural networks to reduce noise and improve accuracy.
problem Reduction of non-stationary noise in financial time series data.
method Co-distillation of smaller networks trained on indicators to transfer prior knowledge and reduce overfitting.
result The proposed method outperforms traditional methods in terms of speed and accuracy on real financial datasets.
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern…
In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, fina…
Enhances neural networks with prior knowledge through a composite kernel.
problem Lack of effective methods to incorporate prior knowledge into neural networks.
method Integrates a composite kernel combining a neural network kernel and a GP kernel for modeling known properties.
result Demonstrates superior performance and flexibility of the Implicit Composite Kernel (ICK) on synthetic and real-world data.
New LSTM scheme incorporates prior knowledge and measurement uncertainties.
problem Overfitting and insufficient data for accurate time-dependent solutions.
method Sparse Bayesian training algorithm for automatic connection determination.
result Less prone to overfitting, smaller data set required for satisfying accuracy.
ARGUE combines expert networks for anomaly detection.
problem Anomaly detection without labeled data.
method Gated mixture-of-experts architecture combining expert networks.
result Prior knowledge about normal data distribution is valuable.
Human ability at solving complex tasks is helped by priors on object and event semantics of their environment. This paper investigates the use of similar prior knowledge for transfer learning in Reinforcement Learning agents. In particular, the paper proposes to use a first-order-logic language grounded in deep neural …
New Gromov-Wasserstein metric controls rigidity and incorporates prior knowledge.
problem Inflexible Gromov-Wasserstein distance and lack of feature alignment.
method Augmented Gromov-Wasserstein distance with feature alignments and prior knowledge.
result Improved performance in single-cell multi-omic alignment and transfer learning.
Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning models. When modeling complex physical systems, both domain knowledge and data provide necessary information about the system. In this paper…
This paper improves Gaussian process predictions by integrating prior knowledge.
problem Gaussian processes lack predictive power when prior information is ignored.
method Derive mean and covariance functions from previous data using weighted sums of basis functions.
result Integrating prior knowledge significantly increases look-ahead time and accuracy.
The goal of imitation learning is for an apprentice to learn how to behave in a stochastic environment by observing a mentor demonstrating the correct behavior. Accurate prior knowledge about the correct behavior can reduce the need for demonstrations from the mentor. We present a novel approach to encoding prior knowl…
NTS-NOTEARS learns DBNs from time-series data with prior knowledge.
problem Learning dynamic Bayesian networks from time-series data with nonlinear and lagged relationships.
method Uses 1D CNNs to model DBNs, incorporating prior knowledge as constraints.
result Achieves state-of-the-art DAG structure quality compared to parametric and nonparametric methods.
Enhances model compression with multi-teacher knowledge distillation.
problem Uncertainty evaluation and diverse teacher expertise in model deployment.
method Bayesian inference and teacher-informed prior with entropy-based weighting.
result Improved predictive accuracy and robust uncertainty quantification.
Survey of integrating physics knowledge into machine learning models.
problem Mitigating data shortage and ensuring physical plausibility.
method Combining physics knowledge with machine learning models.
result Summarizes recent works in physics-informed machine learning.
A new method converts neural networks to function space for scalable sequential learning.
problem Challenges in gradient-based deep learning for sequential data.
method Dual parameterization of neural networks from weight to function space.
result Efficient scaling, knowledge retention, and new data incorporation.
Bayesian optimisation is improved by incorporating expert prior through space warping.
problem Cold start phase in expensive function optimisation.
method Prior distribution warps the search space around high probability regions of function optimum.
result Improves optimisation performance through acquisition agnostic approach.
We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network …
High-dimensional prediction is a challenging problem setting for traditional statistical models. Although regularization improves model performance in high dimensions, it does not sufficiently leverage knowledge on feature importances held by domain experts. As an alternative to standard regularization techniques, we p…
ColaBO accelerates optimization with user beliefs.
problem Optimizing expensive functions with limited prior knowledge.
method General Bayesian framework for incorporating user beliefs.
result Significant optimization acceleration with accurate prior information.