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.

168,657 papers · 148 categories

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48 results for “Distribution Estimation”

Distributed Nonparametric Function Estimation: Optimal Rate of Convergence and Cost of Adaptationmath.ST

Study on distributed nonparametric function estimation with optimal rate and cost of adaptation.

problem Optimal rate of convergence and cost of adaptation in distributed nonparametric function estimation.

method Distributed minimax estimation and adaptive estimation under communication constraints for Gaussian sequence model and white noise model.

result Established minimax rate of convergence and exact communication cost for adaptation.

distributed estimation nonparametric function optimal rate cost of adaptation Besov class

2021-07-01abs ↗pdf ↗

A likelihood approach to nonparametric estimation of a singular distribution using deep generative modelsstat.ML

Improved likelihood estimation for singular distributions using deep models.

problem Estimating singular distributions using deep generative models.

method Data perturbation to avoid singularity issues in likelihood estimation.

result Consistent estimation of target distribution with desirable rates.

likelihood estimation deep generative models singular distributions data perturbation nonparametric estimation

2021-05-09abs ↗pdf ↗

Mutual Information Multinomial Estimationcs.LG

New estimator improves mutual information estimation.

problem Estimating mutual information in data science and machine learning.

method Proposes a new estimator that uses a preliminary estimate of the data distribution.

result A preliminary estimate helps in estimating mutual information more accurately.

mutual information estimation data science machine learning preliminary estimate

2024-08-18abs ↗pdf ↗

Beyond Normal: On the Evaluation of Mutual Information Estimatorsstat.ML

Paper benchmarks mutual info estimators on diverse distributions.

problem Evaluating mutual information estimators on complex, real-world distributions.

method Constructs a diverse family of known-ground truth distributions, proposes a benchmark platform.

result Highlights differences in classical and neural estimators' performance across various conditions.

Mutual Information Evaluation Benchmarking Diverse Distributions Complex Distributions

2023-06-19abs ↗pdf ↗

Distributed Nonparametric Estimation under Communication Constraintsmath.ST

New framework for distributed nonparametric estimation under slow communication.

problem Efficiently estimate nonparametric models across multiple nodes with limited communication.

method Developed a general framework for nonparametric estimation under communication constraints.

result Derived minimax lower and upper bounds for various models.

distributed estimation nonparametric models communication constraints minimax bounds regression

2022-04-21abs ↗pdf ↗

Nonparametric Density Estimation under Distribution Driftcs.LG

Study non-stationary distributions, proving risk bounds for density estimation.

problem Estimating current distribution under gradual changes.

method Proves tight minimax risk bounds for nonparametric density estimation under drift.

result Generalizes previous results on agnostic learning under drift.

density estimation nonparametric distribution drift minimax risk agnostic learning

2023-02-05abs ↗pdf ↗

Statistical Analysis from the Fourier Integral Theoremstat.ME

The paper uses Fourier integral theorem for estimating multivariate distributions.

problem Estimating multivariate distributions and conditional distribution functions.

method Natural Monte Carlo and fully nonparametric estimators based on Fourier integral theorem.

result Explicit Monte Carlo estimators without estimated covariance matrix.

Fourier integral theorem Monte Carlo estimation multivariate distributions conditional distribution functions

2021-06-11abs ↗pdf ↗

Concentration Bounds for Discrete Distribution Estimation in KL Divergencestat.ML

Improved bounds for estimating discrete distributions in KL divergence.

problem Estimating discrete distributions in KL divergence with accuracy.

method Used Laplace estimator and established concentration bounds.

result Deviation from mean scales as

\sqrt{k}/n

for

n \ge k

KL divergence distribution estimation concentration bounds Laplace estimator

2023-02-14abs ↗pdf ↗

Distributed Parameter Estimation in Probabilistic Graphical Modelsstat.ML

This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimator…

2014-06-11abs ↗pdf ↗

Instance-Optimality for Private KL Distribution Estimationstat.ML

Private KL distribution estimation improved with instance-optimality.

problem Minimizing KL divergence between true and estimated distributions.

method Construct minimax optimal private estimators, then focus on instance-optimality.

result Achieved instance-optimality up to constant factors for KL estimation.

