A new wave of work on covariance cleaning and nonlinear shrinkage has delivered asymptotically optimal analytical solutions for large covariance matrices. Building on this progress, these ideas have been generalized to empirical cross-covariance matrices, whose singular-value shrinkage characterizes comovements between one set of assets and another. Existing analytical cross-covariance cleaners are derived under strong stationarity and large-sample assumptions, and they typically rely on mesoscopic regularity conditions such as bounded spectra; macroscopic common modes (e.g., a global market factor) […]

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Background: Artificial intelligence (AI) has moved from research labs into the everyday work of clinicians and software engineers. In healthcare, AI systems now shape triage, imaging interpretation, risk prediction and documentation workflows, while in computer science and AI development, models are increasingly used to generate code, tests and even other AI systems [1–4,7,10,11,76–82]. Early governance frameworks framed “human oversight” as a high-level ethical injunction but provided limited operational guidance on how humans should interact with data-intensive, adaptive AI […]

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Implementing your own neural network can be hard, especially if you’re like me, coming from a computer science background, math equations/syntax makes you dizzy and you would understand things better using actual code. Today I’ll show you how easy it is to implement a flexible neural network and train it using the backpropagation algorithm.

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TL;DR – Hypothetically If the majority of code written is eventually generative, does this mean that the field of categorization will stagnate? If yes, does this have real implications; what if the future bottle neck isn’t the AI or its capabilities, but antiquated ways in which we conceptualize and group objects and their behaviours? How we approach business problems: splitting up services, data models, and other types of grouping within problem spaces has radically changed over the past […]

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The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics. The ML-for-combinatorial-optimization community has focused on learning to emph{replace} solvers, yet recent evidence shows that graph neural networks (GNNs) rarely outperform well-tuned classical methods on standard benchmarks. We pursue a different objective: learning to predict emph{when} a given instance is hard for greedy allocation, enabling instance-dependent algorithm selection. We design a 20-dimensional structural feature vector […]

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If you have shipped a serious Angular app in the last few years, you probably have BehaviorSubjects, selector streams, and NgRx slices everywhere—and no one on the team is excited to refactor them. With Angular 19+, Signals are no longer experimental or “nice to have.” They are stable, performant, and increasingly treated as the default way to model local state. As a result, many Angular teams are actively revisiting long-standing RxJS and NgRx patterns—sometimes rewriting them entirely—while community discussions are filled […]

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Insights from conversations with CEOs, CHROs, and innovation leaders.

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Quantum reservoir computing offers a promising route for time series learning by modelling sequential data via rich quantum dynamics while the only training required happens at the level of a lightweight classical readout. However, studies featuring efficient and implementable quantum reservoir architectures along with model learning guarantees remain scarce in the literature. To close this gap, we introduce QuaRK, an end-to-end framework that couples a hardware-realistic quantum reservoir featurizer with a kernel-based readout scheme. Given a sequence of […]

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arXiv:2410.05225v3 Announce Type: replace-cross Abstract: We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards. To enhance exploration, we introduce a search procedure, emph{${epsilon}{t}$-greedy}, which generates exploratory options for exploring less-visited states. We prove that search using $epsilon t$-greedy has polynomial sample complexity under mild MDP assumptions. To more efficiently use the information provided by rewarded transitions, we develop a new dual experience replay buffer framework, emph{GDRB}, and implement emph{longest n-step returns}. […]

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