arXiv research

Paper compares Bayesian and de-biased estimators for low-rank matrix completion.

New method for estimating treatment effects without complex propensity models.

Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenarios has led to interesting advancement, it has not been able to tell if models are relying on dataset biases as shortcuts for successful predi…

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new gene…

The Whittle likelihood is a widely used and computationally efficient pseudo-likelihood. However, it is known to produce biased parameter estimates for large classes of models. We propose a method for de-biasing Whittle estimates for second-order stationary stochastic processes. The de-biased Whittle likelihood can be …

Framework for ensuring fairness in machine learning models across multiple groups.

Novel characterization of augmented balancing weights combining outcome and weighting models.

A distributed bootstrap method for high-dimensional data reduces communication rounds efficiently.

We study high-dimensional Gaussian mixture classification using statistical physics methods.

Synthetic control method improves policy evaluation in high-dimensional settings.

New methods for estimating treatment effects with missing data.

We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension $p$ can grow exponentially fast with the sample size $n$. Our method combines the de-biasing technique with the composite quantile function to construct an estimator that …

Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be g…

New method constructs confidence bands for ODE models with unknown regulatory effects.

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular f…

Proposes FARM model combining latent factor and sparse regression.

Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high…

Develops methods for statistical inference in high-dimensional linear mixed models.

Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained estimates and how to perform statistical inference on the unknown mat…

Paper addresses eigenvector perturbation in small eigen-gap scenarios.

The paper develops inference methods for high-dimensional multi-task regression with row-sparse coefficients.

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual regression coefficients, avera…

Estimates social network structure from random walk subgraphs.

This paper studies schemes to de-bias the Lasso in a linear model $y=Xβ+ε$ where the goal is to construct confidence intervals for $a_0^Tβ$ in a direction $a_0$, where $X$ has iid $N(0,Σ)$ rows. We show that previously analyzed propositions to de-bias the Lasso require a modification in order to enjoy efficiency in a f…

A new method corrects bias in high-dimensional ridge regression.

The paper develops statistical inference methods for SHAP values.

Anonymizing company names in financial news improves trading performance, contrary to initial expectations.

This paper examines AI and ML bias and fairness issues.

Estimates nonparametric densities from mixed samples.

New method improves online covariance estimation for SGD.

FairMixRep learns fair representations from mixed data types.

Reference class forecasting is a method to remove optimism bias and strategic misrepresentation in infrastructure projects and programmes. In 2012 the Hong Kong government's Development Bureau commissioned a feasibility study on reference class forecasting in Hong Kong - a first for the Asia-Pacific region. This study …

Neural Empirical Bayes estimates source distributions from noisy simulations.

This note studies a method for the efficient estimation of a finite number of unknown parameters from linear equations, which are perturbed by Gaussian noise. In case the unknown parameters have only few nonzero entries, the proposed estimator performs more efficiently than a traditional approach. The method consists o…

New method reduces bias in NLI models using ensemble adversarial training.

Develops NFCF to reduce gender bias in social media recommendation systems.

This paper explores how NLP enhances insurance data analysis.

We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator. Two types of oracle inequalities are derived for the NBR's elastic-net estimates by using the Compatibility Factor Condition and the Stabil Condition. The second type of oracl…

Large, pre-trained generative models have been increasingly popular and useful to both the research and wider communities. Specifically, BigGANs a class-conditional Generative Adversarial Networks trained on ImageNet---achieved excellent, state-of-the-art capability in generating realistic photos. However, fine-tuning …

Optimizer memory affects learning rate sensitivity in shuffle order, impacting fine-tuning noise.

Low-rank matrix regression refers to the instances of recovering a low-rank matrix based on specially designed measurements and the corresponding noisy outcomes. In the last decade, numerous statistical methodologies have been developed for efficiently recovering the unknown low-rank matrices. However, in some applicat…

We consider the estimation and inference of graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kron…

This paper develops robust confidence intervals in high-dimensional and left-censored regression. Type-I censored regression models are extremely common in practice, where a competing event makes the variable of interest unobservable. However, techniques developed for entirely observed data do not directly apply to the…

Paper analyzes singular subspace estimation in noisy matrix models.

The paper reviews machine learning safety techniques for autonomous vehicles.

We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task,…

Optimal Transport enhances machine learning with new methods.

The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to b…