Extends multi-output Gaussian processes for heterogeneous outputs.
problem Handling multiple correlated outputs with varying likelihoods.
method Vector-valued Gaussian process prior, linear model of coregionalisation, tractable variational bounds.
result Model performs well on synthetic and real datasets.
Improved inference for heterogeneous multi-output Gaussian processes using natural gradient optimization.
problem Challenges in adaptive gradient optimization for multi-output Gaussian processes.
method Introducing a fully natural gradient scheme to overcome optimization issues.
result Better local optima solutions and higher test performance rates compared to adaptive gradient methods.
Paper extends multi-task Gaussian Cox processes for heterogeneous tasks.
problem Modeling multiple heterogeneous correlated tasks jointly.
method Data augmentation and mean-field approximation for non-conjugate Bayesian inference.
result Demonstrates improved performance and inference on synthetic and real data.
Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.
problem Accurately learning system dynamics in complex, multi-channel systems with nonlinear and noisy data.
method Formulates system as Gaussian process state-space models (GPSSMs), introduces heterogeneous multi-output kernel, and develops recursive inference framework.
result Matches SOTA offline GPSSMs in accuracy with 1/100 runtime, and outperforms SOTA online GPSSMs by 70% in accuracy under noise with 1/20 runtime.
Two strategies for training network classifiers with feature heterogeneity.
problem Training network classifiers with agents having varying feature sizes and unreliable local decisions.
method Promotes global and local smoothing of classifier outputs.
result Output smoothing makes network classifier dynamics more complex, requiring regularization of parameters.
LESS combines local predictors for subsets to learn from heterogeneous input-output pairs.
problem Learning from heterogeneous input-output pairs in populations with varied behavior.
method LESS algorithm: generates subsets, trains local predictors, combines them.
result LESS is highly competitive compared to state-of-the-art methods.
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.
Proposes a flexible deep learning model for complex distributions.
problem Complex shapes, strong skews, and multiple modes in output variable distributions.
method Uncountable Mixture of Asymmetric Laplacians (UMAL) deep learning framework.
result UMAL can estimate heterogeneous distributions without strong assumptions.
Algorithm mines environment assumptions for cyber-physical systems.
problem Modeling and verifying complex cyber-physical systems.
method Supervised learning to mine STL formulas for input signals.
result Algorithm learns both the structure and constants of STL formulas.
New Random Forest variants estimate heterogeneous treatment effects using Wasserstein distances.
problem Estimating heterogeneous treatment effects in complex situations.
method Proposes natural variants of Random Forests using Wasserstein distances.
result Natural variants of Random Forests are well-suited for estimating conditional distributions.
New method handles metric space data for regression.
problem Handling heterogeneous data like curves, images, and shapes.
method Fréchet trees and Fréchet random forests for metric space valued regression.
result Random forests can now handle complex data types.
Combines classifiers from different types to improve ensemble accuracy.
problem Improving ensemble accuracy by combining classifiers of different types.
method Builds heterogeneous ensembles by pooling classifiers from multiple homogeneous ensembles, using cross-validation or out-of-bag data for optimal composition.
result Optimal heterogeneous ensemble compositions can be determined using cross-validation or out-of-bag data.
We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…
The paper investigates how dataset quality and heterogeneity affect model confidence in machine learning.
problem Understanding how dataset quality and heterogeneity impact model confidence in machine learning.
method The study uses theoretical explanations and experimental demonstrations to investigate the effects of dataset size, label noise, and class heterogeneity on model confidence.
result Label noise reduces model confidence, while reduced dataset size increases it, and class heterogeneity leads to inconsistent confidence across classes.
Method learns cluster structure while estimating heterogeneous graphical models.
problem Joint estimation of multiple graphical models from heterogeneous and high-dimensional data.
method High-dimensional ECM algorithm with joint graphical lasso penalty.
result Demonstrated superior performance and new insights in Glioblastoma cancer dataset.
A post-hoc framework improves model performance by calibrating different feature spaces.
problem Improving AUC performance on binary classification tasks for overconfident models.
method Identifies heterogeneous partitions of the feature space and applies post-hoc calibration techniques to each partition.
result Theoretical optimality of the framework for any model, demonstrated on deep neural networks.