KL divergence private estimation instance-optimality Good-Turing estimator differential privacy

2025-05-29abs ↗pdf ↗

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductionecon.EM

Method improves treatment effect estimation in randomized experiments.

problem Estimating distributional treatment effects in randomized experiments.

method Distributional regression framework with machine learning for variance reduction.

result The proposed method reduces variance of distributional treatment effect estimators.

distributional treatment effects randomized experiments machine learning variance reduction asymptotic properties

2024-07-22abs ↗pdf ↗

Estimation of the shape parameter of a generalized Pareto distribution based on a transformation to Pareto distributed variablesq-fin.CP

Random variables of the generalized Pareto distribution, can be transformed to that of the Pareto distribution. Explicit expressions exist for the maximum likelihood estimators of the parameters of the Pareto distribution. The performance of the estimation of the shape parameter of generalized Pareto distributed using …

2012-10-29abs ↗pdf ↗

Transformer-based Parameter Estimation in Statisticscs.LG

Transformer improves parameter estimation without needing closed-form solutions.

problem Parameter estimation in statistics, especially for complex distributions.

method Transformer-based approach for parameter estimation without closed-form solutions or derivations.

result Transformer-based approach achieves similar or better accuracy than maximum likelihood estimation.

Transformer parameter estimation statistical inference machine learning maximum likelihood

2024-02-28abs ↗pdf ↗

A Unified Framework for Multi-distribution Density Ratio Estimationcs.LG

Unified framework for estimating density ratios across multiple distributions.

problem Binary density ratio estimation for multiple distributions.

method Unified framework based on Bregman divergence minimization.

result Generalization of binary DRE methods to multiple distributions.

density ratio estimation multi-distribution Bregman divergence scoring rules downstream tasks

2021-12-07abs ↗pdf ↗

Data Amplification: Instance-Optimal Property Estimationmath.ST

The best-known and most commonly used distribution-property estimation technique uses a plug-in estimator, with empirical frequency replacing the underlying distribution. We present novel linear-time-computable estimators that significantly "amplify" the effective amount of data available. For a large variety of distri…

2019-03-04abs ↗pdf ↗

A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimationstat.ML

Unified view on learning unnormalized distributions using NCE.

problem Learning unnormalized distributions across different communities.

method Noise-Contrastive Estimation (NCE) for a unified perspective.

result Established finite-sample convergence rates for exponential families.

Noise-Contrastive Estimation Unnormalized Distributions Exponential Families Finite-Sample Convergence

2024-09-26abs ↗pdf ↗

On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholdingmath.ST

We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in the large-sample limit. The asymptotic distributions are derived for both the case where the estimators are tuned to perform consistent model selection and for the case where the estimators are tuned to perform conserva…

2007-11-05abs ↗pdf ↗

L^1

Estimation: On the Optimality of Linear Estimatorsmath.ST

This work shows that Gaussian is the only prior for optimal linear estimation in $L^1$ loss.

problem Optimal linear estimation of a random variable from noisy observations under

L^1

fidelity criterion.

method Analyzes the conditions under which the conditional median is a linear estimator and identifies the Gaussian distribution as the only prior that induces linearity.

result Gaussian is the only prior distribution that induces linearity in the conditional median for

L^1

loss.

Gaussian distribution conditional median optimal Bayesian estimator linear estimation L1 loss

2023-09-17abs ↗pdf ↗

Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examplescs.LG

New method calibrates uncertainty estimates for image classifiers without labeled data.

problem Uncertainty estimates for modern classifiers are unreliable without labeled calibration data.

method Calibrates uncertainty estimates using unlabeled examples for distribution shifts.

result Proposes a method that provides excellent uncertainty estimates under natural distribution shifts.

distribution shift uncertainty estimation image classification conformal predictors reliability

2022-10-09abs ↗pdf ↗

Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysiscs.DC

This paper tackles distributed estimation of the top-L eigenspace in PCA for large data sets.

problem Challenges in estimating the top-L eigenspace in principal component analysis for large data sets.

method Proposes a novel multi-round algorithm using shift-and-invert preconditioning and convex optimization.

result Achieves a fast convergence rate and covers the targeted top-L eigenspace without explicit eigengap assumption.

distributed estimation principal component analysis eigenspace PCA convex optimization

2020-04-05abs ↗pdf ↗

The Difference-of-Log-Normals Distribution: Properties, Estimation, and Growthstat.ME

Paper characterizes DLN distribution, its properties, and estimation methods.

problem No specific problem stated, focuses on DLN distribution properties.

method Characterization of PDF, CDF, moments; generalization to N-dimensions; methods to handle double-exponential nature.

result Characterization of DLN distribution and its properties, including estimation methods.

