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Found 14 papers, 1 papers with code
A Mean-Field Analysis of Neural Gradient Descent-Ascent: Applications to Functional Conditional Moment Equations
Under this regime, gradient descent-ascent corresponds to a Wasserstein gradient flow over the space of probability measures defined over the space of neural network parameters.
SGMM: Stochastic Approximation to Generalized Method of Moments
We introduce a new class of algorithms, Stochastic Generalized Method of Moments (SGMM), for estimation and inference on (overidentified) moment restriction models.
STEEL: Singularity-aware Reinforcement Learning
Batch reinforcement learning (RL) aims at leveraging pre-collected data to find an optimal policy that maximizes the expected total rewards in a dynamic environment.
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves
This paper considers general nonlinear sieve quasi-likelihood ratio (GN-QLR) based inference on expectation functionals of time series data, where the functionals of interest are based on some nonparametric function that satisfy conditional moment restrictions and are learned using multilayer neural networks.
Adaptive incomplete multi-view learning via tensor graph completion
With the advancement of the data acquisition techniques, multi-view learning has become a hot topic.
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision process with continuous states and actions.
Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators
We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics.
Adaptive Estimation and Uniform Confidence Bands for Nonparametric Structural Functions and Elasticities
The first is a data-driven choice of sieve dimension for a popular class of sieve two-stage least squares estimators.
Simple Adaptive Estimation of Quadratic Functionals in Nonparametric IV Models
This paper considers adaptive, minimax estimation of a quadratic functional in a nonparametric instrumental variables (NPIV) model, which is an important problem in optimal estimation of a nonlinear functional of an ill-posed inverse regression with an unknown operator.
Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders
This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial neural networks (ANNs) to approximate the unknown nuisance function of growing-dimensional confounders.
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions with high dimensional conditioning instruments.
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019
This study explores how people view and respond to the proposals of NYC congestion pricing evolve in time.
Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models
We propose a new adaptive hypothesis test for inequality (e. g., monotonicity, convexity) and equality (e. g., parametric, semiparametric) restrictions on a structural function in a nonparametric instrumental variables (NPIV) model.
Promoting carsharing attractiveness and efficiency: An exploratory analysis
Carsharing has grown significantly over recent years.
