mailitics

Tag: varepsilon

  • Near-Optimal Sample Complexity for Online Constrained MDPs

    Near-Optimal Sample Complexity for Online Constrained MDPs arXiv:2602.15076v1 Announce Type: cross Abstract: Safety is a fundamental challenge in reinforcement learning (RL), particularly in real-world applications such as autonomous driving, robotics, and healthcare. To address this, Constrained Markov Decision Processes (CMDPs) are commonly used to enforce safety constraints while optimizing performance. However, existing methods often suffer…

  • Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem

    Detecting Stochasticity in Discrete Signals via Nonparametric Excursion Theorem arXiv:2601.06009v1 Announce Type: new Abstract: We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our approach is based on classical excursion and crossing theorems for continuous semimartingales, which correlates number $N_varepsilon$ of excursions of magnitude…

  • Learning Multinomial Logits in $O(n log n)$ time

    Learning Multinomial Logits in $O(n log n)$ time arXiv:2601.04423v1 Announce Type: cross Abstract: A Multinomial Logit (MNL) model is composed of a finite universe of items $[n]={1,…, n}$, each assigned a positive weight. A query specifies an admissible subset — called a slate — and the model chooses one item from that slate with probability…

  • Central limit theorems for the eigenvalues of graph Laplacians on data clouds

    Central limit theorems for the eigenvalues of graph Laplacians on data clouds arXiv:2507.18803v1 Announce Type: new Abstract: Given i.i.d. samples $X_n ={ x_1, dots, x_n }$ from a distribution supported on a low dimensional manifold ${M}$ embedded in Eucliden space, we consider the graph Laplacian operator $Delta_n$ associated to an $varepsilon$-proximity graph over $X_n$ and…

  • Fundamental Limits of Learning High-dimensional Simplices in Noisy Regimes

    Fundamental Limits of Learning High-dimensional Simplices in Noisy Regimes arXiv:2506.10101v1 Announce Type: new Abstract: In this paper, we establish sample complexity bounds for learning high-dimensional simplices in $mathbb{R}^K$ from noisy data. Specifically, we consider $n$ i.i.d. samples uniformly drawn from an unknown simplex in $mathbb{R}^K$, each corrupted by additive Gaussian noise of unknown variance. We…