Preference elicitation (PE) is an important component of interactive decision support systems that aim to make optimal recommendations to users by actively querying their preferences. In this paper, we outline five principles important for PE in real-world problems: (1) real-time, (2) multiattribute, (3) low cognitive load, (4) robust to noise, and (5) scalable. In light of these requirements, we introduce an approximate PE framework based on TrueSkill for performing efficient closed-form Bayesian updates and query selection for a […]

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arXiv:2602.02056v1 Announce Type: cross Abstract: Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales. Meeting these requirements demands low-latency, fixed-precision computation under strict memory constraints, a regime in which conventional Multi-Layer Perceptrons (MLPs) are both inefficient and numerically unstable. We identify key properties of Kolmogorov-Arnold Networks (KANs) that align with these constraints. Specifically, we show that: (i) KAN updates exploiting B-spline locality are […]

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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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I’m working on a sales forecasting task with historical seasonal data. Right now, I can train a supervised model, specifically XGBoost, that works reasonably well. I was told by my supervisor to use RL on top of the supervised model predictions, but I’m having trouble understanding how reinforcement learning would actually be structured for my problem. What part of the system would it actually adjust or control? Is this supposed to be an offline bandit, or a full […]

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arXiv:2601.22347v1 Announce Type: new Abstract: Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of full-vector rotations, the effect of block structure on outlier suppression remains poorly understood. To fill this gap, we present the first systematic, non-asymptotic analysis of outlier suppression for block Hadamard rotations. Our analysis reveals that outlier suppression is fundamentally limited by the geometry of the input vector. In particular, post-rotation outliers are […]

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Fairness in machine learning is increasingly critical, yet standard approaches often treat data as static points in a high-dimensional space, ignoring the underlying generative structure. We posit that sensitive attributes (e.g., race, gender) do not merely shift data distributions but causally warp the geometry of the data manifold itself. To address this, we introduce Causal Manifold Fairness (CMF), a novel framework that bridges causal inference and geometric deep learning. CMF learns a latent representation where the local Riemannian […]

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