arXiv:2602.00918v1 Announce Type: new Abstract: Early Classification of Time Series (ECTS) addresses decision-making problems in which predictions must be made as early as possible while maintaining high accuracy. Most existing ECTS methods assume that the time-dependent decision costs governing the learning objective are known, fixed, and correctly specified. In practice, however, these costs are often uncertain and may change over time, leading to mismatches between training-time and deployment-time objectives. In this paper, we study ECTS under two practically […]

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arXiv:2506.17764v2 Announce Type: replace Abstract: Band-limited functions are fundamental objects that are widely used in systems theory and signal processing. In this paper we refine a recent nonparametric, nonasymptotic method for constructing simultaneous confidence regions for band-limited functions from noisy input-output measurements, by working in a Paley-Wiener reproducing kernel Hilbert space. Kernel norm bounds are tightened using a uniformly-randomized Hoeffding’s inequality for small samples and an empirical Bernstein bound for larger ones. We derive an approximate threshold, based […]

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arXiv:2602.02565v1 Announce Type: new Abstract: This paper approaches high-rank matrix completion (HRMC) by filling missing entries in a data matrix where columns lie near a union of subspaces, clustering these columns, and identifying the underlying subspaces. Current methods often lack theoretical support, produce uninterpretable results, and require more samples than theoretically necessary. We propose clustering incomplete vectors by grouping proxy subspaces and minimizing two criteria over the Grassmannian: (a) the chordal distance between each point and its corresponding […]

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Image-to-PointCloud place recognition is vital for autonomous systems, yet faces challenges from the inherent modality gap and drastic environmental variations. We propose Cross-Modal Invariant Representation Learning (CMIRL) to learn highly invariant cross-modal global descriptors. CMIRL introduces an Adaptive Cross-Modal Alignment (ACMA) module, which dynamically projects point clouds based on image semantics to generate view-optimized dense depth maps. A Dual-Stream Invariant Feature Encoder, featuring a Transformer-based Cross-Modal Attention Fusion (CMAF) module, then explicitly learns and emphasizes features shared across […]

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These last months, while working on my graduation project, I had the chance to learn a lot about Data Quality, Anomaly Detection and especially Outliers Detection. In these series, I will be explaining what outliers are, the difference between novelty and outliers detection and how we can detect outliers using different algorithms.

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arXiv:2503.17300v4 Announce Type: cross Abstract: We propose a variational tail bound for norms of random vectors under moment assumptions on their one-dimensional marginals. A simplified version of the bound that parametrizes the “aggregating distribution” using a certain pushforward of the Gaussian distribution is also provided. We apply the proposed method to reproduce some of the well-known bounds on norms of Gaussian random vectors, and also obtain dimension-free tail bounds for the Euclidean norm of random vectors with arbitrary […]

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arXiv:2601.22228v1 Announce Type: new Abstract: Vision-Language Models (VLMs) perform well in 2D perception and semantic reasoning compared to their limited understanding of 3D spatial structure. We investigate this gap using relative camera pose estimation (RCPE), a fundamental vision task that requires inferring relative camera translation and rotation from a pair of images. We introduce VRRPI-Bench, a benchmark derived from unlabeled egocentric videos with verbalized annotations of relative camera motion, reflecting realistic scenarios with simultaneous translation and rotation around […]

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