New network learns non-parametric invariances from data.
problem Modeling non-parametric invariances in data.
method Introduces PRC-NPTN networks with permanent random connectomes.
result Improves generalization and outperforms existing methods.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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48 results for “Non-parametric Learning”
Proposes method for eliciting non-parametric joint priors using normalizing flows.
problem Learning complex non-parametric joint priors for model parameters.
method Expert elicitation combined with normalizing flows for generative modeling.
result Framework supports elicitation of both parametric and non-parametric priors.
Method learns Markov networks from continuous data without distributional assumptions.
problem Learning Markov network structures for continuous data without distributional assumptions.
method Combines non-parametric mutual information estimator with constraint-based algorithm for learning graph structure.
result Shows superior structure learning accuracy compared to competing methods on synthetic data with non-linear dependencies.
New defense method for non-parametric classifiers robust against adversarial attacks.
problem Lack of robustness in non-parametric classifiers against adversarial attacks.
method Adversarial pruning method to preprocess datasets and a novel attack.
result Adversarial pruning provides a robust defense for non-parametric classifiers.
New algorithm learns nonlinear phenomena from noisy local measurements without data exchange.
problem Learning nonlinear phenomena from noisy local measurements in a decentralized network.
method Non-parametric learning algorithm that spreads information only between neighboring nodes.
result Non-asymptotic estimation error bounds for the proposed method.
FAMOS combines parametric and non-parametric methods for efficient image stylization.
problem Efficiently stylize images with limited data and compute resources.
method Fully Adversarial Mosaics (FAMOS) that integrates parametric and non-parametric approaches.
result Demonstrates the effectiveness of FAMOS in stylizing images with minimal data and compute resources.
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
problem Over-engineering in GNN architectures for common semi-supervised node classification datasets.
method Replacing feature aggregation with a non-parametric learner to streamline GNN design.
result Non-parametric regression is effective for semi-supervised learning on sparse, directed networks.
Bayesian model learns complex multivariate dependencies.
problem Learning dependency structures across multiple dimensions.
method Flexible Gaussian process priors and Dirichlet process for structure learning.
result Efficient variational inference for model parameters.
Develops a new method for learning non-parametric DAGs using RKHS.
problem Challenges of learning non-parametric causal models with large combinatorial search space.
method Uses reproducing kernel Hilbert spaces (RKHS) and sparsity-inducing regularization terms based on partial derivatives to enforce acyclicity.
result Shows improved performance through simulations and data analyses.
Study compares non-parametric models for predicting medical insurance reimbursement delays.
problem Estimating the time-lapse between medical insurance reimbursement.
method Comparative study of four non-parametric regression models (KNNs, SVMs, Decision Trees, Random Forests) using R-squared metric.
result Each model's performance varies with training data size, feature space, and hyperparameters.
COHORTNEY groups web users based on activity patterns.
problem Lack of academic discussion on cohort analysis for user behavior.
method Unsupervised non-parametric machine learning approach.
result COHORTNEY outperforms traditional methods in cohort analysis.
Enhances ML model explanations through Bayesian non-parametric approach.
problem Difficulty in understanding and interpreting ML model decisions.
method Augments a Bayesian non-parametric regression mixture model with elastic nets.
result Empirically outperforms state-of-the-art techniques in explaining individual decisions.
Unified data representation learning improves non-parametric two-sample testing.
problem Improving non-parametric two-sample testing accuracy.
method Proposes RL-TST framework combining IRs and DRs for better test power.
result RL-TST outperforms existing methods by leveraging both IRs and DRs.
New test detects when generative models memorize training data.
problem Detecting when generative models overfit by memorizing training data.
method A non-parametric three-sample test using training set, target distribution, and model-generated samples.
result The test effectively detects data-copying in various models and datasets.
Paper proposes a machine learning-based method for estimating mediation effects.
problem Challenges in estimating mediation effects with multiple, continuous mediators.
method Developed a one-step estimation algorithm using machine learning and Riesz learning.
result Proposed method can estimate mediation effects from just two statistical estimands.
Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixtur…
Bayesian approach improves uncertainty in deep learning models.
problem Uncertainty quantification in deep learning models.
method Bayesian point of view, Gaussian approximability, semi-parametric Bernstein-von Mises theorems.
result Bayesian credible regions have valid frequentist coverage, providing theoretical justification for deep learning.
Non-parametric time series forecasting without assuming a specific distribution.
problem Time series forecasting with numerical stability issues in classical models.
method Generates predictions by sampling from the empirical distribution of time series data.
result The proposed method produces reasonable forecasts without numerical stability issues.
We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hi…
Paper relaxes assumptions for non-parametric estimation in pairwise learning.
problem Generalization performance of non-parametric estimation for pairwise learning.
method Significantly relaxes restrictive assumptions, constructs structured deep ReLU neural network, and designs targeted hypothesis space.
result Establishes a sharp oracle inequality for empirical minimizer with general hypothesis space for Lipschitz continuous pairwise losses.
Study learns mixtures of smooth product distributions from samples.
problem Learning mixtures of non-parametric product distributions.
method Two-stage approach using identifiability properties of tensor decomposition and signal processing techniques.
result Recovery of component distributions under a smoothness condition.
The paper reviews methods for estimating individual treatment effects using non-parametric regression models.
problem Estimating heterogeneous treatment effects in observational data.
method Non-parametric regression models to estimate individual treatment effects.
result A review and development of existing state-of-the-art frameworks for individual treatment effects estimation.
The paper proposes a method to learn the structure of continuous-action games with non-parametric utilities using a limited number of samples.
problem Learning the exact structure of continuous-action games with non-parametric utility functions.
method An regularized method that encourages sparsity of the Fourier transform coefficients of the utility functions, accessed via a few Nash equilibria and their noisy utilities.
result The method recovers the exact structure of the utility functions and the game structure with provable theoretical guarantees.
Novel non-parametric tree model learns tree distributions.
problem Learning distributions for tree-structured data.
method Bottom-up hidden tree Markov model with infinite states.
result Novel non-parametric generalization of hidden tree Markov model.
Proposes a non-parametric method for deep discrete latent variable models.
problem Learning sparse discrete latent representations in deep models.
method Iterative algorithm with Beta-Bernoulli process prior and local data scaling.
result Improves sparsity and scalability of deep discrete latent variable models.
New deep learning model uses self-attention to consider entire dataset for predictions.
problem Traditional deep learning models focus on single input datapoints; this model considers the whole dataset.
method Introduces self-attention mechanism to reason about relationships between datapoints.
result Models solve cross-datapoint lookup and complex reasoning tasks.
Estimates conditional Brenier maps using entropic optimal transport.
problem Non-parametric estimation of conditional Brenier maps.
method Entropic optimal transport for scalable non-parametric estimation.
result Entropic optimal transport maps asymptotically converge to conditional Brenier maps.
Paper proposes a policy-search algorithm to learn entropy-maximizing exploration policies in reward-free environments.
problem Reward-free learning in high-dimensional, continuous-control domains.
method Maximum Entropy POLicy optimization (MEPOL) algorithm that maximizes a non-parametric state entropy estimate.
result MEPOL learns a maximum-entropy exploration policy that facilitates learning various reward-based tasks.
The paper improves prediction intervals for non-parametric regression using histograms.
problem Computing accurate prediction intervals for non-parametric regression models.
method Uses conditional histograms to estimate conditional distributions and compute shortest prediction intervals.
result The method provides prediction intervals with provable marginal coverage and asymptotic conditional coverage.
Proposes a multi-stage algorithm for efficient spectrum access in CR networks.
problem High demand for wireless spectrum and need for high throughput and energy efficiency in SUs.
method Centralized multi-stage algorithm with non-parametric learning and adaptive collision avoidance.
result Ensures minimum interference to licensed users while providing high throughput and energy efficiency.
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimizationstat.ML
Paper introduces an online method for estimating the difference between two probability distributions.
problem Estimating the difference between two probability density functions using available data.
method Non-parametric online likelihood-ratio estimation using Pearson-divergence functional minimization.
result The proposed method provides efficient online updates and theoretical guarantees for performance.
New deep learning model improves impulse response estimation.
problem Impulse response estimation of stable linear systems.
method Data-driven deep learning model.
result New model captures more hidden patterns in data.