Difference-of-Log-Normals Distribution Estimation Growth Double-exponential

2023-02-05abs ↗pdf ↗

Compensatory model for quantile estimation and application to VaRq-fin.MF

Improved quantile estimation model for VaR.

problem Improving quantile estimation under distribution estimation.

method Develops a compensatory model with a penalty term to control convergence error.

result Significant improvement in VaR performance.

quantile estimation value at risk penalty term time series

2021-12-14abs ↗pdf ↗

Do PAC-Learners Learn the Marginal Distribution?cs.LG

New findings on PAC learning and marginal distribution estimation.

problem Understanding how PAC learning relates to marginal distribution estimation under distributional constraints.

method Revisited the connection between PAC learning, uniform convergence, and density estimation, considering a known family of marginal distributions.

result PAC learning is sandwiched between two refined models of density estimation, differing only in whether the learner knows the set of well-estimated events in H.

PAC learning uniform convergence density estimation marginal distribution distributional constraints

2023-02-13abs ↗pdf ↗

Distributional Reinforcement Learning on Path-dependent Optionsq-fin.MF

New method uses DistRL to estimate entire payoff distribution for financial derivatives.

problem Traditional methods focus on expected option value; this tackles risk-aware pricing.

method Reinterprets and proposes a framework using Distributional Reinforcement Learning (DistRL).

result Demonstrates enhanced risk-aware pricing and uncertainty quantification on Asian options.

Reinforcement Learning Financial Derivatives Distributional Estimation Asian Options Risk-Aware Pricing

2025-07-16abs ↗pdf ↗

Attainability of Two-Point Testing Rates for Finite-Sample Location Estimationmath.ST

The study examines conditions for achieving a simple lower bound in estimating mean from samples.

problem Achieving a simple lower bound for estimating the mean of a distribution.

method Analyzes conditions for nearly attaining Le Cam's two-point testing lower bound for mean estimation.

result An algorithm nearly attains the two-point testing rate for mixtures of symmetric, log-concave distributions with a common mean.

mean estimation Le Cam's two-point testing log-concave distributions Hellinger modulus of continuity adaptive estimation

2025-02-09abs ↗pdf ↗

Finite Sample Guarantees for Distributed Online Parameter Estimation with Communication Costseess.SY

The paper provides bounds on estimation error in a distributed online learning setting.

problem Estimating an unknown parameter in a distributed and online manner with finite sample guarantees.

method Proposes a distributed online estimation algorithm that improves accuracy through communication, providing non-asymptotic bounds on estimation error.

result Demonstrates a trade-off between estimation error and communication costs, and determines a stopping time for communication based on desired accuracy.

distributed learning online estimation finite sample guarantees estimation error communication costs

2022-09-12abs ↗pdf ↗

Consistency for constrained maximum likelihood estimation and clustering based on mixtures of elliptically-symmetric distributions under general data generating processesmath.ST

Consistent estimator for mixtures of nonparametric elliptical distributions helps cluster analysis.

problem Consistency of maximum likelihood estimator for mixtures of nonparametric elliptical distributions.

method Maximum likelihood estimation for mixtures of elliptically-symmetric distributions under nonparametric

P

result Components of the estimator correspond to well-separated components of the underlying distribution

P

maximum likelihood elliptical distributions mixtures consistency cluster analysis

2023-11-10abs ↗pdf ↗

Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inferencestat.ML

Neural Empirical Bayes estimates source distributions from noisy simulations.

problem Estimating source distributions from noisy, simulated data.

method Uses neural density estimators to estimate a prior or source distribution over uncorrupted samples, then performs posterior inference.

result Recovering ground truth source distributions up to symmetries.

Neural Empirical Bayes Source Distribution Estimation Simulation-Based Inference Empirical Bayes Neural Density Estimators

2020-11-11abs ↗pdf ↗

Gradient Estimation with Discrete Stein Operatorsstat.ML

Improves gradient estimation for discrete distributions with variance reduction techniques.

problem Excessive variance in gradient estimation for discrete distributions.

method Stein operators for discrete distributions and control variates.

result Substantially lower variance in gradient estimation.

gradient estimation discrete distributions variance reduction control variates REINFORCE

2022-02-19abs ↗pdf ↗

Estimating Potential Outcome Distributions with Collaborating Causal Networksstat.ML

CCN estimates full potential outcome distributions without restrictive assumptions.

problem Estimating CATE is insufficient; full potential outcome distributions provide greater insights.

method Collaborating Causal Networks (CCN) learns full potential outcome distributions without restrictive assumptions.

result CCN learns distributions that asymptotically capture true potential outcome distributions.

