technocracy

arXiv:2601.11134v1 Announce Type: cross Abstract: Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential […]

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Please consider nominating worthy candidates to the Knuth Prize!  The prize is awarded “for major research accomplishments and contributions to the foundations of computer science over an extended period of time.” 2025 Knuth Prize Committee:Noga Alon, Edith Cohen (Chair), David Eppstein, Valerie King, Salil Vadhan, and Moshe Vardi. Deadline:  March 31 https://www.sigact.org/prizes/knuth.html

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Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distribution induced by the observed data and a balanced target distribution under which performance is evaluated. Within this framework, we study a family of oversampling procedures that augment the training data by generating synthetic samples from an estimated minority-class distribution to roughly balance the classes, among which the celebrated SMOTE algorithm […]

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For years, emerging projects were competing for distribution and attention with 2 types of brands: large mature businesses with unlimited marketing budgets (think, Binance, Coinbase, or Ethereum) and new projects with a growth-hacking mindset pulling guerrilla stunts (e.g., Grass, Cluely). But, in 2026, the most powerful distribution channel won’t belong to any of them – it will be owned by governments. Here’s why. The crypto world used to run on one simple emotional loop: greed and fear. Greed […]

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arXiv:2601.09000v1 Announce Type: new Abstract: The Warmup Stable Decay (WSD) learning rate scheduler has recently become popular, largely due to its good performance and flexibility when training large language models. It remains an open question whether the remarkable performance of WSD – using a decaying learning rate for only a fraction of training compared to cosine decay – is a phenomenon specific to transformer-based language models that can potentially offer new theoretical insights into their training dynamics. Inspired […]

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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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Hey folks! I’ve compiled a list of available RL-related position in game studios worldwide. I’m sure I captured the majority of positions on the market, although if I missed something please comment below. RL positions are extremely rare so I hope it will be useful to somebody Original list on LinkedIn: https://www.linkedin.com/posts/viktor-zatorskyi_rl-activity-7416719619899576321-X_Tq submitted by /u/bmind7 [link] [comments]

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How enterprise AI is finally graduating from prototype theater to mission-critical systems engineering — and why your architecture determines everything The industry has reached an inflection point that most practitioners haven’t fully internalized yet. After spending the better part of 2024 watching organizations burn through millions on AI “pilot programs” that never left the sandbox, we’re now witnessing something fundamentally different: the wholesale transformation of AI agents from conversational toys into production-grade infrastructure components that need to meet the same […]

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Background: Pancreatic cancer is one of the most aggressive cancers, with poor survival rates. Endoscopic ultrasound (EUS) is a key diagnostic modality, but its effectiveness is constrained by operator subjectivity. This study evaluates a Vision Transformer-based deep learning segmentation model for pancreatic tumors. Methods: A segmentation model using the USFM framework with a Vision Transformer backbone was trained and validated with 17,367 EUS images (from two public datasets) in 5-fold cross-validation. The model was tested on an independent […]

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