New method improves ensemble diversity and generalization.
problem Ensemble diversity does not guarantee practical generalization.
method Introduced a new diversity metric and training method for extrapolating differently on local data patches.
result Improves generalization and diversity in practical settings, especially under data limits and covariate shift.
This paper assesses GAN diversity using classification-based covariate shift.
problem Evaluating the diversity of GAN distributions is challenging.
method Developed tools to assess GAN diversity using a classification-based perspective.
result Popular GANs have significant problems reproducing training dataset properties.
CMA-ME combines CMA-ES and MAP-Elites for better quality and diversity in continuous domains.
problem Finding a diverse set of high-quality solutions in complex continuous domains.
method Combines CMA-ES self-adaptation with MAP-Elites archiving and mapping.
result CMA-ME outperforms MAP-Elites in both quality and diversity of solutions.
Improves low-shot learning with novel GAN for diverse example generation.
problem Overfitting and forgetting in small data settings.
method Covariance-Preserving Adversarial Augmentation Networks (CPGANs).
result Significant improvement on ImageNet benchmark.
New method stabilizes private LASSO for high-dimensional data with diverse covariate scales.
problem Privacy constraints and heterogeneity in covariate scales degrade LASSO stability and accuracy.
method Gram-based anisotropic objective perturbation to counteract covariate structure.
result Significantly improves convergence and statistical efficiency of private LASSO estimators.
DEN learns diverse tasks to generalize to unseen tasks.
problem Generalization from a diverse set of classification tasks with limited data.
method Three-block architecture: covariate transformation, distribution embedding, and classification.
result DEN outperforms existing methods in various synthetic and real tasks.
GANs mode collapse solved with Bures distance.
problem GANs mode collapse or mode dropping.
method Use Bures distance to match real and fake batch diversity in feature space.
result Diversity matching reduces mode collapse and improves sample quality.
Bayesian model fuses diverse microbiome data types.
problem Challenges in fusing different types of microbiome data.
method Flexible multinomial-Gaussian generative model with variational EM algorithm.
result Inferred latent variables provide common dimensionality reduction and predictive posterior distribution.
Novel Bayesian neural network method for robustness.
problem Adversarial robustness without online training.
method Distributes uncertainty across all inputs.
result Demonstrates robustness on benchmark datasets.
Tightens analysis for inferring latent community structure with node covariates.
problem Inferring latent community structure from graphs with node covariates.
method Information theoretic analysis combining graph and node covariates.
result Necessity of combining graph and node covariates for accurate inference.
PACE-GGM uses Gaussian mechanism for private covariance estimation.
problem Private estimation of covariance matrices in high dimensions.
method Data-adaptive selection of entries, Gaussian mechanism, maximum-entropy reconstruction.
result Consistent improvements in estimation error compared to Gaussian mechanism and baselines.
A new method finds diverse near-optimal portfolios using quality-diversity.
problem Optimizing financial portfolios with robustness to input parameter uncertainties.
method Quality-Diversity (QD) optimization using CVT-MAP-Elites algorithm.
result Diverse set of near-optimal portfolios identified.
We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of features (similar to orthogonal matching pursuit) or alternatively based on side information. We introduce our approach in the context of Bayes…
Neural network method estimates covariate-dependent graphical models with statistical guarantees.
problem Estimating graph structure from covariate-dependent data.
method Neural network approach that allows flexible functional dependency on covariates.
result Theoretical PAC guarantees for the method's performance.
Batch Active Learning uses derivative information for Gaussian Process regression.
problem Efficiently selecting data batches in Gaussian Process regression models.
method Proposes using the predictive covariance matrix to select data batches, exploiting full correlation.
result Demonstrates the effectiveness of incorporating derivative information across diverse applications.
Improved covariate shift handling with node-based Bayesian neural networks.
problem Improving generalization under covariate shift in neural networks.
method Introduced node-based Bayesian neural networks that learn latent noise variables to represent input corruptions.
result Node-based BNNs perform well under covariate shift due to input perturbations, improving uncertainty estimation and robustness.
We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse …
A simple greedy algorithm can be rate optimal in contextual bandits with sufficient randomness.
problem Sub-optimal greedy algorithms in contextual bandits without exploration.
method Proved greedy algorithms can be rate optimal under certain conditions and introduced Greedy-First.
result Standard bandit algorithms may unnecessarily explore, and a greedy algorithm can be rate optimal with positive probability.
