Paper optimizes estimation of quadratic functionals in nonparametric IV models.
problem Optimal estimation of a nonlinear functional in ill-posed inverse regression.
method Adaptive, minimax estimation using leave-one-out, sieve NPIV estimator with data-driven sieve dimension selection.
result Adaptive estimator achieves minimax optimal rate in various ill-posed cases.
Paper develops methods for inference on time series data using neural networks and sieves.
problem Inference on time series data with nonparametric conditional moment restrictions.
method GN-QLR based inference using general nonlinear sieves and multilayer neural networks.
result Optimally weighted GN-QLR statistic is asymptotically Chi-square distributed.
CORES2 removes noisy labels by sieving out corrupted examples.
problem Instance-dependent label noise degrades DNN performance.
method CORES2 (COnfidence REgularized Sample Sieve) progressively sieves out corrupted examples.
result CORES2 provides theoretical guarantees for filtering out corrupted examples.
Develops methods for estimating and providing confidence bands in sparse high-dimensional additive models.
problem Estimating and providing reliable confidence bands for nonparametric components in high-dimensional additive models.
method Integrates sieve estimation into a high-dimensional Z-estimation framework, employing a multiplier bootstrap procedure.
result Constructs uniformly valid confidence bands for the target component f1 in sparse high-dimensional additive models. We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the literature, the kernel regression and the spline method, and is of interest in it…
AR-Sieve Bootstrap improves Random Forest time series prediction accuracy.
problem Inaccurate time series prediction due to inadequate resampling methods.
method Combines Random Forest with AR-Sieve Bootstrap for better resampling.
result AR-Sieve Bootstrap leads to more accurate predictions compared to other methods.
We introduce a new framework for unsupervised learning of representations based on a novel hierarchical decomposition of information. Intuitively, data is passed through a series of progressively fine-grained sieves. Each layer of the sieve recovers a single latent factor that is maximally informative about multivariat…
Efficiently calculates privacy guarantees for 2020 Census data.
problem Evaluate privacy guarantees for 2020 U.S. Census data releases.
method Sieve-accelerated quadrature method to evaluate tail probabilities of high-dimensional convolutions.
result Achieves 1,824-fold speedup over prior methods while maintaining error tolerances.
New methods for estimating and inferring nonparametric structural functions and elasticities.
problem Estimating and inferring nonparametric structural functions and their derivatives.
method Data-driven sieve dimension choice and uniform confidence bands construction.
result Optimal estimation and inference procedures with minimax rates of convergence.
Develops a nonparametric method to estimate isotropic covariance functions efficiently.
problem Estimating isotropic covariance functions without assuming a specific parametric form.
method Uses Bernstein polynomials and sieve maximum likelihood estimation.
result Consistent estimator with improved performance compared to parametric and nonparametric alternatives.
The paper uses deep neural networks to estimate economic models without separability restrictions.
problem Estimating economic models with complex interaction effects and non-separable restrictions.
method Uses deep neural networks as a nonparametric sieve to approximate regression functions from nonlinear latent variable models.
result Economic shape, sparsity, or separability restrictions are imposed more straightforwardly when a flexible latent variable model is used.
We prove that the set of non-pseudo-Anosov elements in the Torelli group is exponentially small.
A test for neural networks identifies genetic associations.
problem Testing complex associations in neural networks.
method Sieve quasi-likelihood ratio test for neural networks with one hidden layer.
result The test statistic has an asymptotic chi-squared distribution.
Two approaches to directly estimating Riesz representer are shown to be numerically equivalent under certain conditions.
problem Estimating Riesz representer in semiparametric statistics.
method Two distinct optimization problems solved by automatic debiased machine learning and sieve methods for conditional moment models.
result Numerical equivalence of estimators under specific regularization schemes, but not for others.
The paper develops methods for causal function estimation and inference with multiway clustered data.
problem Estimation and inference for causal functions under multiway clustering.
method Two-step procedure using machine learning for nuisance parameters and projection onto basis functions.
result Rejects the null hypothesis of uniformly zero effects and reveals heterogeneous treatment effects.
A new model explains asset returns with a single factor, improving cross-sectional performance.
problem Understanding the cross-section of asset returns with complex models.
method Proposes a non-linear single-factor asset pricing model with a nonparametric link function estimated jointly with sieve-based estimators.
result The model delivers superior cross-sectional performance with a low-dimensional approximation of the link function.
