论文检索

样本选择模型的双重机器学习AI 解读

This article considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly…

机器学习对因果推断的增量价值：来自既有研究再分析的证据AI 解读

Summary A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal…

深度非线性充分降维AI 解读

Linear sufficient dimension reduction, as exemplified by sliced inverse regression, has seen substantial development in the past thirty years. However, with the advent of more complex scenarios, nonlinear dimension reduction has gained considerable interest recently. This paper introduces a novel method for nonlinear sufficient dimension reduction, utilizing the generalized martingale difference divergence measure in conjunction with deep neural networks. The optimal…

高维预测回归中的 LASSOAI 解读

This paper examines LASSO, a widely-used L1-penalized regression method, in high dimensional linear predictive regressions, particularly when the number of potential predictors exceeds the sample size and numerous unit root regressors are present. The consistency of LASSO is contingent upon two key components: the deviation bound of the cross product of the regressors and the error term, and the restricted eigenvalue of the Gram matrix. We present new probabilistic bounds…

深度 Kronecker 网络AI 解读

Summary We develop a novel framework for the analysis of medical imaging data, including magnetic resonance imaging, functional magnetic resonance imaging, computed tomography and more. Medical imaging data differ from general images in two main aspects: (i) the sample size is often considerably smaller and (ii) the interpretation of the model is usually more crucial than predicting the outcome. As a result, standard methods such as convolutional neural networks cannot be…

双重稳健泛函的选择性机器学习AI 解读

Summary While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function and several candidate machine…

核插值的泛化表现不佳AI 解读

Summary One of the most interesting problems in the recent renaissance of the studies in kernel regression might be whether kernel interpolation can generalize well, since it may help us understand the ‘benign overfitting phenomenon’ reported in the literature on deep networks. In this paper, under mild conditions, we show that, for any ε>0, the generalization error of kernel interpolation is lower bounded by Ω(n−ε). In other words, the kernel interpolation generalizes…

关于“通过深度学习自动检测时间序列变点”讨论的作者回复AI 解读

Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network. Our…

Papp、Fearnhead 与 Sherlock 对机器学习概率统计讨论会的评论AI 解读

We provide comments only on the read paper “From Denoising Diffusions to Denoising Markov Models” by Benton, Shi, De Bortoli, Deligiannidis and Doucet. We congratulate the authors on an important contribution to the exciting area of generative models: providing a simple framework for applying the idea of diffusion generative models to data defined on general spaces and for general noising processes. Diffusion models have shown remarkable empirical performance for several…

基于深度学习的时间序列自动变点检测AI 解读

Abstract Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. We show how to automatically generate new offline detection methods based on training a neural network.…

van Lieshout 与 Lu 对机器学习概率统计讨论会论文的讨论意见AI 解读

We congratulate Professors Li, Fearnhead, Fryzlewics, and Wang on their fine work that represents classic test statistics for change-point detection as a neural network-based classifier and develops improved offline detection algorithms for historical, labelled data. The paper focuses on real-valued observation vectors in a temporal regression model with training data being either labelled historical data or obtained by simulation from a model. In many applications, though,…

Cribben 与 Anastasiou 对机器学习概率统计讨论会论文的评论AI 解读

We extend our congratulations to the authors for their innovative contribution. Here, we address three key points: change-point labelling, handling imbalanced datasets, and computational complexity. Change-point labelling in datasets has many flaws and challenges. The primary flaw is the subjectivity inherent in the task as it often relies on human judgement. This subjectivity introduces biases and inconsistencies. One challenge is the lack of a universally accepted standard…

Hernando Ombao 对机器学习概率与统计讨论会论文的评论AI 解读

I congratulate the authors for their fundamental contribution that demonstrates how the existing tests for change-points can be represented by a neural network. The major consequence is that with sufficient data, the performance of these neural networks should be at par with these existing tests. This formulation is very insightful and inspired me to revisit the previous work on models for non-stationary time series that use time-localized representations such as wavelets,…

Yingnian Wu 与 Weng Kee Wong 对机器学习概率与统计讨论会的评论AI 解读

We thank the authors for the interesting and timely article. Our commentaries are as follows: From equilibrium Langevin dynamics to non-equilibrium ordinary and stochastic differential equations. We wish to add a little background explanation, with no claim of originality, to this deep and elegant paper that greatly expands the scope of diffusion/score-based models. The diffusion-based model can be viewed as a refined variational auto-encoder and a non-equilibrium Langevin…

