arXiv:2509.25783v5 Announce Type: replace Abstract: Understanding the geometry of the loss landscape near a minimum is key to explaining the implicit bias of gradient-based methods in non-convex optimization problems such as deep neural network training and deep matrix factorization. A central quantity to characterize this geometry is the maximum eigenvalue of the Hessian of the loss. Currently, its precise role has been obfuscated because no exact expressions for this sharpness measure were known in general settings. In this […]

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arXiv:2510.23631v2 Announce Type: replace-cross Abstract: Alignment of large language models (LLMs) has predominantly relied on pairwise preference optimization, where annotators select the better of two responses to a prompt. While simple, this approach overlooks the opportunity to learn from richer forms of human feedback, such as multiway comparisons and top-$k$ rankings. We introduce Ranked Choice Preference Optimization (RCPO), a unified framework that bridges preference optimization with (ranked) choice modeling via maximum likelihood estimation. RCPO supports both utility-based and […]

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Hi everyone! Please bear with me with this question 🫣 I’m looking for someone in research to pick their brain about the similarities and differences between statistical learning in cognitive science and in machine learning, so definition, conceptual differences/similarities, predictions, testing…. Hope it makes sense, I’m doing research in cognitive sciences and I’d love to learn more about this term’s use in ML for a review I’m working on 🙂 thanks! submitted by /u/Ok_Fudge1993 [link] [comments]

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Hi guys, I’m learning meta RL. I’m trying to try the RL2 algorithm with some Gymnasium environments. However, it seems that there is no implementation of this algorithm in current RL libraries like rllib, stable-baselines3, TorchRL. Do you have any ideas of implementing of this RL algorithm? Which library should I use? submitted by /u/ZitaLovesCats [link] [comments]

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arXiv:2602.22974v1 Announce Type: cross Abstract: Counting immunopositive cells on biological tissues generally requires either manual annotation or (when available) automatic rough systems, for scanning signal surface and intensity in whole slide imaging. In this work, we tackle the problem of counting microglial cells in lumbar spinal cord cross-sections of rats by omitting cell detection and focusing only on the counting task. Manual cell counting is, however, a time-consuming task and additionally entails extensive personnel training. The classic automatic […]

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arXiv:2512.07578v2 Announce Type: replace Abstract: We propose $phi$-test, a global feature-selection and significance procedure for black-box predictors that combines Shapley attributions with selective inference. Given a trained model and an evaluation dataset, $phi$-test performs SHAP-guided screening and fits a linear surrogate on the screened features via a selection rule with a tractable selective-inference form. For each retained feature, it outputs a Shapley-based global score, a surrogate coefficient, and post-selection $p$-values and confidence intervals in a global feature-importance table. […]

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arXiv:2602.10118v1 Announce Type: new Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics — lazy thinking — has lowered standards. Prior work treats lazy thinking detection as a single-label task, but review segments may exhibit multiple issues, including broader clarity problems, or specificity issues. Turning detection into actionable improvements requires guideline-aware feedback, which is currently missing. We introduce an LLM-driven framework that decomposes reviews into argumentative segments, identifies issues via a neurosymbolic module combining […]

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State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as transformers. Advancing SMMs to graph structured data, especially for large graphs, is a significant challenge because SSMs are sequence models and the shear graph volumes make it very expensive to convert graphs as sequences for effective learning. In this paper, we propose COMBA to tackle large graph learning using state space models, with two […]

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arXiv:2602.10219v1 Announce Type: new Abstract: Generative image steganography is a technique that conceals secret messages within generated images, without relying on pre-existing cover images. Recently, a number of diffusion model-based generative image steganography (DM-GIS) methods have been introduced, which effectively combat traditional steganalysis techniques. In this paper, we identify the key factors that influence DM-GIS security and revisit the security of existing methods. Specifically, we first provide an overview of the general pipelines of current DM-GIS methods, finding […]

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