Image Source: https://www.technologynetworks.com/diagnostics/news/wealth-and-education-play-significant-role-in-heart-disease-risk-396976 Why ensemble methods generalize better than deep learning on clinical tabular data Heart disease continues to be the leading cause of death worldwide, responsible for millions of deaths every year. Despite advances in clinical diagnostics, early and accurate detection remains a persistent challenge. Traditional diagnostic procedures are often invasive, expensive, and heavily dependent on physician interpretation. This is where Machine Learning (ML) offers a compelling alternative. In this article, I present a comprehensive machine learning study […]

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Hey guys i have recently started learning about RL, dont know much in depth but focused more on implementing it in the stock market. I am not looking for some crazy unrealistic returns… just want to make something that can perform better than the market and want to learn along the way. My current roadmap is to just test how different models are performing on a basic level. I’d appreciate any kind of help or suggestion come my […]

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TL;DR: We ran a factorial study on policy conditioning (appending a “goal” signal to observations). We found that while it barely improves “tracking precision,” it leads to a 23x improvement in tail-risk (CVaR). Crucially, we prove that temporal correlation—not just having the extra data—is the causal driver. The Problem: The “Black Box” of Conditioning In RL, we often append a task descriptor (goal, context vector, or latent) to the agent’s observation. We assume it helps the agent adapt. […]

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arXiv:2309.08945v2 Announce Type: replace-cross Abstract: In recent years, a certain type of problems have become of interest where one wants to query a trained classifier. Specifically, one wants to find the closest instance to a given input instance such that the classifier’s predicted label is changed in a desired way. Examples of these “inverse classification” problems are counterfactual explanations, adversarial examples and model inversion. All of them are fundamentally optimization problems over the input instance vector involving a […]

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arXiv:2602.06900v1 Announce Type: cross Abstract: Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio […]

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arXiv:2601.10865v1 Announce Type: new Abstract: Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis for JavaScript poses two major challenges. First, JavaScript’s dynamic features complicate data flow extraction required for taint tracking. Second, npm’s large library ecosystem makes it difficult to identify relevant sources/sinks and establish taint propagation across dependencies. In this paper, we present SemTaint, a multi-agent system […]

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arXiv:2603.23568v1 Announce Type: cross Abstract: Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem. Rather than treating this as a classification challenge, we propose to frame it as a causal signal reconstruction problem: given probabilistic sentiment outputs from a fixed classifier, recover a stable latent sentiment series that is robust to the structural pathologies of news data […]

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Dense image captioning is critical for cross-modal alignment in vision-language pretraining and text-to-image generation, but scaling expert-quality annotations is prohibitively expensive. While synthetic captioning via strong vision-language models (VLMs) is a practical alternative, supervised distillation often yields limited output diversity and weak generalization. Reinforcement learning (RL) could overcome these limitations, but its successes have so far been concentrated in verifiable domains that rely on deterministic checkers — a luxury not available in open-ended captioning. We address this bottleneck […]

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