Deep neural network solves complex groundwater contaminant source identification.
problem Identifying groundwater contaminant sources in highly heterogeneous media.
method Deep autoregressive neural network surrogate for forward model, ILUES for inversion.
result Deep autoregressive neural network provides accurate approximation for high-dimensional model.
Federated learning improves by training central model with client model outputs.
problem Direct averaging of client models is limited in FL.
method Ensemble distillation for model fusion.
result Central model trained faster with fewer communication rounds.
A new approach combines attributes and sequences for better item recommendations.
problem Difficulty in leveraging attribute information due to heterogeneity and sparseness.
method Heterogeneous Attribute Recurrent Neural Networks (HA-RNN) that incorporates heterogeneous attributes and captures sequential dependencies.
result Significant improvements over state-of-the-art models in item recommendation.
GeMA learns latent manifolds to benchmark complex systems.
problem Benchmarking complex systems like rail networks and economies with classical methods.
method Geometric Manifold Analysis (GeMA) using a productivity-manifold variational autoencoder (ProMan-VAE).
result GeMA provides more nuanced efficiency evaluations in complex systems.
New method for decentralized learning from diverse clients.
problem Achieving high-performance global model from diverse client models.
method Decentralized Learning via Adaptive Distillation (DLAD).
result Demonstrates effectiveness of DLAD on multiple public datasets.
Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category…
Study on self-consuming generative models with diverse human curation, focusing on convergence and stability.
problem Analyzing self-consuming generative models with heterogeneous human curation.
method Investigates the asymptotic behavior of retraining dynamics using nonlinear Perron--Frobenius theory and Banach contraction mapping.
result Improves convergence results and provides stability and non-stability analyses for the model.
Deep neural network models for efficient uncertainty quantification in multiphase flow.
problem Uncertainty quantification of dynamic multiphase flow in heterogeneous media due to high dimensionality and discontinuities.
method Convolutional encoder-decoder neural network for image-to-image regression, incorporating time as an input.
result Accurate surrogate model capable of characterizing spatio-temporal pressure and saturation fields with limited training data.
Improved linear regression for diverse data batches.
problem Learning from multiple heterogeneous data sources.
method Gradient-based algorithm for different input distributions.
result Significant reduction in the number of required batches and sample size.
MetaDVFS uses device and application metadata to improve DVFS efficiency.
problem Improving energy efficiency in mobile platforms with diverse applications and hardware.
method Formulates DVFS as a multi-task reinforcement learning problem and introduces MetaDVFS, leveraging metadata for knowledge transfer.
result MetaDVFS achieves up to 26% improvement in Quality of Experience and up to 17% improvement in Performance-Power Ratio.
Proposes a framework to fuse heterogeneous data sources for better modeling.
problem Heterogeneous data sources with different input parameter spaces.
method Input mapping calibration (IMC) and latent variable Gaussian process (LVGP).
result Improved predictive accuracy over single source models.
Much of the analysis of economic growth has focused on the study of aggregate output. Here, we deviate from this tradition and look instead at the structure of output embodied in the network connecting countries to the products that they export.We characterize this network using four structural features: the negative r…
DSL estimates heterogeneous treatment effects over time in survival settings.
problem Complicated by right censoring and time-varying treatment effects.
method Deep survival learner (DSL) for estimating CATEs over a clinically relevant time spectrum.
result DSL reveals heterogeneity in perioperative chemotherapy effects over time.
DAG-FM discovers causal relationships from heterogeneous data.
problem Challenges in causal discovery from heterogeneous causal mechanisms.
method DAG-FM uses two specialized Transformer-based sub-modules and a robust tabular interaction block to model complex row-column interactions.
result DAG-FM achieves state-of-the-art performance on synthetic and real-world datasets.
Deep neural networks learn spatially heterogeneous patterns from input data.
problem Understanding the hidden layers of deep neural networks.
method Statistical mechanics approach with a teacher-student setting.
result Learning by deep neural networks is spatially heterogeneous, with central regions less correlated.
This paper investigates how economic shocks propagate and amplify through the input-output network connecting industrial sectors in developed economies. We study alternative models of diffusion on networks and we calibrate them using input-output data on real-world inter-sectoral dependencies for several European count…
New algorithm learns from distributed, heterogeneous data without shuffling.
problem Efficiently learning from distributed, heterogeneous data in exascale simulations.
method Block-random gradient descent algorithm for distributed, heterogeneous data.
result Algorithm enables in situ learning without pre-shuffling data.
