Adapts data analysis for growing data, improving generalization guarantees.
problem Challenges of overfitting and statistical validity in adaptive workflows with growing data.
method Generalizes adaptive analysis on dynamic data, incorporating time-varying empirical accuracy bounds and mechanisms.
result First generalization bounds for adaptive analysis on dynamic data, matching prior works' improvement over data splitting.
Traditional statistical analysis requires that the analysis process and data are independent. By contrast, the new field of adaptive data analysis hopes to understand and provide algorithms and accuracy guarantees for research as it is commonly performed in practice, as an iterative process of interacting repeatedly wi…
Noise addition prevents overfitting in adaptive data analysis.
problem Overfitting in repeated use of a data sample via adaptively chosen queries.
method Simple noise addition algorithms and differential privacy-based analysis.
result Noise-addition algorithms provide variance-dependent guarantees for unbounded queries.
In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data. Consequentially, the so-called source classifier, trained on the available labelled data, deteriorates on the test, or target, data. Domain adaptive classifiers aim to combat this probl…
VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.
problem Efficiently estimating student skill levels with uncertainty for adaptive testing.
method VarFA uses variational inference to extend factor analysis models for educational data.
result VarFA efficiently handles large datasets and produces uncertainty estimates.
In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are often randomized in order to reduce overfitting for such adaptively chosen queries. In this paper, we propose a minimax framework for adaptive…
Improves survival analysis across multiple domains with machine learning.
problem Adapting survival analysis to new or rare illness types with limited labeled data.
method Introduces a new survival metric and discrepancy measure for censored data, enabling domain adaptation.
result Superb performance on target domains, better treatment recommendations, and interpretable weight matrix.
An online learning framework for survival analysis with real-time adaptation.
problem Real-time adaptation to dynamic environments and censored data in survival analysis.
method Online Newton Step (ONS) for optimal second order online convex optimization.
result Logarithmic stochastic regret for ONS with adaptive aggregation method.
Generative model identifies temporal count data components with regime-dependent contributions.
problem Modeling temporal count data with regime-dependent dynamics.
method Generative framework combining regime-adaptive dynamics with Poisson log-normal emissions.
result Established identifiability of the model and revealed co-variation patterns and regime shifts.
In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional…
TabSurv adapts tabular neural networks for survival analysis.
problem Survival analysis on tabular data using deep learning methods.
method Adapts modern tabular architectures to survival analysis using Weibull distribution or non-parametric prediction. Optimizes SurvHL histogram loss function.
result TabSurv consistently outperforms classical and deep learning baselines on 10 real-world survival datasets.
A new method detects unknown classes and adapts to extra dimensions in high-dimensional classification.
problem Handling unknown classes and extra variables in high-dimensional classification.
method Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA) using an EM algorithm for model estimation.
result The method can adapt to unknown classes and extra dimensions in high-dimensional data.
Traditional statistical theory assumes that the analysis to be performed on a given data set is selected independently of the data themselves. This assumption breaks downs when data are re-used across analyses and the analysis to be performed at a given stage depends on the results of earlier stages. Such dependency ca…
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framework that we adapt to survival analysis on small data sets with missing values. In our model, the true health status appears as a set of late…
Proposes CC-NMDF for analyzing manifold-valued data.
problem Nonlinear structure in manifold-valued data requires new analysis methods.
method Curvature-corrected nonnegative manifold data factorization (CC-NMDF) with an iterative algorithm.
result Demonstrates CC-NMDF on real-world diffusion tensor MRI data.
Bayesian adaptive PCE method improves surrogate modeling and sensitivity analysis.
problem Lack of fully Bayesian PCE methods in statistics.
method Developed a novel fully Bayesian adaptive PCE method with R implementation.
result Bayesian adaptive PCE provides competitive performance for various UQ tasks.
TripleSurv improves survival analysis by ranking samples with time-adaptive adjustments.
problem Modeling censored time-to-event data with high accuracy and robustness.
method Introduces a time-adaptive coordinate loss function to rank samples and calibrate robustness.
result TripleSurv outperforms state-of-the-art methods on various survival datasets.
