arXiv research

Enhances anomaly detection using multiple reference datasets.

Analyzes symmetries in neural networks to predict learning dynamics.

Paper explores fair classification with bounded disparity using finite datasets.

Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, even models learned with privacy guarantees can inadvertently memorize unique training examples or leak sensitive features. To identify such privacy violations, existing model auditin…

Split conformal prediction provides finite-sample guarantees for black-box models without distributional assumptions.

Paper improves generalization bounds for multi-kernel learning with mixed datasets.

Stochastic optimization algorithms with variance reduction have proven successful for minimizing large finite sums of functions. Unfortunately, these techniques are unable to deal with stochastic perturbations of input data, induced for example by data augmentation. In such cases, the objective is no longer a finite su…

New method clusters large datasets using geometric properties.

New approach for distributed learning of Gaussian mixtures.

The likelihood function of a finite mixture model is a non-convex function with multiple local maxima and commonly used iterative algorithms such as EM will converge to different solutions depending on initial conditions. In this paper we ask: is it possible to assess how far we are from the global maximum of the likel…

KIP meta-learning compresses datasets significantly.

Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of h…

Transformers can interpolate finite input sequences exactly.

Study shows offline RL with partial coverage and weak function classes is possible.

In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification pe…

Probabilistic SR method speeds up high-fidelity simulations with reliable uncertainty estimates.

Cyclic coordinate descent identifies models in finite time and converges linearly.

Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm in the sequential data setting. Our algorithm is applicable to both finite hidden Markov models and hierarchical D…

Exact inference method for Wasserstein distance with finite-sample coverage.

The paper studies the graph geometry of finite groups, creating a dataset and analyzing its properties.

Proposes a method to ensure low losses across all subpopulations in large datasets.

Empirical study compares wide neural networks to kernel methods, resolving open questions.

Transformers with CoT don't enhance reasoning power across all tasks.

New scalable methods for robust model learning from large datasets.

Extends SGM to functional spaces for multimodal data.

RFAD uses random features to speed up dataset distillation.

We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions. The framework is fundamentally different from graph convolutions b…

The paper analyzes Laplace learning for Gaussian measure data in infinite dimensions, proving convergence.

Paper proposes AdaDetect for FDR-controlled novelty detection.

New algorithm improves dataset distillation with 108% improvement on ImageNet.

Approaches for approximating persistent homology for large datasets.

A representative model in integrative analysis of two high-dimensional correlated datasets is to decompose each data matrix into a low-rank common matrix generated by latent factors shared across datasets, a low-rank distinctive matrix corresponding to each dataset, and an additive noise matrix. Existing decomposition …

Considering the creation of persistence landscape on a parametrized curve and structure of sampling, there exists a random process for which a finite mixture model of persistence landscape (FMMPL) can provide a better description for a given dataset. In this paper, a nonparametric approach for computing integrated mean…

Paper detects gradual changes in cluster structure using MC fusion.

An approximate method for conducting resampling in Lasso, the $\ell_1$ penalized linear regression, in a semi-analytic manner is developed, whereby the average over the resampled datasets is directly computed without repeated numerical sampling, thus enabling an inference free of the statistical fluctuations due to sam…

Empirical study shows standard CNNs deviate from NTK predictions.

In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing characters of a new font using a set of different fonts. While most recent research has considered ad-hoc combination rules to address this probl…

The study reveals a transition in neural network performance from infinite-width to variance-limited behavior as dataset size increases.

FaiREE provides fair classification with guarantees for small datasets.

A new algorithm estimates item parameters in item response theory models.

Improved accuracy in dynamic response variation analysis using multi-fidelity data fusion.

Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.

A method smears likelihood to reveal physical energy scales in jet identification.

The paper solves IRL for Bayesian stopping time problems.

Infinite-dimensional SBDMs improve image generation across multiple resolutions.

New technique reduces imitation learning performance gap in finite samples.

The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a kn…

Optimal quantization improves dataset distillation for faster training.