technocracy

arXiv:2506.16965v3 Announce Type: replace-cross Abstract: Ensemble learning remains a cornerstone of machine learning, with stacking used to integrate predictions from multiple base learners through a meta-model. However, deep stacking remains uncommon due to feature redundancy, complexity, and computational burden. To address these limitations, RocketStack is introduced as a level-aware recursive stacking architecture explored up to ten stacking levels, extending beyond prior architectures. At level 1, base-learner predictions are fused with original features; at later levels, weaker learners are […]

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Sponsor content from IBM.

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I’m trying to build a clear conceptual understanding of Model-Based Reinforcement Learning, but I’m getting confused because several ideas seem to overlap. For example, I’ve encountered: – Dyna-style methods: learning a model and generating synthetic (imagined) data to improve policy/value learning – World models (e.g., Dreamer): learning latent dynamics and doing policy optimization in imagination – Planning-based approaches such as MPC or Monte Carlo Tree Search: using the learned model to select actions via planning What confuses me […]

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arXiv:2504.11304v2 Announce Type: replace Abstract: In statistical applications it has become increasingly common to encounter data structures that live on non-linear spaces such as manifolds. Classical linear regression, one of the most fundamental methodologies of statistical learning, captures the relationship between an independent variable and a response variable which both are assumed to live in Euclidean space. Thus, geodesic regression emerged as an extension where the response variable lives on a Riemannian manifold. The parameters of geodesic regression, […]

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Every day, media use is growing at an unimaginable rate and generating huge amounts of data. Facebook generates nearly 5 petabytes of data per day; Netflix, around 2 petabytes per week; and Google, about 20 petabytes every day. Business decisions depend on analytics, and analytics requires processed data every single day. So how do we process all this data? Traditional ETL tools can’t keep up when data volumes reach terabytes—and they’re too slow. That’s why the data world […]

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arXiv:2602.11159v1 Announce Type: new Abstract: The increasing use of Machine Learning (ML) in sensitive domains such as healthcare, finance, and public policy has raised concerns about the transparency of automated decisions. Explainable AI (XAI) addresses this by clarifying how models generate predictions, yet most methods demand technical expertise, limiting their value for novices. This gap is especially critical in no-code ML platforms, which seek to democratize AI but rarely include explainability. We present a human-centered XAI module in […]

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arXiv:2603.12463v1 Announce Type: new Abstract: With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in […]

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Release: llm-all-models-async 0.1 LLM plugins can define new models in both sync and async varieties. The async variants are most common for API-backed models – sync variants tend to be things that run the model directly within the plugin. My llm-mrchatterbox plugin is sync only. I wanted to try it out with various Datasette LLM features (specifically datasette-enrichments-llm) but Datasette can only use async models. So… I had Claude spin up this plugin that turns sync models into […]

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