technocracy

arXiv:2111.09266v5 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal distributions where some variables are unspecified and, of particular […]

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arXiv:2602.05961v1 Announce Type: cross Abstract: Sampling from a distribution $p(x) propto e^{-mathcal{E}(x)}$ known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the rise of a new family of amortised sampling algorithms, commonly referred to as diffusion samplers, that enable fast and efficient sampling from an unnormalised density. Such algorithms have been widely studied for continuous-space sampling tasks; however, their application to problems in discrete space remains largely unexplored. Although […]

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Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have advanced faster than rigorous financial benchmarking can properly evaluate them. Existing comparisons are often limited by inconsistent hyperparameter budgets, single-seed evaluation, narrow asset coverage, and a lack of statistical validation. This study presents a controlled comparison of nine architectures—Autoformer, DLinear, iTransformer, LSTM, ModernTCN, N-HiTS, PatchTST, TimesNet, and TimeXer—spanning four model families (Transformer, MLP, CNN, and RNN), evaluated across three asset […]

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Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations from structured data. Despite their growing popularity and success across various applications, GNNs encounter several challenges that limit their performance. in their generalization, robustness to adversarial perturbations, and the effectiveness of their representation learning capabilities. In this dissertation, I investigate these core aspects through three main contributions: (1) developing new representation learning techniques based on Graph Shift Operators (GSOs, aiming for enhanced performance across various contexts […]

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Your Computer System Just Became Your Universal AI Agent Toolkit For Everything! :::tip What if you could talk to your computer the way you talk to a colleague — and it just did the work? ::: Not autocomplete. Not a chatbot that pastes code snippets. Not a suggestion engine that highlights the next three words. A system that takes your intent and orchestrates your entire machine — files, browser, terminal, internet — to deliver the result. That is exactly […]

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arXiv:2603.02607v1 Announce Type: new Abstract: Sparse PCA is one of the most well-studied problems in high-dimensional statistics. In this problem, we are given samples from a distribution with covariance $Sigma$, whose top eigenvector $v in R^d$ is $s$-sparse. Existing sparse PCA algorithms can be broadly categorized into (1) combinatorial algorithms (e.g., diagonal or elementwise covariance thresholding) and (2) SDP-based algorithms. While combinatorial algorithms are much simpler, they are typically only analyzed under the spiked identity model (where $Sigma […]

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arXiv:2603.03376v1 Announce Type: new Abstract: As V2X (Vehicle-to-Everything) technology becomes increasingly prevalent, the security of V2X networks has garnered growing attention worldwide. In North America, the IEEE 1609 series standards are primarily used, while Europe adopts the ETSI series standards, and China has also established its industry standard, YD/T 3957-2021, among others. Although these standards share some commonalities, they also exhibit differences. To achieve compatibility across these standards, analyzing their similarities and differences is a crucial issue. Therefore, […]

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Deep reinforcement learning (DRL) may explore infeasible actions during training and execution. Existing approaches assume a symbol grounding function that maps high-dimensional states to consistent symbolic representations and a manually specified action masking techniques to constrain actions. In this paper, we propose Neuro-symbolic Action Masking (NSAM), a novel framework that automatically learn symbolic models, which are consistent with given domain constraints of high-dimensional states, in a minimally supervised manner during the DRL process. Based on the learned symbolic […]

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Generative models have recently demonstrated remarkable success across diverse domains, motivating their adoption as expressive policies in reinforcement learning (RL). While they have shown strong performance in offline RL, particularly where the target distribution is well defined, their extension to online fine-tuning has largely been treated as a direct continuation of offline pre-training, leaving key challenges unaddressed. In this paper, we propose Flow Matching with Injected Noise for Offline-to-Online RL (FINO), a novel method that leverages flow matching-based […]

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For most of the internet era, educational technology followed a familiar pattern: digitize the textbook, put worksheets on a screen, maybe add a video. The goal was efficiency. Faster distribution. Easier grading. More content, more quickly. But that model is starting to feel dated. Today’s learners don’t just want information delivered. They expect to interact with it. Remix it. Build on top of it. The question is no longer whether technology belongs in education; it’s whether our education […]

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