Training task diversity improves ICL with linear attention.
problem Understanding the impact of training task diversity on in-context learning.
method Modeling training task vectors as a mixture of low-rank Gaussians.
result Our model explains why training with task diversity shortens the ICL plateau and achieves out-of-distribution generalization.
Novel Fréchet regression method handles errors-in-variables with low-rank covariates.
problem Regression with noisy and limited covariate data.
method Combines global Fréchet regression and principal component regression for low-rank structure.
result Improved efficiency and accuracy in high-dimensional and noisy data settings.
New method clusters high-dimensional data with anisotropic noise.
problem Clustering high-dimensional anisotropic mixtures with varying noise structures.
method Covariance Projected Spectral Clustering (COPO) method that projects data onto a low-dimensional space and reassigns clusters based on estimated covariances.
result COPO achieves minimax-optimal misclustering rates in Gaussian settings.
Chronos-2 forecasts multivariate and covariate data without task-specific training.
problem Limited applicability of existing time series forecasting models to real-world multivariate and covariate data.
method Chronos-2 uses a group attention mechanism for in-context learning across multiple time series.
result Chronos-2 achieves state-of-the-art performance across comprehensive benchmarks.
Optimization algorithm CoCo improves causal inference from diverse data.
problem Identifying true causal relationships from data with spurious associations.
method CoCo optimizes for causal inference using environments with invariant causal relationships.
result CoCo provides more accurate causal estimates and predictions.
New method uses graph to generalize bandedness for more covariance matrix estimation problems.
problem Estimating covariance matrices in high-dimensional settings with known graph structure.
method Develops convex regularizers based on graph-guided bandedness for covariance matrix estimation.
result Generalizes bandedness to broader applications than just time or 1D space data.
Improved Bayesian uncertainty quantification using variational bagging.
problem Inefficient and underestimating uncertainty in mean-field variational Bayes.
method Integrates bagging with variational Bayes for improved inference.
result Bagged variational posterior provides proper uncertainty quantification.
A new algorithm speeds up rerandomization for better experiment balance.
problem Achieving optimal covariate balance in randomized experiments.
method Metropolis-Hastings framework with sampling-importance resampling.
result PSRSRR achieves significant speedups while maintaining statistical guarantees.
Improved graph matching using covariates for network data integration.
problem Matching networks without unique identifiers.
method Two novel covariate-assisted seeded graph matching methods.
result Improved alignment accuracy through covariate information.
DPERC efficiently estimates covariance matrices for mixed data with missing values.
problem Estimating covariance matrices for datasets with missing values and mixed features.
method Direct Parameter Estimation for Randomly Missing Data with Categorical Features (DPERC).
result DPERC outperforms other methods in estimating covariance matrices for mixed data with missing values.
Safe-DRFS selects features robust to covariate shifts for reliable performance.
problem Feature selection fails in diverse deployment environments.
method Safe-DRFS extends safe screening to distributionally robust settings under covariate shift.
result Safe-DRFS identifies a feature subset encompassing optimal subsets across distribution shifts.
GLSKF improves tensor completion by capturing both global and local variations.
problem Tensor completion with missing entries, especially in data with spatial or temporal side information.
method Integrates smoothness-constrained low-rank factorization with a locally correlated residual process.
result GLSKF achieves superior performance and scalability on real-world datasets.
FoRDE uses input gradients to improve neural network ensembles.
problem Improving neural network ensembles for robustness and accuracy.
method Proposes FoRDE, an ensemble learning method based on ParVI, which repels function space by input gradients.
result FoRDE significantly outperforms DEs and other ensemble methods in accuracy and calibration.
Paper improves signal proportion estimation by accounting for variable dependence.
problem Traditional estimators assume independence, limiting applicability in real-world scenarios.
method Integrates arbitrary covariance dependence information using principal factor approximation.
result Method outperforms state-of-the-art estimators in accuracy and detection of weaker signals.
Convex polytope trees expand decision trees with interpretable boundaries.
problem High accuracy often requires many nodes in decision trees, reducing interpretability.
method CPT uses logical disjunction of weighted linear decision-makers, geometrically a convex polytope.
result CPT achieves high accuracy with fewer nodes compared to existing methods.
A new kernel-based CI test improves on existing methods.
problem Testing conditional independence (CI) in a broad range of dependencies.
method Regression-model-agnostic kernel-based CI test using reproducing kernel Hilbert spaces.
result GKCM outperforms state-of-the-art CI tests in simulations.
