The high-dimensionality and volume of large scale multistream data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. This data, also categorized as big data, is becoming common in manufacturing plants. In this paper, we propose an integrated M\&D approach…
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48 results for “adaptive data integration”
Greedy PIG adapts integrated gradients for better feature attribution.
problem Interpreting deep learning model predictions.
method Unified discrete optimization framework for feature attribution and selection.
result Greedy PIG improves feature attribution on various tasks.
A new adaptive binarization technique using fuzzy integrals improves image quality.
problem Improving image thresholding quality.
method FLAT (Fuzzy Local Adaptive Thresholding) based on fuzzy integrals.
result The proposed FLAT method produces better image quality than traditional algorithms and neural networks.
learn2mix trains neural nets faster by adjusting class proportions dynamically.
problem Training neural nets efficiently with limited resources and imbalanced classes.
method Adaptive class proportion adjustment during training.
result Neural nets trained with learn2mix converge faster than static methods.
Data integration methods that analyze multiple sources of data simultaneously can often provide more holistic insights than can separate inquiries of each data source. Motivated by the advantages of data integration in the era of "big data", we investigate feature selection for high-dimensional multi-view data with mix…
This paper introduces a novel approach to robust surface reconstruction from photometric stereo normal vector maps that is particularly well-suited for reconstructing surfaces from noisy gradients. Specifically, we propose an adaptive dictionary learning based approach that attempts to simultaneously integrate the grad…
BAM integrates new data while selectively remembering past observations.
problem Slow adaptation and convergence to incorrect parameter values in non-stationary environments.
method Bayes' theorem with adaptive memory selection.
result BAM generalizes and demonstrates continuous adaptation in changing environments.
A new data-adaptive prior stabilizes kernel learning in operators.
problem Learning kernels in operators from data is ill-posed due to nonlocal dependence.
method Introduces a data-adaptive prior to stabilize the Bayesian posterior mean.
result The data-adaptive prior achieves a stable posterior with small noise limits.
This paper provides an existence-and-uniqueness theorem characterizing the stochastic integral with respect to a Wiener process. The integral is represented as a mapping from the space of measurable and adapted pathwise locally integrable processes to the space of continuous adapted processes. It is characterized in te…
Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a …
AdaWISH improves efficiency of discrete integration queries.
problem Efficiently solving discrete integration in high-dimensional spaces.
method Adaptive quantile queries to reduce query count.
result AdaWISH achieves the same approximation guarantee with fewer queries.
PAIN network improves imputation for mixed datasets.
problem Missing data in diverse scientific domains.
method Dynamic adaptive imputation using statistical methods, random forests, and autoencoders.
result PAIN outperforms traditional imputation methods in preserving data distributions.
Nash integrates covariate-specific side info into sparse regression via neural networks.
problem Sparse linear regression struggles with covariates exhibiting structure or coming from heterogeneous sources.
method Neural Adaptive Shrinkage (Nash) framework that integrates side information into sparse regression via neural networks. Uses split variational empirical Bayes algorithm.
result Nash improves accuracy and adaptability over existing methods in real data experiments.
Adapts IG for better feature attributions and robustness.
problem Reliability concerns in feature attributions for deep learning models.
method Adaptation of path-based feature attribution to Riemannian geometry of data manifolds.
result IG along geodesics generates more intuitive and robust explanations.
Combines control variates and adaptive importance sampling for Monte Carlo integration.
problem Improving Monte Carlo integration accuracy with control variates and adaptive sampling.
method A quadrature rule combining control variates and adaptive importance sampling.
result Non-asymptotic bound on the probabilistic error of the procedure.
Paper proposes adaptive parameter selection for KGD algorithms.
problem Improving parameter selection for kernel-based gradient descent.
method Integrates bias-variance analysis with splitting method, introduces empirical effective dimension.
result Adaptive parameter selection strategy achieves optimal generalization error bound.
