2期

最新文章

• Prediction and outlier detection in classification problems AI翻译
  作者：Guan, Leying; Tibshirani, Robert
  作者单位：Yale University; Stanford University
  摘要：We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction sets). BCOPS constructs a prediction set C(x) as a subset of class labels, possibly empty. It tries to optimize the out-of-sample performance, aiming to include the correct class and to detect outliers x as often as possible. BCOPS returns no prediction (corresponding to C(x) equ...
• Synthetic controls with staggered adoption AI翻译
  作者：Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse
  作者单位：Harvard University; University of California System; University of California Berkeley
  摘要：Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units that closely balances the treated unit's pre-treatment outcomes. In this paper, we generalize SCM, originally designed to study a single treated unit,...
• Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment-free effect models AI翻译
  作者：Mo, Weibin; Liu, Yufeng
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, researchers can search for the optimal individualized treatment rule (ITR) that maximizes the expected outcome. Existing methods typically require initial estimation of some nuisance models. The double robustness property that can protect from misspecification of either the treatment-free effect or the propensit...
• Non-reversible parallel tempering: A scalable highly parallel MCMC scheme AI翻译
  作者：Syed, Saifuddin; Bouchard-Cote, Alexandre; Deligiannidis, George; Doucet, Arnaud
  作者单位：University of British Columbia; University of Oxford
  摘要：Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to sample complex high-dimensional probability distributions. They rely on a collection of N interacting auxiliary chains targeting tempered versions of the target distribution to improve the exploration of the state space. We provide here a new perspective on these highly parallel algorithms and their tuning by identifying and formalizing a sharp divide in the behaviour and performance of reversible v...
• Model identification via total Frobenius norm of multivariate spectra AI翻译
  作者：McElroy, Tucker S.; Roy, Anindya
  作者单位：University System of Maryland; University of Maryland Baltimore County
  摘要：We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density-this is more demanding than Kullback-Leibler discrepancy, which is instead related to one-step ahead forecasting performance. We develop new asymptotic results for linear and quadratic functionals of the periodogram, and make two applications ...
• A kernel-expanded stochastic neural network AI翻译
  作者：Sun, Yan; Liang, Faming
  作者单位：Purdue University System; Purdue University
  摘要：The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so-called kernel-expanded stochastic neural network (K-StoNet) model, which incorporates support vector regression as the first hidden layer and reformulates the neural network as a latent variable model. The former maps the input vector into an infini...
• On efficient dimension reduction with respect to the interaction between two response variables AI翻译
  作者：Luo, Wei
  作者单位：Zhejiang University
  摘要：In this paper, we propose the novel theory and methodologies for dimension reduction with respect to the interaction between two response variables, which is a new research problem that has wide applications in missing data analysis, causal inference, graphical models, etc. We formulate the parameters of interest to be the locally and the globally efficient dimension reduction subspaces, and justify the generality of the corresponding low-dimensional assumption. We then construct estimating eq...
• The Barker proposal: Combining robustness and efficiency in gradient-based MCMC AI翻译
  作者：Livingstone, Samuel; Zanella, Giacomo
  作者单位：University of London; University College London; Bocconi University; Bocconi University
  摘要：There is a tension between robustness and efficiency when designing Markov chain Monte Carlo (MCMC) sampling algorithms. Here we focus on robustness with respect to tuning parameters, showing that more sophisticated algorithms tend to be more sensitive to the choice of step-size parameter and less robust to heterogeneity of the distribution of interest. We characterise this phenomenon by studying the behaviour of spectral gaps as an increasingly poor step-size is chosen for the algorithm. Moti...
• Gaussian prepivoting for finite population causal inference AI翻译
  作者：Cohen, Peter L.; Fogarty, Colin B.
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：In finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, hypotheses which impute the missing potential outcomes. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject-specific treatment effects unspecified. Tests valid for sharp null hypotheses can be anti-conservative should only the weak null hold. We develop a general framework for u...
• Graph based Gaussian processes on restricted domains AI翻译
  作者：Dunson, David B.; Wu, Hau-Tieng; Wu, Nan
  作者单位：Duke University; Duke University
  摘要：In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian process (GP) models, proposing a new class of Graph Laplacian based GPs (GL-GPs), which learn a covariance that respects the geometry of the inp...