arXiv - STAT - Machine Learning最新文献_第5页

Think Twice Before You Act: Improving Inverse Problem Solving With MCMC 三思而后行：利用 MCMC 改进逆向问题的解决

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08551

Yaxuan Zhu, Zehao Dou, Haoxin Zheng, Yasi Zhang, Ying Nian Wu, Ruiqi Gao

{"title":"Think Twice Before You Act: Improving Inverse Problem Solving With MCMC","authors":"Yaxuan Zhu, Zehao Dou, Haoxin Zheng, Yasi Zhang, Ying Nian Wu, Ruiqi Gao","doi":"arxiv-2409.08551","DOIUrl":"https://doi.org/arxiv-2409.08551","url":null,"abstract":"Recent studies demonstrate that diffusion models can serve as a strong prior\u0000for solving inverse problems. A prominent example is Diffusion Posterior\u0000Sampling (DPS), which approximates the posterior distribution of data given the\u0000measure using Tweedie's formula. Despite the merits of being versatile in\u0000solving various inverse problems without re-training, the performance of DPS is\u0000hindered by the fact that this posterior approximation can be inaccurate\u0000especially for high noise levels. Therefore, we propose textbf{D}iffusion\u0000textbf{P}osterior textbf{MC}MC (textbf{DPMC}), a novel inference algorithm\u0000based on Annealed MCMC to solve inverse problems with pretrained diffusion\u0000models. We define a series of intermediate distributions inspired by the\u0000approximated conditional distributions used by DPS. Through annealed MCMC\u0000sampling, we encourage the samples to follow each intermediate distribution\u0000more closely before moving to the next distribution at a lower noise level, and\u0000therefore reduce the accumulated error along the path. We test our algorithm in\u0000various inverse problems, including super resolution, Gaussian deblurring,\u0000motion deblurring, inpainting, and phase retrieval. Our algorithm outperforms\u0000DPS with less number of evaluations across nearly all tasks, and is competitive\u0000among existing approaches.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty Estimation by Density Aware Evidential Deep Learning 通过密度感知证据深度学习进行不确定性估计

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08754

Taeseong Yoon, Heeyoung Kim

引用次数: 0

An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations 基于熵的水平集可视化不确定性建模测试与开发框架

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08445

Robert Sisneros, Tushar M. Athawale, David Pugmire, Kenneth Moreland

引用次数: 0

A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm 通过适当贝叶斯引导法进行聚类的贝叶斯方法：贝叶斯袋式聚类（BBC）算法

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08954

Federico Maria Quetti, Silvia Figini, Elena ballante

引用次数: 0

Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks 因果 GNN：网络中因果推理的 GNN 驱动型工具变量方法

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08544

Xiaojing Du, Feiyu Yang, Wentao Gao, Xiongren Chen

引用次数: 0

Sub-graph Based Diffusion Model for Link Prediction 基于子图的链接预测扩散模型

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08487

Hang Li, Wei Jin, Geri Skenderi, Harry Shomer, Wenzhuo Tang, Wenqi Fan, Jiliang Tang

{"title":"Sub-graph Based Diffusion Model for Link Prediction","authors":"Hang Li, Wei Jin, Geri Skenderi, Harry Shomer, Wenzhuo Tang, Wenqi Fan, Jiliang Tang","doi":"arxiv-2409.08487","DOIUrl":"https://doi.org/arxiv-2409.08487","url":null,"abstract":"Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary\u0000class of generative models with exceptional qualities in both synthesis and\u0000maximizing the data likelihood. These models work by traversing a forward\u0000Markov Chain where data is perturbed, followed by a reverse process where a\u0000neural network learns to undo the perturbations and recover the original data.\u0000There have been increasing efforts exploring the applications of DDPMs in the\u0000graph domain. However, most of them have focused on the generative perspective.\u0000In this paper, we aim to build a novel generative model for link prediction. In\u0000particular, we treat link prediction between a pair of nodes as a conditional\u0000likelihood estimation of its enclosing sub-graph. With a dedicated design to\u0000decompose the likelihood estimation process via the Bayesian formula, we are\u0000able to separate the estimation of sub-graph structure and its node features.\u0000Such designs allow our model to simultaneously enjoy the advantages of\u0000inductive learning and the strong generalization capability. Remarkably,\u0000comprehensive experiments across various datasets validate that our proposed\u0000method presents numerous advantages: (1) transferability across datasets\u0000without retraining, (2) promising generalization on limited training data, and\u0000(3) robustness against graph adversarial attacks.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Batch Ensemble for Variance Dependent Regret in Stochastic Bandits 随机匪帮中的方差依赖回退的批量合奏

