Learning and Sampling of Atomic Interventions from Observations
National University of Singapore（新加坡国立大学）；Cornell University（康奈尔大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）
Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, N. V. Vinodchandran
备注：
26 pages, 4 figures, a version appeared in ICML 2020
We study the problem of efficiently estimating the effect of an intervention on a single variable (atomic interventions) using observational samples in a causal Bayesian network. Our goal is to give algorithms that are efficient in both time and sample complexity in a non-parametric setting.
Tian and Pearl (AAAI `02) have exactly characterized the class of causal graphs for which causal effects of atomic interventions can be identified from observational data. We make their result quantitative. Suppose P is a causal model on a set $\vec{V}$ of n observable variables with respect to a given causal graph G with observable distribution $P$. Let $P_x$ denote the interventional distribution over the observables with respect to an intervention of a designated variable X with x. Assuming that $G$ has bounded in-degree, bounded c-components ($k$), and that the observational distribution is identifiable and satisfies certain strong positivity condition, we give an algorithm that takes $m=\tilde{O}(nε^{-2})$ samples from $P$ and $O(mn)$ time, and outputs with high probability a description of a distribution $\hat{P}$ such that $d_{\mathrm{TV}}(P_x, \hat{P}) \leq ε$, and:
1. [Evaluation] the description can return in $O(n)$ time the probability $\hat{P}(\vec{v})$ for any assignment $\vec{v}$ to $\vec{V}$
2. [Generation] the description can return an iid sample from $\hat{P}$ in $O(n)$ time.
We also show lower bounds for the sample complexity showing that our sample complexity has an optimal dependence on the parameters $n$ and $ε$, as well as if $k=1$ on the strong positivity parameter.
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Sharif University of Technology（谢里夫理工大学）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
Generative Intervention Models for Causal Perturbation Modeling
Technical University of Munich（慕尼黑技术大学）；Department of Computer Science, ETH Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；Munich Center for Machine Learning, Germany（慕尼黑机器学习中心）
T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detection
Department of Computer Science, University of Central Florida（计算机科学系，中央佛罗里达大学）；Department of Otolaryngology Head Neck Surgery, University of Cincinnati College of Medicine（耳鼻喉科与头颈外科系，辛辛那提大学医学院）；Department of Internal Medicine, University of Central Florida College of Medicine（内科系，中央佛罗里达大学医学院）
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Samsung Advanced Institute of Technology (SAIT)（三星先进技术研究院）；Korea Advanced Institute of Science & Technology (KAIST)（韩国科学技术院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
School of Computer and Communication Sciences, EPFL（洛桑联邦理工学院计算机与通信科学学院）；Borealis AI（Borealis人工智能公司）；College of Management of Technology, EPFL（洛桑联邦理工学院技术管理学院）；Department of Computer Science, TUM（慕尼黑工业大学计算机科学系）
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Princeton University（普林斯顿大学）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）；CAEP Software Center for High Performance Numerical Simulation（中国工程物理研究院高性能数值模拟软件中心）；Beijing Institute of Big Data Research（北京大数据研究院）
BFTS: Thompson Sampling with Bayesian Additive Regression Trees
Department of Statistics and Data Science, National University of Singapore（统计与数据科学系，国立新加坡大学）；Centre for Biomedical Data Science, Duke-NUS Medical School（生物医学数据科学中心，杜克-新加坡国立大学医学院）；Department of Biostatistics and Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Department of Statistics and Data Science, Washington University in St. Louis（统计与数据科学系，圣路易斯华盛顿大学）
Selective Safety Steering via Value-Filtered Decoding
Department of Electrical and Computer Engineering, Technion IIT（技术学院电气与计算机工程系）；Department of Statistics, University of Oxford（牛津大学统计系）；OATML, Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Department of Computer Science, Technion IIT（技术学院计算机科学系）
