arXiv - STAT - Machine Learning最新文献_第7页

Automated Discovery of Pairwise Interactions from Unstructured Data 从非结构化数据中自动发现配对交互作用

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07594

ZuhengDavid, Xu, Moksh Jain, Ali Denton, Shawn Whitfield, Aniket Didolkar, Berton Earnshaw, Jason Hartford

{"title":"Automated Discovery of Pairwise Interactions from Unstructured Data","authors":"ZuhengDavid, Xu, Moksh Jain, Ali Denton, Shawn Whitfield, Aniket Didolkar, Berton Earnshaw, Jason Hartford","doi":"arxiv-2409.07594","DOIUrl":"https://doi.org/arxiv-2409.07594","url":null,"abstract":"Pairwise interactions between perturbations to a system can provide evidence\u0000for the causal dependencies of the underlying underlying mechanisms of a\u0000system. When observations are low dimensional, hand crafted measurements,\u0000detecting interactions amounts to simple statistical tests, but it is not\u0000obvious how to detect interactions between perturbations affecting latent\u0000variables. We derive two interaction tests that are based on pairwise\u0000interventions, and show how these tests can be integrated into an active\u0000learning pipeline to efficiently discover pairwise interactions between\u0000perturbations. We illustrate the value of these tests in the context of\u0000biology, where pairwise perturbation experiments are frequently used to reveal\u0000interactions that are not observable from any single perturbation. Our tests\u0000can be run on unstructured data, such as the pixels in an image, which enables\u0000a more general notion of interaction than typical cell viability experiments,\u0000and can be run on cheaper experimental assays. We validate on several synthetic\u0000and real biological experiments that our tests are able to identify interacting\u0000pairs effectively. We evaluate our approach on a real biological experiment\u0000where we knocked out 50 pairs of genes and measured the effect with microscopy\u0000images. We show that we are able to recover significantly more known biological\u0000interactions than random search and standard active learning baselines.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206610","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

Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks 连续时间随机梯度下降的收敛性及其在线性深度神经网络中的应用

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07401

Gabor Lugosi, Eulalia Nualart

引用次数: 0

Exploring User-level Gradient Inversion with a Diffusion Prior 利用扩散先验探索用户级梯度反演

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07291

Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Bradley Malin, Kieran Parsons, Ye Wang

引用次数: 0

Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization 通过隐含正则化实现无调整在线稳健主成分分析

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07275

Lakshmi Jayalal, Gokularam Muthukrishnan, Sheetal Kalyani

引用次数: 0

Reranking Laws for Language Generation: A Communication-Theoretic Perspective 语言生成的重新排序法则：传播理论视角

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07131

António Farinhas, Haau-Sing Li, André F. T. Martins

{"title":"Reranking Laws for Language Generation: A Communication-Theoretic Perspective","authors":"António Farinhas, Haau-Sing Li, André F. T. Martins","doi":"arxiv-2409.07131","DOIUrl":"https://doi.org/arxiv-2409.07131","url":null,"abstract":"To ensure large language models (LLMs) are used safely, one must reduce their\u0000propensity to hallucinate or to generate unacceptable answers. A simple and\u0000often used strategy is to first let the LLM generate multiple hypotheses and\u0000then employ a reranker to choose the best one. In this paper, we draw a\u0000parallel between this strategy and the use of redundancy to decrease the error\u0000rate in noisy communication channels. We conceptualize the generator as a\u0000sender transmitting multiple descriptions of a message through parallel noisy\u0000channels. The receiver decodes the message by ranking the (potentially\u0000corrupted) descriptions and selecting the one found to be most reliable. We\u0000provide conditions under which this protocol is asymptotically error-free\u0000(i.e., yields an acceptable answer almost surely) even in scenarios where the\u0000reranker is imperfect (governed by Mallows or Zipf-Mandelbrot models) and the\u0000channel distributions are statistically dependent. We use our framework to\u0000obtain reranking laws which we validate empirically on two real-world tasks\u0000using LLMs: text-to-code generation with DeepSeek-Coder 7B and machine\u0000translation of medical data with TowerInstruct 13B.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206643","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

From optimal score matching to optimal sampling 从最佳分数匹配到最佳抽样

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07032

Zehao Dou, Subhodh Kotekal, Zhehao Xu, Harrison H. Zhou

{"title":"From optimal score matching to optimal sampling","authors":"Zehao Dou, Subhodh Kotekal, Zhehao Xu, Harrison H. Zhou","doi":"arxiv-2409.07032","DOIUrl":"https://doi.org/arxiv-2409.07032","url":null,"abstract":"The recent, impressive advances in algorithmic generation of high-fidelity\u0000image, audio, and video are largely due to great successes in score-based\u0000diffusion models. A key implementing step is score matching, that is, the\u0000estimation of the score function of the forward diffusion process from training\u0000data. As shown in earlier literature, the total variation distance between the\u0000law of a sample generated from the trained diffusion model and the ground truth\u0000distribution can be controlled by the score matching risk. Despite the widespread use of score-based diffusion models, basic theoretical\u0000questions concerning exact optimal statistical rates for score estimation and\u0000its application to density estimation remain open. We establish the sharp\u0000minimax rate of score estimation for smooth, compactly supported densities.\u0000Formally, given (n) i.i.d. samples from an unknown (alpha)-H\"{o}lder\u0000density (f) supported on ([-1, 1]), we prove the minimax rate of estimating\u0000the score function of the diffused distribution (f * mathcal{N}(0, t)) with\u0000respect to the score matching loss is (frac{1}{nt^2} wedge\u0000frac{1}{nt^{3/2}} wedge (t^{alpha-1} + n^{-2(alpha-1)/(2alpha+1)})) for\u0000all (alpha > 0) and (t ge 0). As a consequence, it is shown the law\u0000(hat{f}) of a sample generated from the diffusion model achieves the sharp\u0000minimax rate (bE(dTV(hat{f}, f)^2) lesssim n^{-2alpha/(2alpha+1)}) for\u0000all (alpha > 0) without any extraneous logarithmic terms which are prevalent\u0000in the literature, and without the need for early stopping which has been\u0000required for all existing procedures to the best of our knowledge.","PeriodicalId":501340,"journal":{"name":"arXiv - STAT - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206635","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

Training-Free Guidance for Discrete Diffusion Models for Molecular Generation 分子生成离散扩散模型的免训练指导

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07359

Thomas J. Kerby, Kevin R. Moon

引用次数: 0

Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models 通过一致性模型对玻尔兹曼分布进行高效无偏采样

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07323

Fengzhe Zhang, Jiajun He, Laurence I. Midgley, Javier Antorán, José Miguel Hernández-Lobato

引用次数: 0

Manifold Learning via Foliations and Knowledge Transfer 通过对折和知识转移进行多元学习

arXiv - STAT - Machine Learning Pub Date : 2024-09-11 DOI: arxiv-2409.07412

E. Tron, E. Fioresi