arXiv:2604.25984v1 Announce Type: new Abstract: The marginal likelihood, also known as the evidence, is regarded as a mathematical embodiment of Occam’s razor, enabling model selection that avoids overfitting. The evidence lower bound (ELBO) objective from variational inference has also been used for similar purposes. Prior work has shown that restricting the approximate posterior family via a mean-field approximation can lead the ELBO to underfit. In this paper, we show how ELBO-based hyperparameter learning in a simple over-parameterized regression […]

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Estimating the Riesz representer is central to debiased machine learning for causal and structural parameter estimation. We propose generalized Riesz regression, a unified framework that estimates the Riesz representer by fitting a representer model via Bregman divergence minimization. This framework includes the squared loss and the Kullback–Leibler (KL) divergence as special cases: the former recovers Riesz regression, while the latter recovers tailored loss minimization. Under suitable model specifications, the dual problems correspond to covariate balancing, which we call […]

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Someone asked if there was an Anthropic equivalent to OpenAI’s IRS mission statements over time. Anthropic are a “public benefit corporation” but not a non-profit, so they don’t have the same requirements to file public documents with the IRS every year. But when I asked Claude it ran a search and dug up this Google Drive folder where Zach Stein-Perlman shared Certificate of Incorporation documents he obtained from the State of Delaware! Anthropic’s are much less interesting that […]

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arXiv:2604.00683v1 Announce Type: cross Abstract: Stochastic natural gradient variational inference (NGVI) is a popular and efficient algorithm for Bayesian inference. Despite empirical success, the convergence of this method is still not fully understood. In this work, we define and study a projected stochastic NGVI when variational distributions form an exponential family. Stochasticity arises when either gradients are intractable expectations or large sums. We prove new non-asymptotic convergence results for combinations of constant or decreasing step sizes and constant […]

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Why migrating from YOLO to Grounding DINO was a total grind against CPU caches — and why the “Magic Optimization” button is a lie In my previous post (Python is a Video Latency Suicide Note: How I Hit 29 FPS with Zero-Copy C++ ONNX), I detailed how I murdered the Global Interpreter Lock. By mapping hardware Luma (Y) natively into a Zero-Copy C++ pipeline, I drove YOLOv8 to a blistering 29+ FPS on a standard CPU. I had […]

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arXiv:2603.27864v1 Announce Type: cross Abstract: We review recently proposed Bayesian approaches for clustering high-dimensional data. After identifying the main limitations of available approaches, we introduce an alternative framework based on vertical consensus inference (VCI) to mitigate the curse of dimensionality in high-dimensional Bayesian clustering. VCI builds on the idea of consensus Monte Carlo by dividing the data into multiple shards (smaller subsets of variables), performing posterior inference on each shard, and then combining the shard-level posteriors to obtain […]

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arXiv:2502.09880v2 Announce Type: replace-cross Abstract: Stemming from physics and later applied to other fields such as ecology, the theory of critical transitions suggests that some regime shifts are preceded by statistical early warning signals. Reddit’s r/place experiment, a large-scale social game, provides a unique opportunity to test these signals consistently across thousands of subsystems undergoing critical transitions. In r/place, millions of users collaboratively created ”compositions”, or pixel-art drawings, in which transitions occur when one composition rapidly replaces another. […]

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