Gilbert MacKenzie 对机器学习概率与统计讨论会的评论AI 解读

I would like first to congratulate both authors on presenting their very stimulating work in the spirit of the conference’s theme of statisticians and machine learners working in alliance. There is no doubt in my mind that collaboration will lead to better, more parsimonious, models. My comments relate, mainly, to the second discussant’s points on over-parametrization. If a feed-forward neural net (Ripley, 1994) is denoted by Net(x,θ)⁠, where x is the input layer and ϕ is…

Gilliot 等对机器学习概率与统计讨论会的讨论意见：扩散模型的隐含分布约束AI 解读

We would like to thank the authors for their interesting contribution and the clarification brought on a particular aspect of diffusion models concerned with the criterion used in the fitting phase of the procedure. Another aspect, not discussed extensively, is the choice of the neural network used to model the gradients involved in the generative phase. In particular, the implicit prior information this induces on the class of distributions considered may seem mysterious…

洪永淼、Oliver Linton、孙佳静与朱美婷对机器学习概率与统计讨论会的评论AI 解读

This paper proposes a neural network-based approach for automating offline change-point detection. The authors show that cumulative sum (CUSUM) and generalized CUSUM are a special case of their neural network class. They emphasize misclassification error rates and their theoretical contribution is to establish some elegant results for these under i.i.d. unit variance Gaussian data with a possible change in mean. Their theoretical results outline the conditions on the…

Kanti Mardia 对“机器学习的概率与统计方面”讨论会论文的评论AI 解读

I enjoyed reading this paper which has so many outstanding features; this is where Modern Statistics is moving—high-level computing with sound mathematical statistical. My comments are as follows. The authors use the wrapped normal on SO(3) using the exponential map and I have some reservations related to this choice since the most common distribution used on SO(3) is the Fisher matrix distribution which has many desirable properties (see, e.g. Mardia & Jupp, 2000) whereas…

Andi Wang 对“机器学习概率与统计方面讨论会”的评论AI 解读

I would like to congratulate the authors for this excellent article, which includes both careful theoretical developments at the foundations of such denoising models and exciting methodological innovations and experiments. Of course, there are still many unanswered questions in relation to these denoising Markov models. One of the most tantalizing such questions is touched on by the authors in their concluding discussion, namely the nature of the implicit biases which are…

Shakeel Gavioli-Akilagun 对机器学习概率与统计讨论会论文的评论AI 解读

We congratulate the authors for the interesting and thought provoking paper. Lemma 3.2 shows that the generalized likelihood ratio (GLR) test for stability of a linear regression can be viewed as a simple feed forward neural network. However, close inspection of the lemma reveals that the setup rules out several common change point problems. Consider the piecewise polynomial regression: For ξ ’s distributed i.i.d. N(0,1)⁠, the likelihood ratio statistic for a change at…

关于“机器学习的概率与统计方面”讨论会的致谢提议与讨论意见AI 解读

Computational statistics and machine learning (ML) are closely related, and there are many opportunities for cross-fertilization of ideas between the two fields. Both can benefit from greater interaction, and the two papers being discussed here highlight some ways that this can happen. The main focus of this paper is offline detection of a single change-point using labelled training data. Interest is in the automatic generation of new offline detection methods using neural…

检验全局原假设的集成方法AI 解读

Abstract Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning…

利用深度学习将天气动态纳入保险理赔分流AI 解读

In property insurance claims triage, insurers often use static information to assess the severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of weather conditions throughout the course of a loss event is predictive of the insured losses, and hence appropriate use of weather dynamics improves the operation of insurers’ claim management. To test this hypothesis, we propose a deep learning method to incorporate dynamic weather information…

利用机器学习推断处理特定生存曲线AI 解读

In the absence of data from a randomized trial, researchers may aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome. In this context, interest often focuses on the treatment-specific survival curves, that is, the survival curves were the population under study to be assigned to receive the treatment or not. Under certain conditions, including that all confounders of the treatment-outcome relationship are observed,…