CoPhIK uses physics-informed Kriging to improve data-model convergence.
problem Improving data-model convergence in multifidelity problems.
method Physics-informed CoKriging (CoPhIK) combines PhIK and a parameterized GP to model discrepancies.
result CoPhIK reduces optimization cost and satisfies physical constraints up to an error bound.
Proposes CoDEAL for estimating heterogeneous treatment effects in panel data models.
problem Estimating heterogeneous treatment effects in causal panel data models with covariate effects.
method Covariate-Adjusted Deep Causal Learning (CoDEAL) integrating neural networks and autoencoders.
result Establishes theoretical guarantees and demonstrates compelling performance in simulations and real data.
GenHMM combines neural networks with HMM for sequential data modeling.
problem Sequential data modeling challenges.
method Integrates neural network generative models into HMM framework.
result GenHMM improves likelihood computation and tractability.
Study how neural networks learn from non-Gaussian data models.
problem Understanding neural network learning dynamics with non-Gaussian data.
method Developed a two-layer neural network with Hermite polynomial activations to control high-order cumulants.
result Neural networks progressively learn high-order cumulants after capturing low-order statistics.
Improved convergence of fixed-point methods using windowed Anderson acceleration.
problem Improving convergence of fixed-point methods for symmetric operators.
method Windowed Anderson acceleration for symmetric fixed-point iterations.
result Windowed Anderson acceleration improves convergence over standard fixed-point methods.
Two algorithms converge to dictionary learning with geometric rate for non-uniform data.
problem Dictionary learning for non-uniform data models.
method Derivation of convergence conditions for MOD and ODL.
result Both algorithms converge to the generating dictionary with geometric rate under certain conditions.
Study identifies negative data externalities affecting model performance on specific groups.
problem Negative data externalities on group performance in machine learning models.
method Characterized and detected data-model inefficiencies, focusing on specific types of externalities.
result Negative data externalities can lower model performance on specific sub-groups, even with larger datasets.
Sparsity-based models and techniques have been exploited in many signal processing and imaging applications. Data-driven methods based on dictionary and sparsifying transform learning enable learning rich image features from data, and can outperform analytical models. In particular, alternating optimization algorithms …
In this letter, we generalize the convolutional NMF by taking the β-divergence as the contrast function and present the correct multiplicative updates for its factors in closed form. The new updates unify the β-NMF and the convolutional NMF. We state why almost all of the existing updates are inexact and approximat…
A scalable framework uses Langevin sampling to approximate neural network models of evolving processes.
problem Uncertainty quantification in neural network models of dynamic systems.
method Flexible data model based on NODE, joint learning of data model and posterior parameters, Langevin sampling.
result Demonstrated performance on chemical reaction and material physics data, compared favorably to variational inference.
Historical (Stressed-) Value-at-Risk ((S)VAR), and Expected Shortfall (ES), are widely used risk measures in regulatory capital and Initial Margin, i.e. funding, computations. However, whilst the definitions of VAR and ES are unambiguous, they depend on input distributions that are data-cleaning- and Data-Model-depende…
Investigates the benefits of multi-head attention in Transformers, deriving convergence and generalization guarantees.
problem Underexplored dynamics of multi-head attention in Transformer training and generalization.
method Derives convergence and generalization guarantees for gradient-descent training of a multi-head self-attention model.
result Establishes conditions for initialization that ensure multi-head attention's realizability.
New method ISR improves domain generalization with provable guarantees.
problem Achieving reliable performance across unseen environments.
method Invariant-feature Subspace Recovery (ISR) algorithms.
result ISR can achieve provable domain generalization with fewer training environments.
Paper improves SOMs for non-Euclidean data modeling.
problem Traditional SOMs assume Euclidean data, limiting their applicability.
method Introduces topology-related extensions to traditional SOM algorithm.
result Improves SOMs for non-Euclidean data, enhancing data modeling.
FGPR uses averaging and SGD for federated GP regression, excelling in personalization and multi-fidelity modeling.
problem Privacy-preserving multi-fidelity data modeling and personalization.
method Federated Gaussian process framework with averaging and SGD for local computations.
result FGPR converges to a critical point of the full log-likelihood function, excels in personalization and multi-fidelity modeling.
