Where Senior Leaders Are Struggling with AI Adoption, According to Research
Insights from conversations with CEOs, CHROs, and innovation leaders.
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Assessing the informative value of macroeconomic indicators for public health forecasting
arXiv:2601.15514v1 Announce Type: cross Abstract: Macroeconomic conditions influence the environments in which health systems operate, yet their value as leading signals of health system capacity has not been systematically evaluated. In this study, we examine whether selected macroeconomic indicators contain predictive information for several capacity-related public health targets, including employment in the health and social assistance workforce, new business applications in the sector, and health care construction spending. Using monthly U.S. time series data, we evaluate multiple forecasting […]
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A Brief Intro to SQL
A lot of people say “S..Q..L”, by separating the letters or some pronounce it as “sequel”. It’s up to you, I like “sequel”, it just sounds cooler. SQL is a superrrrrrrrrrr important skill to learn when dealing with huge amounts of data sets. SQL, which stands for Structured Query Language, is a programming language used for retrieving data and manipulating the data models (aka in table format). To specify that, it is used to extract data from a relational […]
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SR4-Fit: An Interpretable and Informative Classification Algorithm Applied to Prediction of U.S. House of Representatives Elections
arXiv:2602.06229v1 Announce Type: new Abstract: The growth of machine learning demands interpretable models for critical applications, yet most high-performing models are “black-box” systems that obscure input-output relationships, while traditional rule-based algorithms like RuleFit suffer from a lack of predictive power and instability despite their simplicity. This motivated our development of Sparse Relaxed Regularized Regression Rule-Fit (SR4-Fit), a novel interpretable classification algorithm that addresses these limitations while maintaining superior classification performance. Using demographic characteristics of U.S. congressional districts from […]
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Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events
arXiv:2601.15475v1 Announce Type: new Abstract: Novel view synthesis from low dynamic range (LDR) blurry images, which are common in the wild, struggles to recover high dynamic range (HDR) and sharp 3D representations in extreme lighting conditions. Although existing methods employ event data to address this issue, they ignore the sensor-physics mismatches between the camera output and physical world radiance, resulting in suboptimal HDR and deblurring results. To cope with this problem, we propose a unified sensor-physics grounded NeRF […]
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Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
We study gap-dependent performance guarantees for nearly minimax-optimal algorithms in reinforcement learning with linear function approximation. While prior works have established gap-dependent regret bounds in this setting, existing analyses do not apply to algorithms that achieve the nearly minimax-optimal worst-case regret bound $tilde{O}(dsqrt{H^3K})$, where $d$ is the feature dimension, $H$ is the horizon length, and $K$ is the number of episodes. We bridge this gap by providing the first gap-dependent regret bound for the nearly minimax-optimal algorithm LSVI-UCB++ […]
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AI boom reportedly made tech billionaires like Elon Musk even richer in 2025
Key Highlights: The artificial intelligence boom is helping billionaires get even richer, and 2025 has been one if the most profitable years for most of them. According to latest market data, America’s richest tech figures have added more than half a trillion dollars to the combined wealth, thanks to ever-soaring AI-related stick prices. The Guardian, citing Bloomberg figures, reports that the top 10 U.S. tech founders and executives now control close to $2.5 trillion in wealth. Previous year, […]
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[D] Advice on a Modern NLP Roadmap (for someone with strong ML theory background)
I have a strong background in ML theory (did a Ph.D. in the field) but I’m out of the loop on the current NLP state-of-the-art. I’m looking for a “roadmap” that respects a PhD-level understanding of math/optimization while skipping “Intro to Python” style tutorials. The end goal isn’t academia but more of industry / research roles, maybe. If you had to design a 4-week “crash course” for someone who already understands backprop but hasn’t touched a Transformer, what […]
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Beating the Winner’s Curse via Inference-Aware Policy Optimization
arXiv:2510.18161v3 Announce Type: replace Abstract: There has been a surge of recent interest in automatically learning policies to target treatment decisions based on rich individual covariates. In addition, practitioners want confidence that the learned policy has better performance than the incumbent policy according to downstream policy evaluation. However, due to the winner’s curse — an issue where the policy optimization procedure exploits prediction errors rather than finding actual improvements — predicted performance improvements are often not substantiated by […]
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A PAC-Bayesian Analysis of Channel-Induced Degradation in Edge Inference
arXiv:2601.10915v1 Announce Type: new Abstract: In the emerging paradigm of edge inference, neural networks (NNs) are partitioned across distributed edge devices that collaboratively perform inference via wireless transmission. However, standard NNs are generally trained in a noiseless environment, creating a mismatch with the noisy channels during edge deployment. In this paper, we address this issue by characterizing the channel-induced performance deterioration as a generalization error against unseen channels. We introduce an augmented NN model that incorporates channel statistics […]
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