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AI Blogs
A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition Masaru Ito, Zhaosong Lu, Chuan He; 24(166):1−34, 2023. Abstract This paper addresses a composite optimization problem that involves minimizing the sum of a weakly smooth function and a convex…
Ultrasound imaging is a crucial tool for diagnosing cervical lymph node lesions. However, the accuracy of these diagnoses heavily relies on the expertise of medical professionals, making the process prone to misdiagnoses. While deep learning has significantly improved the diagnoses…
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin; 24(77):1−42, 2023. Abstract This study explores the statistical properties of a likelihood approach to nonparametric estimation of a…
The focus of this paper is on the challenge of localizing data in a federated setting where the data is spread across multiple devices. This problem is complex due to the decentralized nature of federated environments and the presence of…
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo; 24(167):1−37, 2023. Abstract This study examines a broad range of bilevel problems, where the upper-level problem focuses on minimizing a smooth objective function…
Partial Label Learning (PLL) is a form of weakly supervised learning in which each training instance is assigned multiple candidate labels, but only one label is considered the true label. However, this assumption may not always hold true due to…
Approximate Post-Selective Inference for Regression with the Group LASSO Snigdha Panigrahi, Peter W MacDonald, Daniel Kessler; 24(79):1−49, 2023. Abstract In the absence of adjustments for selection bias, inference for the selected parameters after using the Group LASSO (or its generalized…
The trustworthiness of machine learning models in practical applications has been recently threatened by vulnerabilities to backdoor attacks. While it is commonly believed that not everyone can be an attacker due to the significant effort and extensive experimentation required to…
Inference on the Change Point in a High Dimensional Covariance Shift Authors: Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis, George Michailidis; Journal of Machine Learning Research, 24(168):1−68, 2023. Abstract This study addresses the problem of constructing confidence intervals for the change…
[Submitted on 31 Aug 2023] Download a PDF of the paper titled “Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness” by Jan Simson, Florian Pfisterer, and Christoph Kern…