Paper develops an online learning algorithm for functional data models.
problem Recovering slope functions or predictors in functional data models.
method Online regularized learning algorithm in reproducing kernel Hilbert spaces with polynomially decaying step-size.
result Established fast convergence rates for estimation error without capacity assumption.
The abstract discusses how humans use visualizations in machine learning.
problem The reliance on human involvement in AI systems and analytics.
method Review of seven steps in the ML process and different visualization techniques.
result Different visualizations are used at various stages of the ML process.
Responding to discussions on missing data models.
problem Clarifying causal and counterfactual views in missing data models.
method Summarizing and responding to comments from discussants.
result Summarized main contributions and addressed each discussion.
New algorithm recovers model coefficients and supports from noisy data.
problem Simultaneous estimation and support recovery in linear models with Gaussian noise.
method Projection-based algorithm for STG regularized minimization problem, proving convergence and support recovery guarantees.
result New algorithm outperforms existing methods in support recovery for various data setups.
New algorithm resists Byzantine attacks in distributed SGD for heterogeneous data.
problem Byzantine attacks in distributed SGD for heterogeneous data.
method Polynomial-time outlier-filtering for robust mean estimation, new matrix concentration result.
result Tolerates up to 25% Byzantine workers and achieves optimal convergence rates.
New insights into when benign overfitting occurs in linear and classification tasks.
problem Understanding when benign overfitting happens in linear and classification models.
method Analysis of a generic data model and comparison of predictors (minimum-norm interpolating and max-margin).
result The minimum-norm interpolating predictor is biased towards an inconsistent solution, preventing benign overfitting in linear regression.
New algorithm identifies missing data models using directed acyclic graphs.
problem Identifying missing data distributions represented by DAGs.
method Proposed a new algorithm that generalizes ID algorithm for causal inference.
result Significantly generalizes types of manipulations for identification.
MOGPTK simplifies multi-channel data modeling with Gaussian processes.
problem Modeling multi-channel data efficiently and accurately.
method Python package with TensorFlow backend, supporting various GP kernels and parameter initialization strategies.
result Enables GPU-accelerated training and comprehensive GP modeling pipeline.
Our article considers the class of recently developed stochastic models that combine claims payments and incurred losses information into a coherent reserving methodology. In particular, we develop a family of Heirarchical Bayesian Paid-Incurred-Claims models, combining the claims reserving models of Hertig et al. (198…
This paper analyzes and improves EM algorithms for large datasets.
problem Scaling EM algorithm to large datasets.
method Incremental and stochastic EM algorithms, variance reduction.
result Established non-asymptotic convergence bounds for global convergence.
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the source-specific data models are complex, or if the number of sources is very large. To facilitate computation, we propose…
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…
Efficient tensor decomposition for count data models achieves near-optimal multiway analysis.
problem Efficient tensor decomposition for count data models.
method Rank-constrained maximum-likelihood estimator for tensor decomposition.
result Achieves multiway analysis with variance matching Cramér-Rao Lower Bound up to constants and logarithmic factors.
Proposed SMO algorithm for OC-SVM+ significantly outperforms non-sequential algorithms.
problem One-class SVM with privileged information
method Sequential Minimal Optimization (SMO) algorithm
result Finite-time convergence established
New methods for estimating complex causal effects in econometrics.
problem Estimating causal parameters in short panel data models using nested nonparametric instrumental variable regression.
method Introducing techniques to limit ill-posedness in nested NPIV, providing explicit mean square rates and efficient inference.
result Explicit mean square rates for nested NPIV and efficient inference for causal parameters.
Breiman's data analysis dichotomy is outdated, offering a third approach: mechanistic models.
problem Data analysis dichotomy between data modelers and algorithmic modelers.
method Interpolating between simple interpretable models and flexible function approximations using mechanistic models.
result Flexible, interpretable, and scientifically-informed hybrids can provide accurate and robust predictions.
