arXiv:2502.09986v4 Announce Type: replace-cross Abstract: Getting tools that allow simple representations and comparisons of a set of categorical trajectories is of major interest for statisticians. Without loosing any information, we associate to each state a binary random indicator function, taking values in ${0,1}$, and turn the problem of statistical description of the categorical trajectories into a multivariate functional principal components analysis. This viewpoint encompasses experimental frameworks where two or more states can be observed simultaneously. The sample paths […]

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arXiv:2604.07506v1 Announce Type: new Abstract: Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment quality of Large Language Models (LLMs). Recently, Generative Reward Models (GRMs) have emerged as a superior paradigm, offering higher interpretability and stronger generalization than traditional scalar RMs. However, existing methods for GRMs focus primarily on outcome-level supervision, neglecting analytical process quality, which constrains their potential. To address this, we propose ReflectRM, a novel GRM […]

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Binary Waring decomposition seeks to express a homogeneous binary form as a minimal sum of powers of linear forms. In the binary setting, Sylvester’s theorem gives a classical algebraic route for rank determination and parameter recovery through structured Hankel/catalecticant matrices. Although this procedure is exact and interpretable in ideal arithmetic, practical rank identification may become unstable when the input coefficients are contaminated by noise or when the underlying roots are close to degenerate configurations. This paper develops a […]

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arXiv:2511.21140v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as scalable evaluators of model responses in lieu of human annotators. However, imperfect sensitivity and specificity of LLM judgments induce bias in naive evaluation scores. We propose a simple plug-in framework that corrects this bias and constructs confidence intervals accounting for uncertainty from both the test dataset and a human-evaluated calibration dataset, enabling statistically sound and practical LLM-based evaluation. Building on this framework, we introduce an […]

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So I am currently pursuing my undergrad and want to create an adaptive honeypot using RL(specifically DQN) and Cowrie Honeypot as my project. But I don’t have any idea on how to start or what to do and not do. I have beginner level knowledge of Q-Learning and Deep Q-Learning. Any help will be appreciated… submitted by /u/dn874 [link] [comments]

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arXiv:2505.03797v2 Announce Type: replace-cross Abstract: Partial Bayesian neural networks (pBNNs) have been shown to perform competitively with fully Bayesian neural networks while only having a subset of the parameters be stochastic. Using sequential Monte Carlo (SMC) samplers as the inference method for pBNNs gives a non-parametric probabilistic estimation of the stochastic parameters, and has shown improved performance over parametric methods. In this paper we introduce a new SMC-based training method for pBNNs by utilising a guided proposal and […]

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Generative models have made it possible to synthesize highly realistic images, potentially providing an abundant data source for training machine learning models. Despite the advantages of these synthesizable data sources, the indiscriminate use of generated images as real images for training can even cause mode collapse due to modality discrepancies between real and synthetic domains. In this paper, we propose a novel framework for discriminative use of generated images, coined GMAIL, that explicitly treats generated images as a […]

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