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Tag: algorithms

  • It’s all the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms

    It’s all the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms arXiv:2601.22378v1 Announce Type: new Abstract: Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machine learning. We prove that under certain conditions in an…

  • Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning

    Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning arXiv:2511.21915v1 Announce Type: new Abstract: Most scientific domains elicit the development of efficient algorithms and accessible scientific software. This thesis unifies our developments in three broad domains: Quasi-Monte Carlo (QMC) methods for efficient high-dimensional integration, Gaussian process (GP) regression for…

  • LLMs Are Randomized Algorithms

    LLMs Are Randomized Algorithms A surprising connection between the newest AI models and a 50-year old academic field The post LLMs Are Randomized Algorithms appeared first on Towards Data Science. Udayan Kanade Go to original source

  • Tractable Instances of Bilinear Maximization: Implementing LinUCB on Ellipsoids

    Tractable Instances of Bilinear Maximization: Implementing LinUCB on Ellipsoids arXiv:2511.07504v1 Announce Type: new Abstract: We consider the maximization of $x^top theta$ over $(x,theta) in mathcal{X} times Theta$, with $mathcal{X} subset mathbb{R}^d$ convex and $Theta subset mathbb{R}^d$ an ellipsoid. This problem is fundamental in linear bandits, as the learner must solve it at every time step…

  • Infinite-Dimensional Operator/Block Kaczmarz Algorithms: Regret Bounds and $lambda$-Effectiveness

    Infinite-Dimensional Operator/Block Kaczmarz Algorithms: Regret Bounds and $lambda$-Effectiveness arXiv:2511.07604v1 Announce Type: new Abstract: We present a variety of projection-based linear regression algorithms with a focus on modern machine-learning models and their algorithmic performance. We study the role of the relaxation parameter in generalized Kaczmarz algorithms and establish a priori regret bounds with explicit $lambda$-dependence to…

  • QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design

    QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design arXiv:2410.07961v2 Announce Type: cross Abstract: Quantum computing is an emerging field recognized for the significant speedup it offers over classical computing through quantum algorithms. However, designing and implementing quantum algorithms pose challenges due to the complex nature of quantum mechanics and the necessity for precise control…

  • Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms

    Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms arXiv:2510.25811v1 Announce Type: new Abstract: We consider a stochastic multi-armed bandit problem with i.i.d. rewards where the expected reward function is multimodal with at most m modes. We propose the first known computationally tractable algorithm for computing the solution to the Graves-Lai optimization problem, which in turn…

  • Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals

    Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals arXiv:2509.15989v1 Announce Type: new Abstract: We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from…

  • The Relative Instability of Model Comparison with Cross-validation

    The Relative Instability of Model Comparison with Cross-validation arXiv:2508.04409v1 Announce Type: new Abstract: Existing work has shown that cross-validation (CV) can be used to provide an asymptotic confidence interval for the test error of a stable machine learning algorithm, and existing stability results for many popular algorithms can be applied to derive positive instances where…

  • How to Evaluate LLMs and Algorithms — The Right Way

    How to Evaluate LLMs and Algorithms — The Right Way Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more. Subscribe today! All the hard work it takes to integrate large language models and powerful algorithms into your workflows can go to waste…

  • Thompson Sampling-like Algorithms for Stochastic Rising Bandits

    Thompson Sampling-like Algorithms for Stochastic Rising Bandits arXiv:2505.12092v1 Announce Type: new Abstract: Stochastic rising rested bandit (SRRB) is a setting where the arms’ expected rewards increase as they are pulled. It models scenarios in which the performances of the different options grow as an effect of an underlying learning process (e.g., online model selection). Even…

  • Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms

    Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms arXiv:2504.20877v1 Announce Type: new Abstract: The objective of canonical multi-armed bandits is to identify and repeatedly select an arm with the largest reward, often in the form of the expected value of the arm’s probability distribution. Such a utilitarian perspective and focus on the probability models’ first…

  • Spectral Algorithms under Covariate Shift

    Spectral Algorithms under Covariate Shift arXiv:2504.12625v1 Announce Type: new Abstract: Spectral algorithms leverage spectral regularization techniques to analyze and process data, providing a flexible framework for addressing supervised learning problems. To deepen our understanding of their performance in real-world scenarios where the distributions of training and test data may differ, we conduct a rigorous investigation…

  • Survey on Algorithms for multi-index models

    Survey on Algorithms for multi-index models arXiv:2504.05426v1 Announce Type: new Abstract: We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases,…

  • Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect

    Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect arXiv:2502.04673v1 Announce Type: new Abstract: Estimation and inference for the Average Treatment Effect (ATE) is a cornerstone of causal inference and often serves as the foundation for developing procedures for more complicated settings. Although traditionally analyzed in a batch setting, recent advances in martingale theory…

  • Algorithms with Calibrated Machine Learning Predictions

    Algorithms with Calibrated Machine Learning Predictions arXiv:2502.02861v1 Announce Type: new Abstract: The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. While this theoretical framework often assumes uniform reliability across all predictions, modern machine learning models can now provide instance-level uncertainty estimates. In this paper,…

  • Optimizing Through Change: Bounds and Recommendations for Time-Varying Bayesian Optimization Algorithms

    Optimizing Through Change: Bounds and Recommendations for Time-Varying Bayesian Optimization Algorithms arXiv:2501.18963v1 Announce Type: new Abstract: Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying, expensive, noisy black-box function. However, most of the solutions proposed so far either rely on unrealistic assumptions on the nature of the objective function or do not…

  • On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models

    On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models arXiv:2412.14315v1 Announce Type: new Abstract: In a graph bisection problem, we are given a graph $G$ with two equally-sized unlabeled communities, and the goal is to recover the vertices in these communities. A popular heuristic, known as spectral clustering, is to output an estimated…