arXiv research

A new method generates counterfactual treatment outcomes for time-varying treatments.

Maximum Likelihood Estimation (MLE) is the bread and butter of system inference for stochastic systems. In some generality, MLE will converge to the correct model in the infinite data limit. In the context of physical approaches to system inference, such as Boltzmann machines, MLE requires the arduous computation of pa…

Proposes DSW for unbiased ITE estimation with dynamic confounders.

The paper analyzes how re-weighting helps in reducing variance in high-dimensional kernel methods under covariate shifts.

In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases inclu…

Self-paced learning and hard example mining re-weight training instances to improve learning accuracy. This paper presents two improved alternatives based on lightweight estimates of sample uncertainty in stochastic gradient descent (SGD): the variance in predicted probability of the correct class across iterations of …

Innovative game theory approach optimizes survival analysis metrics.

Estimates calibration error under label shift without labels.

Optimal insurance minimizes ruin probability with non-decreasing functions.

The paper proposes AIS for Bayesian inversion of multioutput signals with covariance estimation.

In this paper, we develop a general theory of truncated inverse binomial sampling. In this theory, the fixed-size sampling and inverse binomial sampling are accommodated as special cases. In particular, the classical Chernoff-Hoeffding bound is an immediate consequence of the theory. Moreover, we propose a rigorous and…

Inverse classification, the process of making meaningful perturbations to a test point such that it is more likely to have a desired classification, has previously been addressed using data from a single static point in time. Such an approach yields inflated probability estimates, stemming from an implicitly made assum…

In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning…

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-awa…

Improved GAN training stability through clipping and reweighting.

Proposes a new method to handle data heterogeneity in causal inference.

Study shows refugee matching gains are robust to different evaluation methods.

This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on. Specifically, an Iteratively Re-Weighted method (IRW) with so…

New nonparametric estimators improve causal effect estimation.

Paper explores stability, regularization, and gradient flows for stochastic inverse problems.

Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the current theoretical analysis for non-convex algorithms relies heavily on the assumption that every entry is observed with exactly the same pro…

Paper examines stability of Bayesian posterior measures using integral probability metrics.

Generative Adversarial Network model for class-imbalanced tabular data.

New methods for $Z$-transform inversion and Wiener-Hopf factorization.

Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…

Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliar…

The paper analyzes Tikhonov regularization in Hilbert scales for statistical inverse problems.

The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many reward functions, and expert demonstrations may be optimal for man…

This work proposes a new algorithm for training a re-weighted L2 Support Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Candès et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In particular, the margin required for each training vector is set independently, defining a n…

M2m method improves deep learning performance on class-imbalanced datasets.

Bayesian method refines surrogate models for accurate full waveform inversion.

New method makes CP intervals locally adaptive using trainable transformations.

Bayesian method for estimating inputs leading to specific probability outputs.

Researchers analyze inverse optimal transport, deriving theoretical and empirical insights.

Proposes stabilized weights for causal inference using isotonic calibration.

PPI uses predictions and weighting to infer from partially labeled data.

Study designs for estimating treatment effects in adaptive experiments.

DisCor corrects reinforcement learning issues by re-weighting collected data.

Estimates causal contributions of multiple causes on outcome changes.

The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often involve sensitive information, thus far there has been no work on privacy-preserving IPW methods. We add…

This study proposes an efficient surrogate for Darcy flow inverse problems.

Estimates inverse temperature of Ising models with a single sample.

New method uses deep learning to solve linear inverse problems.

It has long been agreed by academics that the inversion method is the method of choice for generating random variates, given the availability of the quantile function. However for several probability distributions arising in practice a satisfactory method of approximating these functions is not available. The main focu…

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematical formulation of how this bias can arise. We do so by assuming the existence of underlying, unknown, and unbiased labels which are overwritt…

New estimator for causal effects in large datasets.

The level crossing and inverse statistics analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, $ν_α^+$, where $T_α =1/ν_α^+$ is the average waiting time for observing the level $α$ again. We estimate the probability $P(K, α)$, which provides us the probab…

The paper proposes an efficient method for estimating ATEs using adaptive experiments.