We construct algorithms with optimal error for learning with adversarial noise. The overarching theme of this work is that the use of textsl{randomized} hypotheses can substantially improve upon the best error rates achievable with deterministic hypotheses. – For $η$-rate malicious noise, we show the optimal error is $frac{1}{2} cdot η/(1-η)$, improving on the optimal error of deterministic hypotheses by a factor of $1/2$. This answers an open question of Cesa-Bianchi et al. (JACM 1999) who showed randomness can […]

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Release: asyncinject 0.7 I built this utility library to support an asyncio dependency injection pattern a few years ago. I was using it with Datasette and Claude Fable 5 spotted some bugs in the dependency which it then fixed for me. It’s a very proactive model! Tags: async, projects, python, claude-mythos

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arXiv:2606.04009v1 Announce Type: new Abstract: Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images. Recent deep two-sample tests improve sensitivity in these settings by learning informative representations, yet they provide limited insight into which data features drive rejection of the null hypothesis $H_0$. To address this issue, we propose a counterfactual explanation framework for deep two-sample testing that […]

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Every investor talks about risk, but few can explain what truly drives it. We measure volatility, beta, or max drawdown, and assume we understand exposure. Yet these numbers are only outcomes. They tell us how much pain existed, not why it happened. Sometimes a high P/E stock crashes harder. Sometimes a low-margin business stays resilient. Correlation alone can’t answer what actually causes those reactions. That’s where most models stop, and where this story begins. This experiment tries to […]

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Hello, I wanted to ask how do you restrict the output vocabulary/ possible actions of VLAs. Specifically I am reading currently the research papers of RT-2 and OpenVLA. OpenVLA references RT-2 and RT-2 says nothing specifically, it just says in the fine-tuning phase: “Thus, to ensure that RT-2 outputs valid action tokens during decoding, we constrain its output vocabulary via only sampling valid action tokens when the model is prompted with a robot-action task …” So do you […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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