Measuring the relationship between any pair of variables is a rich and active area of research that is central to scientific practice. In contrast, characterizing the common information among any group of variables is typically a theoretical exercise with few practical methods for high-dimensional data. A promising sol…
Develops deep generative models for stratified learning.
problem Challenges in learning distributions on stratified spaces with varying dimensions and singularities.
method Two generative frameworks: sieve maximum likelihood and diffusion-based.
result Establishes convergence rates and consistency for estimating intrinsic dimensions and number of strata.
tsbootstrap handles time series uncertainty without assuming independence.
problem Time series data violate IID assumptions, leading to undercoverage in traditional methods.
method Provides various resampling and bootstrap methods, including classical and adaptive conformal calibration.
result Dependence-aware methods reduce coverage deficits, with sieve resampling performing best.
New method constructs synthetic treatment groups without mean exchangeability assumption.
problem Violations of mean exchangeability assumption in randomized controlled trials.
method Weighted mixture of treatment groups from source populations, minimizing conditional maximum mean discrepancy.
result Asymptotic normality of synthetic treatment group estimator established.
The study evaluates the performance of ANNs in financial forecasting.
problem Mixed evidence on the predictive performance of ANNs for financial time-series data.
method Proposes a flexible nonparametric model and compares its performance to other estimators.
result The proposed model shows better performance than basic benchmarks in estimating Value-at-Risk.
Streaming algorithms are generally judged by the quality of their solution, memory footprint, and computational complexity. In this paper, we study the problem of maximizing a monotone submodular function in the streaming setting with a cardinality constraint k. We first propose Sieve-Streaming++, which requires just…
The main result of this paper is an effective count for Apollonian circle packings that are either bounded or contain two parallel lines. We obtain this by proving an effective equidistribution of closed horospheres in the unit tangent bundle of a geometrically finite hyperbolic 3-manifold of infinite volume, whose fun…
Nonparametric density deconvolution and denoising using simulation-based inference
problem Learning latent signals and their distributions in the presence of measurement noise
method Convolutional maximum mean discrepancy (convMMD) loss and likelihood-free framework
result Learn a latent generative model matching observed data distribution
For a geometrically finite group Gamma of G=SO(n,1), we survey recent developments on counting and equidistribution problems for orbits of Gamma in a homogeneous space H\G where H is trivial, symmetric or horospherical. Main applications are found in an affine sieve on orbits of thin groups as well as in sphere countin…
New adaptive test for NPIV models controls size and has superior power.
problem Testing inequality and equality restrictions in nonparametric IV models.
method Adaptive hypothesis test based on modified leave-one-out sample quadratic distance.
result Adaptive test attains the adaptive minimax rate of testing in L2. Study on Q-function estimation for continuous state-action MDPs, deriving rates and conditions.
problem Estimating Q-function in off-policy evaluation for continuous state-action Markov decision processes. method Reformulated as nonparametric instrumental variables (NPIV) problem, derived minimax lower bounds, proposed sieve two-stage least squares estimator.
result First minimax lower bounds for Q-function and its derivatives in sup-norm and L2-norm, same as classical nonparametric regression. Proposes a new method for estimating non-pathwise differentiable functional parameters.
problem Estimating dose-response curves for continuous exposure.
method Targeted Highly Adaptive Lasso (HAL) for non-pathwise differentiable functional parameters.
result The Targeted HAL-MLE achieves dimension-free rates up to log(n) factors and outperforms other methods in simulations.
Nonparametric neural-network estimation of current-status data
problem Estimation of conditional cumulative distribution function with current-status data
method Neural-network sieve maximum likelihood estimator
result Explicit convergence rate for Hölder smoothness
Method constructs nonparametric prediction intervals with finite-sample guarantees.
problem Nonparametric instrumental variable regression with finite-sample coverage.
method Conformal inference framework applied to NPIV, combining with various estimators.
result Distribution-free, finite-sample coverage over chosen IV shifts.
We study likelihood-based methods for distribution regression with deep generative models.
problem Distribution regression with high-dimensional responses concentrated on a low-dimensional manifold.
method Likelihood-based approach using conditional deep generative models.
result Convergence rates for estimating conditional distributions in Hellinger and Wasserstein metrics.