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08570

Asaf CasselSchool of Computer Science, Tel Aviv University, Orin LevySchool of Computer Science, Tel Aviv University, Yishay MansourSchool of Computer Science, Tel Aviv UniversityGoogle Research, Tel Aviv

引用次数: 0

Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control 邻接匹配：用无记忆随机优化控制微调流动和扩散生成模型

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08861

Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen

引用次数: 0

CHARM: Creating Halos with Auto-Regressive Multi-stage networks CHARM：利用自动回归多级网络创建光环

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.09124

Shivam Pandey, Chirag Modi, Benjamin D. Wandelt, Deaglan J. Bartlett, Adrian E. Bayer, Greg L. Bryan, Matthew Ho, Guilhem Lavaux, T. Lucas Makinen, Francisco Villaescusa-Navarro

{"title":"CHARM: Creating Halos with Auto-Regressive Multi-stage networks","authors":"Shivam Pandey, Chirag Modi, Benjamin D. Wandelt, Deaglan J. Bartlett, Adrian E. Bayer, Greg L. Bryan, Matthew Ho, Guilhem Lavaux, T. Lucas Makinen, Francisco Villaescusa-Navarro","doi":"arxiv-2409.09124","DOIUrl":"https://doi.org/arxiv-2409.09124","url":null,"abstract":"To maximize the amount of information extracted from cosmological datasets,\u0000simulations that accurately represent these observations are necessary.\u0000However, traditional simulations that evolve particles under gravity by\u0000estimating particle-particle interactions (N-body simulations) are\u0000computationally expensive and prohibitive to scale to the large volumes and\u0000resolutions necessary for the upcoming datasets. Moreover, modeling the\u0000distribution of galaxies typically involves identifying virialized dark matter\u0000halos, which is also a time- and memory-consuming process for large N-body\u0000simulations, further exacerbating the computational cost. In this study, we\u0000introduce CHARM, a novel method for creating mock halo catalogs by matching the\u0000spatial, mass, and velocity statistics of halos directly from the large-scale\u0000distribution of the dark matter density field. We develop multi-stage neural\u0000spline flow-based networks to learn this mapping at redshift z=0.5 directly\u0000with computationally cheaper low-resolution particle mesh simulations instead\u0000of relying on the high-resolution N-body simulations. We show that the mock\u0000halo catalogs and painted galaxy catalogs have the same statistical properties\u0000as obtained from $N$-body simulations in both real space and redshift space.\u0000Finally, we use these mock catalogs for cosmological inference using\u0000redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics\u0000using simulation-based inference, performing the first inference with\u0000accelerated forward model simulations and finding unbiased cosmological\u0000constraints with well-calibrated posteriors. The code was developed as part of\u0000the Simons Collaboration on Learning the Universe and is publicly available at\u0000url{https://github.com/shivampcosmo/CHARM}.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fair CoVariance Neural Networks 公平共方差神经网络

arXiv - STAT - Machine Learning Pub Date : 2024-09-13 DOI: arxiv-2409.08558

Andrea Cavallo, Madeline Navarro, Santiago Segarra, Elvin Isufi

{"title":"Fair CoVariance Neural Networks","authors":"Andrea Cavallo, Madeline Navarro, Santiago Segarra, Elvin Isufi","doi":"arxiv-2409.08558","DOIUrl":"https://doi.org/arxiv-2409.08558","url":null,"abstract":"Covariance-based data processing is widespread across signal processing and\u0000machine learning applications due to its ability to model data\u0000interconnectivities and dependencies. However, harmful biases in the data may\u0000become encoded in the sample covariance matrix and cause data-driven methods to\u0000treat different subpopulations unfairly. Existing works such as fair principal\u0000component analysis (PCA) mitigate these effects, but remain unstable in low\u0000sample regimes, which in turn may jeopardize the fairness goal. To address both\u0000biases and instability, we propose Fair coVariance Neural Networks (FVNNs),\u0000which perform graph convolutions on the covariance matrix for both fair and\u0000accurate predictions. Our FVNNs provide a flexible model compatible with\u0000several existing bias mitigation techniques. In particular, FVNNs allow for\u0000mitigating the bias in two ways: first, they operate on fair covariance\u0000estimates that remove biases from their principal components; second, they are\u0000trained in an end-to-end fashion via a fairness regularizer in the loss\u0000function so that the model parameters are tailored to solve the task directly\u0000in a fair manner. We prove that FVNNs are intrinsically fairer than analogous\u0000PCA approaches thanks to their stability in low sample regimes. We validate the\u0000robustness and fairness of our model on synthetic and real-world data,\u0000showcasing the flexibility of FVNNs along with the tradeoff between fair and\u0000accurate performance.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0