UBMF tackles fault diagnosis in imbalanced industrial data with enhanced accuracy and adaptability.
problem Fault diagnosis challenges in imbalanced industrial data.
method Integrates four key modules: data perturbation, cross-task feature extraction, uncertainty-based filtering, and Bayesian meta-knowledge integration.
result Achieves an average improvement of 42.22% across ten diagnostic tasks.
Adapts manifold structure for better clustering performance.
problem Lack of consideration for local manifold structure in existing multiple kernel k-means methods.
method Adopts manifold adaptive kernel to integrate local manifold structure of kernels.
result Proposed method outperforms state-of-the-art methods.
A new method for learning function parameters in operators using data-adaptive RKHS.
problem Learning function parameters in operators with robustness to noise and numerical error.
method Data Adaptive RKHS Tikhonov Regularization (DARTR) method.
result DARTR leads to an accurate estimator robust to noise and numerical error, converging at a consistent rate as data refines.
This document investigates the integration of adaptive distinguishing sequences into the process of active automata learning (AAL). A novel AAL algorithm "ADT" (adaptive discrimination tree) is developed and presented. Since the submission of the original thesis, the presented algorithm has been integrated into LearnLi…
Study integrability of specific geometric structures on odd Courant algebroids.
problem Characterize integrability of B_n-generalized structures on odd exact Courant algebroids.
method Characterize integrability in terms of existence of adapted generalized connections.
result Describe affine spaces of adapted generalized connections for integrable structures.
Highly Adaptive Ridgestat.ML
HAR regression improves performance on small datasets.
problem Small datasets with complex functions.
method Data-adaptive kernel ridge regression using tensor-product spline basis.
result Achieves convergence rate for right-continuous functions.
Cross-regularization adapts model complexity during training.
problem Manual tuning of model complexity for overfitting prevention.
method Directly adapts regularization parameters through validation gradients during training.
result Organic emergence of architecture-specific regularization during training.
Adaptive financial dataflow system improves model robustness in dynamic markets.
problem Static historical data leads to poor performance in dynamic financial markets.
method Drift-aware dataflow system with adaptive control and optimization.
result Enhanced model robustness and improved risk-adjusted returns.
MKL-based models outperform complex multi-omics integrative approaches.
problem Integrating diverse omics data sources.
method Supervised multiple kernel learning with different kernel fusion strategies.
result MKL-based models outperform more complex architectures.
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mappingcs.LG
Enhances ocean floor mapping with adaptive uncertainty estimates.
problem Inaccurate bathymetric data for precise ocean modeling.
method Block-based conformal prediction with VQ-VAE architecture.
result Significant improvements in reconstruction quality and uncertainty estimation reliability.
Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensionality (where non-adaptive quadrature is not competitive). Its key ingredient is an acquisition function that changes as a function of previou…
Adaptive HMC improves sampling efficiency by optimizing mass matrix.
problem Inefficient HMC performance due to mass matrix choice.
method Gradient-based adaptation of mass matrix to maximize proposal entropy.
result Adaptation method outperforms HMC variants by optimizing mass matrix.
Paper fine-tunes LLMs for financial tasks using data fusion.
problem Improving LLMs for financial analysis tasks.
method Fine-tuned Llama3-8B and Mistral-7B using PEFT and LoRA, combined datasets for data fusion.
result Enhanced model performance across financial tasks.
Gradient flow autoencoder improves data efficiency over traditional autoencoders.
problem Sub-optimal latent space representations in autoencoders.
method Gradient flow through ODE with adaptive step size for optimization.
result Gradient flow autoencoder achieves higher data efficiency.
For every adapted, càglàd process (strategy) and typical càdlàg price paths whose jumps satisfy some mild growth condition we define integral as a limit of simple integrals.
ART improves transfer learning performance with robust theory and methods.
problem Improving performance of primary tasks using auxiliary data.
method Adaptive Robust Transfer Learning (ART) pipeline with theoretical guarantees.
result ART provides a provable theoretical guarantee for adaptive transfer and robustness.