A graph-based method for two-sample testing across connected nodes.
problem Identifying nodes where two probability distributions differ significantly.
method Collaborative non-parametric two-sample testing (CTST) framework.
result CTST outperforms independent node tests by leveraging graph structure.
Active learning recovers choice model from noisy data.
problem Identifying non-parametric choice models from noisy data.
method Directed acyclic graph (DAG) representation and inclusion-exclusion approach.
result Algorithm more accurately recovers frequent preferences.
New algorithm reduces dynamic regret for noisy gradient feedback with piecewise polynomial comparators.
problem Online estimation of piecewise polynomial trends with noisy feedback.
method Introduces variational constraint for piecewise polynomial comparators, designs adaptive algorithm.
result Achieves nearly optimal dynamic regret of $ ilde{O}(n^{ rac{1}{2k+3}}C_n^{ rac{2}{2k+3}})$.
DeepAveragers solves offline RL by solving derived MDPs from static data.
problem Offline reinforcement learning with limited data.
method Solves derived non-parametric MDPs (DAC-MDPs) using deep representations and costs for under-represented parts.
result The approach can lower-bound performance and scale to complex offline RL problems.
NPOD algorithm improves efficiency in estimating pharmacokinetic parameters.
problem Efficiently estimating joint distribution of model parameters in population pharmacokinetics.
method Uses gradient approach to suggest new support points, reducing evaluation time.
result Achieves similar solutions to NPAG but with significantly fewer cycles and runtime.
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …
Proposes a non-parametric method to calibrate classifier confidence estimates.
problem Inaccurate uncertainty estimation in classification methods.
method Uses a latent Gaussian process for non-parametric calibration of any classifier.
result Improves calibration of confidence estimates across various classifiers and datasets.
SurvMixClust clusters survival data and predicts individual survival curves.
problem Integrating clustering into survival analysis for precision medicine.
method SurvMixClust learns latent representations for clustering and predicts survival functions using a mixture of non-parametric experts.
result SurvMixClust creates balanced clusters with distinct survival curves, outperforming clustering baselines and competing with non-clustering models in predictive accuracy.
Robust learning method minimizes risk with corrupted data.
problem Statistical learning with unknown corrupted data fraction.
method Develops a robust learning method with specified corrupted data fraction upper bound.
result Optimal weights provide robustness against corrupted data.
This study examines when non-parametric methods are robust to adversarial examples.
problem Understanding when non-parametric methods are robust to adversarial examples.
method Examined general non-parametric methods and established conditions for r-consistency.
result Non-parametric methods like nearest neighbors and kernel classifiers are r-consistent when data is well-separated, while histograms are not.
The paper calibrates probabilistic regression models to better estimate real-valued targets.
problem Improving probability density estimates for regression models.
method Proposes three non-parametric approaches to calibrate regression models.
result Demonstrates improved performance of regression models on predictive likelihood.
Efficiently estimates Hawkes process kernels using non-parametric Bayesian methods.
problem Estimating flexible Hawkes process kernels with uncertainty quantification.
method Cluster representation of Hawkes processes, Gibbs sampling, expectation maximization.
result Linear time complexity in both theoretical and empirical settings.
New graphical criteria for efficient covariate adjustment in non-parametric causal models.
problem Estimating population average treatment effects in observational studies using non-parametric causal graphical models.
method Developed new graphical criteria to determine efficient covariate adjustment sets for estimating treatment effects in non-parametric causal graphical models.
result Graphical criteria for efficient covariate adjustment can be applied in both linear and non-parametric causal models.
Study provides guarantees for kernel clustering under non-parametric mixtures.
problem Statistical guarantees for kernel-based clustering without strong assumptions.
method Non-parametric mixture models, kernel-based clustering, consistency guarantees.
result Necessary and sufficient separability conditions for consistent clustering recovery.
Develops flexible non-parametric ACFs using B-spline kernels.
problem Flexible modelling of the autocovariance function (ACF) in time-series, spatial, and spatio-temporal analysis.
method Derives the inverse Fourier transform of B-spline spectral bases to create a general class of non-parametric ACFs.
result Provides a provably dense, flexible, and general class of non-parametric ACFs for various types of processes.
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allo…