Proposes a regularization method for unsupervised domain adaptation that aligns predictions with target data's top singular vectors.
problem Domain adaptation challenges in high joint error scenarios.
method Regularizes classifier to align with unsupervised target data guided by label alignment property (LAP).
result The method improves performance in MNIST-USPS domain adaptation and cross-lingual sentiment analysis.
MILDA uses unlabelled data to compute LDA projections.
problem Training LDA models with unlabelled data.
method Minimal prior information to compute LDA projection vector.
result MILDA closely matches supervised LDA performance and adapts to non-stationary data.
A novel online framework for analyzing multidimensional functional data.
problem Analysis of multidimensional functional data streams poses significant challenges.
method Online functional principal component analysis using tensor product splines on a Stiefel manifold with Riemannian stochastic gradient descent.
result Efficient and scalable modeling of multidimensional functional data.
Study improves domain adaptation with limited labeled data.
problem Adapting a model to a target domain with few labeled samples.
method Discrepancy-based sample reweighting methods and learning algorithms.
result Improved solutions for domain adaptation problems.
Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference however, adaptive data collection induces memory and correlation in the samples, and poses significant challenge. We consider the high-dimensional linear regression, where the s…
ACS is an interactive framework for model-free selection with guaranteed error control.
problem Model-free selection with rigorous error control.
method Adaptive conformal selection with human-in-the-loop data exploration and new information incorporation.
result ACS provides concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating new data.
Proposes a flexible feature allocation model for sparse factor analysis.
problem Sparse data and rigid assumptions in traditional exploratory tools.
method Adaptive latent feature sharing with control over feature sparsity.
result Derives a novel adaptive Factor analysis (aFA) and aPPCA for flexible dimensionality reduction.
This work proves convergence of adaptive resampling for random Fourier features.
problem Sampling Fourier frequencies well for high-dimensional data.
method Data adaptive resampling of Fourier frequencies, asymptotically optimal.
result Proves convergence of adaptive resampling method for regression and classification problems.
AdapTable adapts tabular models to shifts without source data, improving HELOC performance.
problem Distribution shifts in tabular data threaten model performance.
method Shift-aware uncertainty calibrator and label distribution handler.
result Up to 16% improvement on HELOC dataset.
Adapts bandit algorithms for online survival analysis under Cox PH model.
problem Online survival analysis challenges in a bandit framework.
method Adapts three bandit algorithms to balance exploration and exploitation.
result Demonstrates sublinear regret bounds and effective learning of treatment policies.
New federated learning methods improve model performance on non-IID data.
problem Improving model performance on non-IID decentralized data.
method Proposed Federated AGMs using adaptive gradient methods with first-order and second-order momenta.
result The proposed Federated AGMs converge to a first-order stationary point under non-IID and unbalanced data settings for nonconvex optimization.
A new federated learning method reduces communication costs and improves adaptivity.
problem Large communication overhead and lack of adaptivity in federated learning.
method FedCAMS: A novel communication-efficient adaptive federated learning method with theoretical guarantees.
result FedCAMS achieves the same convergence rate as non-compressed federated learning methods.
SP-SPCA improves sparse PCA by adaptively adjusting variable penalties, enhancing interpretability and stability.
problem Poor interpretability and variable redundancy in PCA for high-dimensional data.
method Introduces a single equilibrium parameter to adaptively adjust variable penalties in the L2 regularization framework.
result Consistently outperforms standard sparse PCA methods in identifying sparse loading patterns and preserving cumulative variance.
Adaptive data analysis is frequently criticized for its pessimistic generalization guarantees. The source of these pessimistic bounds is a model that permits arbitrary, possibly adversarial analysts that optimally use information to bias results. While being a central issue in the field, still lacking are notions of na…
Study examines AutoML adaptation to evolving data.
problem Understanding and improving AutoML performance with concept drift.
method 6 concept drift adaptation strategies evaluated on various AutoML approaches.
result Robust AutoML techniques can be developed to handle concept drift.
