February 2026

Hi everyone, I’m an independent researcher (incoming MSc AI, University of Edinburgh) and I’ve written a pre-registration paper modelling the 2026 Formula 1 energy regulations as a Partially Observable Stochastic Game. I’m looking for an arXiv endorsement in cs.AI or cs.LG to upload it before the Melbourne GP on 8 March, ideally even before the race weekend starts. The paper: Opponent State Inference Under Partial Observability: An HMM–POMDP Framework for 2026 Formula 1 Energy Strategy https://www.researchgate.net/publication/401368044_Opponent_State_Inference_Under_Partial_Observability_An_HMM-POMDP_Framework_for_2026_Formula_1_Energy_Strategy The problem: […]

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This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First, it reviews error bounds for empirical risk minimization with a focus on holomorphic operators and neural network approximations. Next, it illustrates fundamental performance limits in terms of sample size by adopting a minimax perspective and considering various notions of regularity beyond holomorphy. The paper ends with a discussion on the interplay between these two perspectives and related open questions.

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In machine learning, curation is used to select the most valuable data for improving both model accuracy and computational efficiency. Recently, curation has also been explored as a solution for private machine learning: rather than training directly on sensitive data, which is known to leak information through model predictions, the private data is used only to guide the selection of useful public data. The resulting model is then trained solely on curated public data. It is tempting to […]

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So I’ve been training an PPO (+mamba) agent to trade a specific crypto future, these are the results of a 64 env run on unseen data, each environment is starting on the same timestamp: – Plots for each env/agent – Overall P&L: +3643.39$ Is it somewhat expected that there would be such variance in evaluation runs and that running multiple parallel environments improves the overall P&L? submitted by /u/KingSignificant5097 [link] [comments]

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Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a network-aware self-supervised framework based on masked reconstruction with cross-attention that explicitly models inter-network dependencies in rs-fMRI. By selectively masking predefined functional networks and reconstructing them from remaining context, our approach enables direct quantification of network […]

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The magic of transformers lies in their attention mechanism. But what does that actually mean? Here’s a simplified explanation to build intuition. A SIMPLE EXAMPLE Consider: “What is the capital of France?” As humans, we parse this as: “What” signals a question “is” indicates the current timeframe “capital” means the main city “France” is the country for which I want the capital We process it instantly. But for a computer? Different story. THE ATTENTION MECHANISM: Q, K, V […]

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I’ve never been good at shell scripting. I’d much rather write scripts in a general purpose language like Python. But occasionally a shell script can do something so simply that it’s worth writing a shell script. Sometimes a shell scripting feature is terse and cryptic precisely because it solves a common problem succinctly. One example of this is working with file extensions. For example, maybe you have a script that takes a source file name like foo.java and […]

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Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes. A necessary prerequisite is to identify subgroups of patients who respond differently to different therapies. Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in most clinical trials. Our approach combines the concept of partly conditional modelling with treatment effect estimation based on the Virtual Twins method. The resulting time-specific responses to treatment are characterised using […]

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