This study evaluates shrinkage estimators for improving mean and covariance in portfolio optimization.
problem Estimation errors in expected returns and covariance matrix in mean-variance model.
method Examined five shrinkage estimators for expected returns and eleven for covariance matrix across six datasets.
result GMV model with Ledoit Wolf COV2 outperforms traditional methods in most scenarios.
Bayesian neural networks improve uncertainty quantification with unlabelled data.
problem Over-confidence in predictions on covariate-shifted data.
method Approximate Bayesian inference using posterior regularisation with pseudo-labels from unlabelled data.
result Significant improvement in uncertainty quantification accuracy on covariate-shifted data.
The paper explores MMPR to select diverse models for scientific insight.
problem Model selection often fails to bring multiple underlying patterns to light.
method Multi-model penalized regression (MMPR) to acknowledge model uncertainty.
result Different penalty settings can promote either shrinkage or sparsity of coefficients in separate models.
Unified framework for OOD detection and generalization using graph theory.
problem Challenges in out-of-distribution (OOD) generalization and detection in real-world machine learning models.
method Graph-theoretic framework to jointly tackle OOD generalization and detection.
result Empirical validation of theoretical underpinnings with competitive performance.
Paper addresses regret minimization and inference in high-dimensional online decision-making.
problem Regret minimization and statistical inference in high-dimensional online decision-making.
method Integrates ε-greedy bandit algorithm with hard thresholding for sparse bandit parameters and debiasing method for inference.
result Achieves either O(T1/2) regret or O(T1/2)-consistent inference, with trade-off between exploration and exploitation. Proposes a new approach to MSDA by introducing latent covariate shift to handle varying label distributions.
problem Challenges of conventional MSDA approaches in real-world settings where label distributions vary across domains.
method Introduces latent covariate shift (LCS) and a causal generative model with latent noises, latent content variable, and latent style variable.
result Identifies latent content variable up to block identifiability, enabling more nuanced label distribution recovery.
Paper presents a rank-1 approximation method for natural policy gradients in deep RL.
problem Computing natural gradients requires inverting the Fisher Information Matrix, which is computationally expensive.
method Develops a rank-1 approximation to the inverse Fisher Information Matrix for efficient natural policy optimization.
result The rank-1 approximation converges faster and has similar sample complexity to stochastic policy gradient methods.
Looped Transformers improve robustness and expressivity in in-context learning for diverse tasks.
problem Improving robustness and expressivity in in-context learning for diverse tasks.
method Study in-context linear regression with diverse tasks, focusing on depth and looping.
result Looped Transformers exhibit similar expressive power and are provably robust under mild assumptions.
While studying response trajectory, often the population of interest may be diverse enough to exist distinct subgroups within it and the longitudinal change in response may not be uniform in these subgroups. That is, the timeslope and/or influence of covariates in longitudinal profile may vary among these different sub…
The paper introduces a method to decompose variance in twin networks for better treatment effect estimation.
problem Accurate treatment effect estimation requires reliable uncertainty measures to locate model failures.
method Layer-wise variance decomposition using Monte Carlo Dropout in twin networks.
result The encoder component dominates under distributional shift, providing a practical diagnostic for data collection.
A new IPM uses ReLU networks to measure probability discrepancies.
problem Measuring the difference between two probability distributions in high dimensions.
method Proposes a new parametric IPM using ReLU neural networks to optimize and distinguish between distributions.
result The proposed IPM has good convergence rates and can be used as a surrogate for other IPMs.
GFN-SR uses deep learning to generate diverse mathematical expressions.
problem Symbolic regression to find best mathematical expressions.
method Traversing a DAG to generate expression trees sequentially with GFlowNet.
result GFN-SR outperforms other SR algorithms in noisy data.
FREDE efficiently embeds graphs using linear space and guarantees quality.
problem Efficiently embedding graphs with quality guarantees and linear space complexity.
method FREDE combines matrix sketching with a nonlinear transform of PageRank similarities to achieve linear space and quality guarantees.
result FREDE provides column-covariance approximation guarantees that are nearly as good as SVD, even with limited node similarities.
Proposes a method to evaluate generalizability in causal inference models.
problem Lack of formal procedures to statistically evaluate generalizability in causal inference.
method Frugal parameterization to simulate from causal benchmarks, using mean and distributional regression methods.
result Ensures more realistic evaluations of causal inference models, avoiding over-reliance on conventional metrics.