arXiv research

Proposes a method to estimate causal effects of continuous treatments using instrumental variables.

Paper introduces new estimator for continuous treatment effects.

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are ei…

Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.

SHIFT improves robustness in estimating dose-response functions with heavy-tailed contamination.

Proposes a method for generating prediction intervals in dose-response models using conformal prediction.

Method bounds continuous-valued treatment effects when confounding variables are hidden.

Paper develops methods to estimate derivative of dose-response curve for continuous treatments.

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…

Proposes estimators for complex dose-response curves using kernel methods.

Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model …

Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by…

Proposes VCNet for estimating ADRFs of continuous treatments.

ContiVAE estimates individual dose-response curves from unobserved confounders using observational data.

Proposes a new method for estimating non-pathwise differentiable functional parameters.

Kernel methods identify treatment effects with unobserved confounding using negative controls.

Kernel method estimates long-term effects from short-term data.

Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.

Develops a scalable model for drug combination prediction in cancer.

Develops scalable methods to assess sensitivity and uncertainty in continuous treatment effects.

New method estimates extreme outcomes in heavy-tailed data, breaking circular dependence.

The literature of heavy tails (typically) starts with a random walk and finds mechanisms that lead to fat tails under aggregation. We follow the inverse route and show how starting with fat tails we get to thin-tails when deriving the probability distribution of the response to a random variable. We introduce a general…

New framework for managing medical risks using convex responses.

A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.

Proposes a new method to measure and avoid harm in machine learning decisions.

Mixed effects (ME) models inform a vast array of problems in the physical and social sciences, and are pervasive in meta-analysis. We consider ME models where the random effects component is linear. We then develop an efficient approach for a broad problem class that allows nonlinear measurements, priors, and constrain…

New theory for nonsmooth systems helps optimize and control complex functions.

Kernel ridge regression for causal inference with missing data.

Study Q-learning with averaging for reinforcement learning, proving efficient inference and error bounds.

GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.

New bounds for agnostic learning with average smoothness.

Improved bounds for Monte Carlo Rademacher Averages using self-bounding functions.

We present sufficient conditions for topological stability of continuous functions $f:\mathbb{R}\to\mathbb{R}$ having finitely many local extrema with respect to averagings by discrete measures with finite supports.

Gromov proposed an averaged version of the Dehn function and claimed that in many cases it should be subasymptotic to the Dehn function. Using results on random walks in nilpotent groups, we confirm this claim for most nilpotent groups. In particular, if a nilpotent group satisfies the isoperimetric inequality $δ(l)<Cl…

We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 f…

We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …

Improved TD learning with tail averaging and regularization achieves optimal convergence rates.

The definition of the covariant space-time averaging scheme for the objects (tensors, geometric objects, etc.) on differentiable metric manifolds with a volume n-form, which has been proposed for the formulation of macroscopic gravity, is analyzed. An overview of the space-time averaging procedure in Minkowski spacetim…

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the pro…

Under-parameterized networks can either copy or average teacher weights, leading to universal optimal solutions.

UCRL2-VTR achieves nearly optimal regret for learning MDPs with linear function approximation.

Efficient algorithm for global optimization of multivariate Lipschitz functions.

Kernel-based function approximation improves reinforcement learning performance.

This paper analyzes SGD with increasingly weighted averaging for optimization and generalization.

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…

We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distribute…

In this thesis, we consider the suitability of using the charged cold fluid model in the description of ultra-relativistic beams. The method that we have used is the following. Firstly, the necessary notions of kinetic theory and differential geometry of second order differential equations are explained. Then an averag…

The paper analyzes MACD using operator theory.