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AI Blogs
FLIP: A Privacy-Preserving Mechanism for Time Series Data Authors: Tucker McElroy, Anindya Roy, Gaurab Hore; Published in Journal of Machine Learning Research, Volume 24, Issue 111, Pages 1-29, 2023. Abstract Ensuring privacy in released data is a crucial objective for…
[Submitted on 31 Aug 2023] Click here to download a PDF of the research paper titled “Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff” by Satoshi Suzuki and 6 other authors. Download PDF Abstract: This paper addresses the…
Metrizing Weak Convergence with Maximum Mean Discrepancies Authors: Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey; Volume 24, Issue 184, Pages 1-20, 2023. Abstract This paper investigates the maximum mean discrepancies (MMD) that can be used to measure the weak…
arXivLabs is a platform where collaborators can create and share new features directly on the arXiv website. Both individuals and organizations that collaborate with arXivLabs share our values of openness, community, excellence, and user data privacy. We only work with…
The Implicit Bias of Benign Overfitting By Ohad Shamir; Published in 2023; Volume 24, Issue 113: Pages 1-40 Abstract Benign overfitting, a phenomenon where a predictor perfectly fits noisy training data while achieving near-optimal expected loss, has gained significant attention…
Click-through rate (CTR) prediction is a crucial challenge in recommendation systems. Several public CTR datasets have emerged, but they have certain limitations. Firstly, existing datasets only include data from a single scenario and same type of items, while users usually…
Theoretical Equivalence of Trade-Off Curves for Statistical Proximity Assessment Rodrigue Siry, Ryan Webster, Loic Simon, Julien Rabin; 24(185):1−34, 2023. Abstract The development of quantitative measures to assess the proximity of two probability distributions has been accelerated by the emergence of…
The effectiveness of implicit neural networks in different tasks has been proven. However, there is a need for theoretical analysis to understand the connections and distinctions between implicit and explicit networks. This study focuses on high-dimensional implicit neural networks and…
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, Anru R. Zhang; 24(115):1−53, 2023. Abstract This paper presents a novel unsupervised learning approach that utilizes tensor decomposition to…
The detection of Extreme Mass Ratio Inspirals (EMRIs) is challenging due to their complex waveforms, long duration, and low signal-to-noise ratio (SNR). This makes them harder to identify compared to compact binary coalescences. While matched filtering techniques are computationally demanding,…