Causal inference Potential outcome distributions Collaborating Causal Networks Observational studies Treatment effects

2021-10-04abs ↗pdf ↗

Learning Parametric Distributions from Samples and Preferencesstat.ML

Paper improves parameter estimation of continuous distributions using preference feedback.

problem Improving parameter estimation of continuous distributions.

method Preference-based M-estimators and deterministic preferences.

result Preference-based estimators achieve an estimation error scaling of O(1/n), significantly faster than sample-only methods.

parameter estimation continuous distributions preference feedback M-estimators asymptotic variance

2025-05-29abs ↗pdf ↗

Improved Distribution Estimation in

\ell_\infty

stat.ML

Improved bounds for discrete probability distribution estimation under the ℓ∞ norm.

problem Estimating discrete probability distributions under the ℓ∞ norm with improved bounds.

method Minimax bounds in expectation and high-probability tail bounds.

result Resolved open questions posed in Kontorovich and Painsky (JMLR, 2025), including a fully empirical tightest risk bound and identifying the worst-case extremal distribution.

Distribution Estimation ℓ∞ Norm Minimax Bounds Probability Theory Empirical Results

2026-05-28abs ↗pdf ↗

Distributed Statistical Estimation and Rates of Convergence in Normal Approximationmath.ST

This paper presents a class of new algorithms for distributed statistical estimation that exploit divide-and-conquer approach. We show that one of the key benefits of the divide-and-conquer strategy is robustness, an important characteristic for large distributed systems. We establish connections between performance of…

2017-04-09abs ↗pdf ↗

A Spectral Approach to Gradient Estimation for Implicit Distributionsstat.ML

Recently there have been increasing interests in learning and inference with implicit distributions (i.e., distributions without tractable densities). To this end, we develop a gradient estimator for implicit distributions based on Stein's identity and a spectral decomposition of kernel operators, where the eigenfuncti…

2018-06-07abs ↗pdf ↗

Improving realistic semi-supervised learning with doubly robust estimationcs.LG

Improves SSL with doubly robust estimation of unlabeled class distribution.

problem Limited labeled data and long-tailed class distributions in unlabeled data.

method Explicitly estimate unlabeled class distribution using doubly robust estimator.

result Improves performance of SSL methods on unlabeled data.

semi-supervised learning doubly robust estimation long-tailed distributions pseudo-labeling

2025-02-01abs ↗pdf ↗

Frustratingly Easy Uncertainty Estimation for Distribution Shiftstat.ML

Simple method improves uncertainty estimation for distribution shifts.

problem Improving uncertainty estimation in deep image classification under distribution shifts.

method Exposing original model to corrupted images and performing simple statistical calibration.

result Superior performance on various distribution shifts and unsupervised domain adaptation tasks.

uncertainty estimation distribution shift deep learning image classification statistical calibration

2021-06-07abs ↗pdf ↗

Risk Bounds For Distributional Regressionstat.ML

The study establishes risk bounds for distributional regression estimators.

problem Estimating distributional regression models with nonparametric methods.

method Theoretical bounds for CRPS and MSE are derived for convex and non-convex constraints.

result Theoretical risk bounds are validated through experiments on simulated and real data.

distributional regression risk bounds nonparametric methods CRPS MSE

2025-05-14abs ↗pdf ↗

Path-Gradient Estimators for Continuous Normalizing Flowscs.LG

New path-gradient estimator for continuous normalizing flows.

problem Limitation of simple Gaussian variational distributions in complex applications.

method Proposed a path-gradient estimator for continuous normalizing flows.

result Empirical evidence of superior performance of the new estimator.

path-gradient continuous normalizing flows variational distributions variational algorithms

2022-06-17abs ↗pdf ↗

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effectsstat.ML

New tests for distributional causal effects using improved kernel estimators.

problem Testing for higher-order moments and multidimensional outcomes affected by treatment.

method Improved kernel estimators based on doubly robust mean embeddings.

