technocracy

In this work, we investigate the use of data-driven equation discovery for dynamical systems to model and forecast continuous-time dynamics of unconstrained optimization problems. To avoid expensive evaluations of the objective function and its gradient, we leverage trajectory data on the optimization variables to learn the continuous-time dynamics associated with gradient descent, Newton’s method, and ADAM optimization. The discovered gradient flows are then solved as a surrogate for the original optimization problem. To this end, we introduce the […]

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Hi everyone, I’m working on a large-scale reinforcement learning experiment to compare the convergence behavior of several classical temporal-difference algorithms such as: SARSA Expected SARSA Q-learning Double Q-learning TD(λ) Deep Q-learning Maybe I currently have access to significant compute resources , so I’m planning to run thousands of seeds and millions of episodes to produce statistically strong convergence curves. The goal is to clearly visualize differences in: convergence speed, stability / variance across runs Most toy environments (CliffWalking, […]

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arXiv:2407.10070v3 Announce Type: replace-cross Abstract: Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due to its starring role in Gaussian process regression. However, full KRR solvers are challenging to scale to large datasets: both direct (i.e., Cholesky decomposition) and iterative methods (i.e., PCG) incur prohibitive computational and storage costs. The standard approach to scale KRR to large datasets chooses a set of […]

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The Fréchet Inception Distance (FID), the standard metric for evaluating deep generative models, aggregates all data into a single score and thereby masks quality degradation in safety-critical minority conditions and in specific temporal regions of generated time series. We trace this dilution problem to a single cause—the absence of stratification—and propose Stratified Fréchet Distance (SFD), which partitions evaluation data into strata along a chosen axis and computes the Fréchet distance within each stratum. The choice of axis determines […]

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arXiv:2601.08045v1 Announce Type: new Abstract: The widespread adoption of Large Language Models (LLMs) in software development is transforming programming from a solution-generative to a solution-evaluative activity. This shift opens a pathway for new cognitive challenges that amplify existing decision-making biases or create entirely novel ones. One such type of challenge stems from cognitive biases, which are thinking patterns that lead people away from logical reasoning and result in sub-optimal decisions. How do cognitive biases manifest and impact decision-making […]

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arXiv:2601.18997v1 Announce Type: new Abstract: The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Conformal prediction (CP) is a powerful distribution-free framework for constructing statistically valid prediction intervals. However, standard applications in segmentation often ignore anatomical context, resulting in fragmented, spatially incoherent, and over-segmented prediction sets that limit clinical utility. To bridge this gap, this paper proposes Random-Walk Conformal Prediction (RW-CP), a model-agnostic framework which can […]

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arXiv:2601.17108v1 Announce Type: new Abstract: This paper proposes a Mamba-assisted neural network framework incorporating self-attention mechanism to achieve improved channel estimation with low complexity for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers. With the integration of customized Mamba architecture, the proposed framework handles large-scale subcarrier channel estimation efficiently while capturing long-distance dependencies among these subcarriers effectively. Unlike conventional Mamba structure, this paper implements a bidirectional selective scan to improve channel estimation […]

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