Paper identifies and estimates CAPCEs in continuous treatment settings.
problem Estimating heterogeneous causal effects of continuous treatments.
method Instrumental variable approach to identify CAPCEs under weaker conditions.
result Developed three families of CAPCE estimators with statistical properties analyzed.
A number of optimal decision problems with uncertainty can be formulated into a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC) algorithm is a popular numerical method to approach solutions of such stochastic control problems as analytical solutions are not tractable in general. This paper ge…
Paper analyzes deep neural networks with dependent data, establishing convergence rates and error bounds.
problem Statistical analysis of deep neural networks under dependent data.
method Establishes rates of convergence and L2-error bounds for nonparametric sieve estimators of DNNs. result Non-asymptotic probability bounds on L2-errors for DNN estimators under stationary β-mixing data. We investigate the asymptotics of the total number of simple 4a+1-knots with Alexander polynomial of the form mt2+(1−2m)t+m for some m∈[−X,X]. Using Kearton and Levine's classification of simple knots, we give equivalent algebraic and arithmetic formulations of this counting question. In particular, thi…
tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier approximation and a recursive median sieve) could be automatically applied to time series classificatio…
Develops asymptotic theory for adversarial estimators.
problem Estimating unknown functions in machine learning and econometrics.
method Derives convergence rates and normality of A-estimators under various conditions.
result Normality of neural-net M-estimators, overcoming previous technical issues.
New algorithm improves knowledge transfer in dynamic decision-making.
problem Utilizing data from existing ventures to improve decision-making in new ventures.
method Proposes Transferred Fitted Q-Iteration algorithm for estimating optimal action-state function Q∗. result Significantly improved final learning error of Q∗ function. Study nonparametric density estimation via measure transport, achieving optimal rates.
problem Nonparametric density estimation with optimal rates.
method Measure transport, penalized maximum likelihood, and sieved wavelet estimators.
result Achieve minimax optimal convergence rates over Hölder classes of densities.
Optimizes treatment duration to maximize quality-adjusted lifetime.
problem Balancing risks and benefits in clinical decision making.
method Proposes a weighted estimating equation to adjust for confounding and informative censoring, and a nonparametric estimator for mean counterfactual quality-adjusted lifetime.
result Shows the optimal time for percutaneous endoscopic gastrostomy insertion in ALS patients.
The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…
Develops a new method for neural network significance testing without strict constraints.
problem Testing neural networks without bounded weights or specific architectural constraints.
method Uses Rademacher complexity bounds, weakened Sobolev space membership conditions, and a modified sieve space construction.
result Achieves optimal convergence rates and valid asymptotic distributions for test statistics.
DFIV uses deep features for IV regression, achieving optimal rates.
problem Optimal IV regression with deep features for complex target functions.
method Two-stage approach: deep feature learning followed by IV regression.
result DFIV achieves minimax optimal learning rate under certain conditions.
The paper proves consistency of neural networks with regularization.
problem Overfitting in neural networks with large scale data.
method Theoretical framework of neural networks with regularization, sieves method, and minimal neural networks theory.
result The estimated neural network converges to the true underlying function as sample size increases.
Study identifies and estimates treatment effect heterogeneity within principal stratification subpopulations.
problem Causal inference with intermediate outcomes and treatment effect heterogeneity.
method Proposes a novel doubly cross-fit doubly robust machine learner to efficiently learn conditional principal causal effects under principal ignorability.
result Demonstrates informative patterns of treatment effect heterogeneity within the always-survivor subpopulation in an acute lung injury trial.
Estimates personalized treatment response curves using covariates.
problem Flexible estimation of personalized treatment response curves.
method Sieve based nonparametric estimator of smoothed regimen-response curve function.
result Asymptotic linearity and undersmoothing criteria for efficient estimation.
Study exact minimax rates for density estimation over convex classes, extending previous work.
problem Deriving minimax rates for density estimation over convex density classes.
method Building on Le Cam's work, determine exact minimax rates using local metric entropy.
result Exact minimax rates derived for any convex density class, including nonparametric and parametric cases.
Let G:=SO(n,1)^\circ and Γbe a geometrically finite Zariski dense subgroup with critical exponent delta bigger than (n-1)/2. Under a spectral gap hypothesis on L^2(Γ\ G), which is always satisfied for delta>(n-1)/2 for n=2,3 and for delta>n-2 for n>= 4, we obtain an {\it effective} archimedean counting result for a dis…