result New permutation-based tests for distributional causal effects with improved convergence rates.

causal inference distributional effects kernel methods doubly robust higher moments

2022-12-09abs ↗pdf ↗

Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regressionstat.ML

New method estimates density ratio for well-separated distributions using multi-class logistic regression.

problem Challenges in estimating density ratio for well-separated distributions.

method Uses multi-class logistic regression with auxiliary densities to estimate log(p/q).

result Demonstrates superior performance on density ratio estimation, mutual information, and representation learning tasks.

density ratio multiclass logistic regression well-separated distributions distribution shift binary classification

2023-05-01abs ↗pdf ↗

Optimal Sub-Gaussian Mean Estimation in

\mathbb{R}

math.ST

New estimator accurately estimates mean of real-valued distributions without variance knowledge.

problem Estimating the mean of real-valued distributions without prior variance knowledge.

method Introduces a novel estimator that converges sub-Gaussian and works across distributions with bounded variance.

result The estimator achieves accuracy of σ·(1+o(1))√(2log(1/δ)/n) with parameters n, δ, and σ².

mean estimation sub-Gaussian convergence variance-free bounded variance dependent random variables

2020-11-17abs ↗pdf ↗

Robust hypothesis testing and distribution estimation in Hellinger distancemath.ST

Robust test for distributions under Hellinger distance, simpler than optimal tests.

problem Testing and estimating distributions robustly under Hellinger distance.

method Simple robust hypothesis test with optimal sample complexity, robust to Hellinger distance perturbations.

result Empirically demonstrated robustness and power of the test on canonical distributions.

Hellinger distance robust hypothesis testing distribution estimation sample complexity

2020-11-03abs ↗pdf ↗

Communication-efficient Distributed Sparse Linear Discriminant Analysisstat.ML

We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size $N$ into $m$ machines, and estimates a local sparse LDA estimator on each machine using the data subset of size $N/m$ . After the distri…

2016-10-15abs ↗pdf ↗

The Vector Poisson Channel: On the Linearity of the Conditional Mean Estimatorcs.IT

The paper examines conditions for linearity in a conditional mean estimator under vector Poisson noise.

problem Conditions for linearity of the conditional mean estimator in vector Poisson noise.

method Analyzes prior distributions and their impact on the conditional mean estimator's linearity.

result The only prior distribution that induces linearity is a product gamma distribution, and non-zero dark current parameter prevents linearity.

vector poisson channel conditional mean estimator linearity product gamma distribution Poisson noise

2020-03-19abs ↗pdf ↗

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationecon.EM

The paper proposes a method to estimate treatment effects using CAR designs with additional covariates.

problem Estimating distributional treatment effects in CAR designs with additional covariates.

method Flexible distribution regression framework that incorporates additional covariates using machine learning methods.

result The proposed estimator attains the semiparametric efficiency bound for distributional treatment effects under CAR.

distributional treatment effects covariate-adaptive randomization flexible regression machine learning asymptotic distribution

2025-06-06abs ↗pdf ↗

Distributional Off-Policy Evaluation with Deep Quantile Process Regressionstat.ML

Paper introduces DQPOPE for estimating return distributions in reinforcement learning.

problem Estimating the entire return distribution from off-policy data.

method Deep quantile process regression for distributional off-policy evaluation.

result DQPOPE achieves statistical advantages by estimating full return distribution with same sample size.

reinforcement learning off-policy evaluation distributional reinforcement learning deep quantile process regression

2026-04-20abs ↗pdf ↗

The Broad Optimality of Profile Maximum Likelihoodstat.ML

We study three fundamental statistical-learning problems: distribution estimation, property estimation, and property testing. We establish the profile maximum likelihood (PML) estimator as the first unified sample-optimal approach to a wide range of learning tasks. In particular, for every alphabet size $k$ and desired…

2019-06-10abs ↗pdf ↗

Differentially Private Assouad, Fano, and Le Camcs.LG

The paper extends statistical estimation techniques under differential privacy.

problem Establishing sample complexity bounds for estimation tasks under differential privacy.

method Proposes analogues of Le Cam's method, Fano's inequality, and Assouad's lemma under central differential privacy.

result Optimal sample complexity bounds for discrete distribution estimation under total variation and

\ell_2

distances.

differential privacy statistical estimation lower bounds coupling sample complexity

2020-04-14abs ↗pdf ↗