arXiv:2511.01734v3 Announce Type: replace Abstract: We provide the first proof of learning rate transfer with width in a linear multi-layer perceptron (MLP) parametrized with $mu$P, a neural network parameterization designed to “maximize” feature learning in the infinite-width limit. We show that under $mu P$, the optimal learning rate converges to a emph{non-zero constant} as width goes to infinity, providing a theoretical explanation to learning rate transfer. In contrast, we show that this property fails to hold under alternative […]

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arXiv:2510.04769v2 Announce Type: replace-cross Abstract: Many machine learning algorithms rely on iterative updates of uncertainty representations, ranging from variational inference and expectation-maximization, to reinforcement learning, continual learning, and multi-agent learning. In the presence of imprecision and ambiguity, credal sets — closed, convex sets of probability distributions — have emerged as a popular framework for representing imprecise probabilistic beliefs. Under such imprecision, many learning problems in imprecise probabilistic machine learning (IPML) may be viewed as processes involving successive applications […]

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arXiv:2601.00264v1 Announce Type: new Abstract: Multimodal learning has revolutionized general domain tasks, yet its application in scientific discovery is hindered by the profound semantic gap between complex scientific imagery and sparse textual descriptions. We present S1-MMAlign, a large-scale, multi-disciplinary multimodal dataset comprising over 15.5 million high-quality image-text pairs derived from 2.5 million open-access scientific papers. Spanning disciplines from physics and biology to engineering, the dataset captures diverse visual modalities including experimental setups, heatmaps, and microscopic imagery. To address […]

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arXiv:2602.15274v1 Announce Type: new Abstract: We explore how different types and uses of memory can aid spatial navigation in changing uncertain environments. In the simple foraging task we study, every day, our agent has to find its way from its home, through barriers, to food. Moreover, the world is non-stationary: from day to day, the location of the barriers and food may change, and the agent’s sensing such as its location information is uncertain and very limited. Any […]

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Hey folks, My team and I are diving into RL training setups and want to chat with folks who have hands-on experience. Could share your process for picking an environment (e.g., Gym, custom sims) and getting it up and running? What pain points have you hit—like scaling, reward shaping, or integration issues—and what fixes made life easier? DMs open or reply below—happy to hop on a quick call! Thanks! submitted by /u/Popular_Piglet_1443 [link] [comments]

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The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Artificial Intelligence (AI) models. However, stringent privacy regulations such as GDPR and HIPAA have created data silos that prevent centralized training. Federated Learning (FL) has emerged as a promising solution that enables collaborative model training without sharing raw patient data. Despite its potential, FL remains vulnerable to poisoning and Sybil attacks, in which malicious participants corrupt the global model or […]

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arXiv:2602.12468v1 Announce Type: new Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous latent dynamics make discrete constraints — e.g., the output should be a JSON file that matches a given schema — difficult to impose. We introduce a training-free guidance method for steering continuous diffusion language models to satisfy formal syntactic constraints expressed using regular expressions. Our approach constructs an analytic score estimating the probability […]

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First time being accepted at WACV (poster). I’ve already submitted the poster, the 5-minute virtual presentation (YouTube link), and the thumbnail. For attendees who aren’t traveling in person: will the recorded virtual talk be played in the hall during the session, or will it only be available online? Also is there any other action that needs to be taken from our side? submitted by /u/Forsaken-Order-7376 [link] [comments]

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arXiv:2511.15315v2 Announce Type: replace Abstract: Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded cumulative corruption budget, which makes them defenseless against even a single corruption of sufficient magnitude. To address this, we introduce a new adversary whose budget is only bounded in the frequency of corruptions, not in their magnitude. We then derive RCGP-UCB, an algorithm coupling the famous upper confidence bound (UCB) approach with a Robust Conjugate Gaussian Process […]

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