DVWU framework improves model performance by considering data value heterogeneity.
problem Existing machine unlearning algorithms ignore data value heterogeneity, potentially degrading model performance.
method Data Value-Weighted Unlearning (DVWU) framework that integrates data values into the unlearning process.
result DVWU achieves superior predictive performance and robustness compared to conventional unlearning approaches.
WP-SGD optimizes SGD for unevenly distributed data in distributed systems.
problem Inequalities in node performance and data consumption in parallel SGD.
method Combines weighted model parameters from different nodes to compensate for performance inconsistencies.
result WP-SGD significantly outperforms traditional parallel SGD in systems with uneven workloads.
Identifying behavior that is relatively invariant under different conditions is a challenging task in far-from-equilibrium complex systems. As an example of how the existence of a semi-invariant signature can be masked by the heterogeneity in the properties of the components comprising such systems, we consider the exc…
One-round FL method improves robustness and reduces communication rounds.
problem Making predictions robust and reducing FL communication rounds in heterogeneous data.
method Bayesian predictive space aggregation of client posteriors in one round.
result One-round FL method outperforms other techniques on heterogeneous settings.
Neural network solves inverse problem in multiscale mechanics.
problem Identifying elastic properties of random materials.
method Artificial neural networks trained on processed databases.
result Robust identification method validated with synthetic and real data.
Extended Chiarella model explains coexistence of trend and value in financial markets.
problem Coexistence and interaction of trend and value anomalies in financial markets.
method Extended Chiarella model with noise traders and fundamentalists, calibrated using Bayesian filtering.
result Extended model reproduces non-monotonic relation between past trends and future returns, leading to bimodal mispricing distribution.
Splat Regression Models use mixtures of bump functions to approximate complex data.
problem Approximating complex data with high interpretability and accuracy.
method Model outputs are mixtures of heterogeneous and anisotropic bump functions (splats) weighted by output vectors. Fitting splat models reduces to optimization over mixing measures using Wasserstein-Fisher-Rao gradient flows.
result Unified theoretical framework for Gaussian Splatting and flexible approach for diverse problems.
OTSS learns personalized decision weights from logged decisions and outputs.
problem Learning context-specific decision weights from logged decisions and outputs.
method Output-targeted soft-segmentation model that deploys personalized decision-ready weight vectors.
result OTSS achieves the lowest mean regret in benchmark settings.
Improves search performance by transferring knowledge from recommender system.
problem Cold start and feedback loop problems in search retrieval.
method Zero-Shot Heterogeneous Transfer Learning framework.
result Significant improvements in relevance and user interactions over production system.
Optimal multiscale learning of linear operators
problem Statistical and computational limits of learning bounded linear operators between Sobolev spaces
method Reformulate as an infinite-dimensional matrix regression problem with heterogeneous multiscale structure
result Establish minimax rates and construct a finite-resolution blockwise least-squares estimator attaining these rates
DDGroup identifies subgroups with uniform linear relationships.
problem Heterogeneous effects of covariates in linear models.
method Data-driven method to identify subgroups with uniform linear relationships.
result DDGroup can discover subgroups with improved performance.
A novel double-space tensor-product RKHS framework for hybrid uncertainty sensitivity analysis.
problem Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses.
method A novel double-space tensor-product RKHS framework for sensitivity analysis under hybrid uncertainty.
result Concurrent double Möbius inversion orthogonally decomposes global dependence measure into pure aleatory effects, pure epistemic effects, and their interaction contributions.
Hierarchical nucleation patterns emerge in deep neural network layers.
problem Understanding the generation of meaningful representations in deep neural networks.
method Analysis of the probability density of ImageNet dataset across hidden layers.
result Density peaks in subsequent layers mirror the semantic hierarchy of concepts, resembling nucleation process.
Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.
problem Need for uncertainty quantification with multiple confidence levels in diverse applications.
method Online optimization perspective to enforce nestedness of prediction sets while controlling quantile estimation error.
result Achieves stable coverage across all levels, strictly nested prediction sets, and improved efficiency.
S-MTGPR improves normative modeling of neuroimaging data.
problem Normative modeling of neuroimaging data without spatial covariance structure.
method Scalable multi-task Gaussian process regression (S-MTGPR) with low-rank approximation and Kronecker product.
result S-MTGPR provides higher sensitivity in novelty detection scenarios.