In Divide & Recombine (D&R), big data are divided into subsets, each analytic method is applied to subsets, and the outputs are recombined. This enables deep analysis and practical computational performance. An innovate D\&R procedure is proposed to compute likelihood functions of data-model (DM) parameters for big dat…
ecpc R-package improves high-dimensional prediction with co-data.
problem High-dimensional prediction with more variables than samples.
method Adaptive ridge penalised models with co-data, including continuous co-data.
result Improved variable selection and prediction performance.
Transformers learn unseen tasks via prompts without fine-tuning.
problem Understanding how transformers learn unseen tasks without additional fine-tuning.
method Structured data model, gradient descent, two-phase convergence analysis.
result Transformers can learn linear function classes via in-context learning.
Deep signature/log-signature FBSDE algorithm improves accuracy and training time.
problem Solving FBSDEs with state and path dependent features.
method Incorporates deep signature/log-signature transformation into RNN model.
result Improves accuracy and training time compared to existing methods.
A new HL-SVR approach handles unequal sample sizes in SVR for engineering data modeling.
problem SVR assumes equal sample sizes, but unequal sizes are common in engineering.
method HL-SVR combines low-level SVR for larger samples and high-level SVR for smaller samples.
result HL-SVR produces more accurate predictions than conventional SVR.
Commentary on Rashomon Effect complicating model selection.
problem Many models equally predict data; hard to draw conclusions.
method Connections to recent ML literature exploring implications.
result Grasping Rashomon Effect can foster collaboration.
PoPPy simplifies point process modeling and analysis.
problem Efficient modeling and analysis of sequential data.
method Flexible design and efficient learning of point process models.
result PoPPy enables large-scale point process analysis, simulation, and prediction.
Study analyzes Bayesian inference algorithms using dynamical functional approach.
problem Analysis of approximate inference algorithms for large Gaussian latent variable models.
method Dynamical functional approach to model nontrivial dependencies and obtain exact effective stochastic process.
result Closed-form expressions for the rate of convergence are derived and validated.
Leo Breiman's Rashomon Effect and Occam Dilemma are re-evaluated in the context of modern machine learning.
problem The tradeoff between model complexity and accuracy in machine learning.
method Modern perspective on Breiman's arguments using current computational capabilities.
result Algorithmic models can be accurate without being complex, nullifying the Occam Dilemma.
High-dimensional data models, often with low sample size, abound in many interdisciplinary studies, genomics and large biological systems being most noteworthy. The conventional assumption of multinormality or linearity of regression may not be plausible for such models which are likely to be statistically complex due …
Weak supervision challenges black-box models, suggesting fusion of modeling cultures.
problem Challenges of strong supervision in achieving accurate predictions.
method Integrating data modeling into algorithmic modeling for weak supervision.
result Integration of data modeling culture improves model stability and accuracy.
TriTPP models enable faster and more flexible event data modeling.
problem Inflexibility and slow sampling in traditional TPP models.
method Triangular Maps and Normalizing Flows for parallel sampling and likelihood computation.
result TriTPP models achieve orders of magnitude faster sampling while maintaining flexibility.
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of…
A new distributed method speeds up sparse model training.
problem Efficiently training models with massive samples and high-dimensional features.
method Distributed Dynamic Safe Screening (DDSS) method for sparsity regularized models.
result Achieves linear convergence rate and eliminates almost all inactive features.
A faster Bayesian method for estimating spatial count data models.
problem Bayesian estimation of spatial count data models is computationally expensive and slow.
method Derive a Variational Bayes (VB) method for posterior inference in negative binomial models with spatial dependence.
result The VB method is up to 50 times faster than MCMC and offers similar accuracy.
Generative model controls heterophily in graph signals.
problem Controlling heterophily in graph signals for better model effectiveness.
method Combines graphon-based generator with spectral filtering of Gaussian node features.
result Establishes theoretical guarantees for heterophily control and convergence.
R package xtdml uses DML for panel data models with fixed effects.
problem Estimating structural parameters in panel data models with fixed effects.
method Combines machine learning with statistical estimation for inference.
result Demonstrates improved performance in learning nuisance functions.