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimesstat.ME
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Paper proposes new density estimators for high-dimensional data.
problem Prohibitive computational cost and slow convergence rate in high-dimensional density estimation.
method Adaptive hyperbolic cross density estimators in mixed smooth Sobolev spaces.
result Proposed estimators do not suffer curse of dimensionality under Integral Probability Metrics.
Proposes a neural density estimator that adapts to low-dimensional structures and integrates into generative models.
problem Challenges in implementing neural density estimators and lack of theoretical understanding.
method Structure-agnostic neural density estimator that is easy to implement and provably adaptive.
result Adapts to low-dimensional structures and achieves faster convergence rates.
Deep learning for integrating diverse clinical measurements.
problem Combining data from different measurement instruments in longitudinal clinical registries.
method Domain adaptation using deep learning for mapping items from different instruments.
result Domain adaptation can recover latent trajectories even with limited data and misalignment.
Adaptive rule improves kernel-based gradient descent performance.
problem Improving convergence speed of kernel-based gradient descent algorithms.
method Empirical effective dimension for stopping rule, learning theory analysis, integral operator approach.
result Optimal learning rates and iteration bounds for KGD with adaptive stopping rule.
Paper examines stability of Bayesian posterior measures using integral probability metrics.
problem Stability of Bayesian inference in large-scale inverse problems.
method New families of integral probability metrics for likelihood and prior perturbations.
result Constructs new stability results for Bayesian posterior measures.
Paper describes integrable structure of Hitchin moduli spaces.
problem Integrable structure of Hitchin moduli spaces.
method Explicit parameterizations and Separation of Variables method.
result Clear analogy with Drinfeld's geometric Langlands correspondence.
BMTI method estimates densities without bins, outperforming traditional estimators.
problem Nonparametric, robust, and data-efficient density estimation in high-dimensional spaces.
method BMTI integrates log-density differences between neighboring points, weighted by uncertainties, using a maximum-likelihood formulation.
result BMTI reconstructs smooth profiles in high-dimensional spaces, outperforming traditional estimators.
Paper tackles robust domain adaptation without target domain data.
problem Learning domain invariant representations without target domain data.
method Integrates deep autoencoder and causal structure learning into a unified model.
result CAE learns causal representations using only source domain data.
Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selectionstat.ME
Guided adaptive shrinkage uses co-data to improve feature selection in genomic studies.
problem Feature selection challenges in high-dimensional genomics data, especially in clinical settings.
method Guided adaptive shrinkage methods that use co-data to adapt shrinkage parameters.
result Improves feature selection in genomic studies, demonstrated through comparisons and examples.
To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method. We conduct several conventional sequential estimation procedures separately, and properly integrate their results while maintaining the desired statistical properties. A…
End-to-end PGL framework tackles open-set domain shift.
problem Real-world domain shift with unknown additional classes.
method Episodic training in graph neural network with adversarial learning.
result Guarantees tighter upper bound of target error.
AdamCB optimizes neural network training by adaptively selecting samples.
problem Inefficient convergence due to unequal influence of different data samples.
method Integrates combinatorial bandit techniques into Adam to adaptively select samples.
result AdamCB achieves faster convergence and better performance than existing methods.
iGecco+ integrates multi-view data for better clustering.
problem Discovering common group structure in mixed multi-view data.
method Integrative Generalized Convex Clustering Optimization (iGecco) with adaptive feature selection.
result iGecco+ achieves superior clustering performance on high-dimensional mixed multi-view data.
PrAda-GAN improves synthetic data generation under differential privacy.
problem Generating synthetic data under differential privacy with marginal-based methods.
method Sequential generator architecture integrating GAN and marginal-based approaches, with adaptive regularization of Bayes network structure.
result PrAda-GAN outperforms existing methods in privacy-utility trade-off on synthetic and real-world datasets.