BAWS adapts window size for financial risk forecasting.
problem Adaptive selection of look-back window for financial risk modeling.
method Data-driven online learning method using bootstrap-based adaptive window selection (BAWS).
result BAWS improves risk forecasting, especially in data with structural changes.
KZImputer improves time series data quality with adaptive imputation for short to medium-sized gaps.
problem Missing data in time series analysis.
method Adaptive imputation method for univariate time series with tailored strategies for different gap positions.
result KZImputer achieves strong performance, especially for high missingness rates and high-sparsity regimes.
Adaptive tensor modeling preserves continuity in multidimensional data.
problem Discretization of continuous multidimensional data loses important information.
method Functional Tucker decomposition (FTD) with RKHS modeling.
result FTD enables adaptive and expressive tensor modeling.
Algorithm improves model performance on shifted concepts without retraining.
problem Improving model performance on shifted concepts with limited source data.
method Model consolidation of intermediate internal distributions after adaptation.
result Effective improvement in model performance on shifted concepts.
The bias of the sample means of the arms in multi-armed bandits is an important issue in adaptive data analysis that has recently received considerable attention in the literature. Existing results relate in precise ways the sign and magnitude of the bias to various sources of data adaptivity, but do not apply to the c…
A new method for tighter privacy loss accounting in adaptive analyses.
problem Ensuring individual privacy in adaptive analyses while staying within a privacy budget.
method A personalized privacy loss estimate and a Rényi differential privacy filter.
result Personalized privacy loss accounting can be practical and tighter than existing methods.
Entropy-based GP adaptive design improves failure probability estimation.
problem Limited accuracy in failure probability estimation due to model evaluation costs.
method Entropy-based Gaussian process (GP) adaptive design combined with multifidelity importance sampling (MFIS).
result More accurate failure probability estimates and higher confidence.
Machine learning systems must adapt to data distributions that evolve over time, in applications ranging from sensor networks and self-driving car perception modules to brain-machine interfaces. We consider gradual domain adaptation, where the goal is to adapt an initial classifier trained on a source domain given only…
FedDuA adapts global learning rate for federated learning.
problem Slow convergence in federated learning due to dataset and parameter space heterogeneity.
method FedDuA uses mirror descent to adaptively select global learning rate based on inter-client and coordinate-wise heterogeneity.
result FedDuA achieves minimax optimal convergence for convex objectives and outperforms baselines in various settings.
Algorithm estimates principal eigenvector with adaptive sensing, improving over non-adaptive methods.
problem Estimating principal eigenvector with limited scalar measurements.
method Compressed variant of Oja's algorithm using two adaptive measurements per sample.
result Convergence rate of O(λ1λ2d2/(Δ2t)) after t iterations, matching information-theoretic lower bound. This paper examines the role and efficiency of the non-convex loss functions for binary classification problems. In particular, we investigate how to design a simple and effective boosting algorithm that is robust to the outliers in the data. The analysis of the role of a particular non-convex loss for prediction accur…
New technique for multiple-source adaptation without density estimation.
problem Multiple-source adaptation problem.
method Discriminative technique that uses conditional probabilities from unlabeled data.
result Our technique outperforms previous generative solutions and other domain adaptation baselines.
Adaptive learning model forecasts financial prices using order book data.
problem Forecasting high-frequency financial time series with non-stationary data.
method Adaptive learning model based on order book data, with stationarity and non-stationarity considerations.
result The model outperforms top fixed models and improves forecasting accuracy.
Unified analysis of generalization and sample complexity for semi-supervised domain adaptation.
problem Theoretical foundations of domain adaptation remain underexplored, especially for modern approaches.
method Unified theoretical study of domain adaptation algorithms based on domain alignment, considering joint learning of feature transformations and shared classifiers in a semi-supervised setting.
result Unified theoretical analysis of domain adaptation algorithms, providing generalization bounds and sample complexity bounds for MMD and adversarial models.
Private algorithms adapt from public to private domains with minimal labeled data.
problem Adapting from a public source domain to a private target domain with few labeled data.
method Differentially private discrepancy minimization algorithms based on Frank-Wolfe and Mirror-Descent methods.
result Effective adaptation with strong generalization and privacy guarantees.