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HashChain: a family of very fast factor-based sublinear exact-matching string search algorithms.
https://github.com/nishihatapalmer/HashChain
saved 2025-03-14

GitHub - nishihatapalmer/HashChain: A very fast online search algorithm
A very fast online search algorithm. Contribute to nishihatapalmer/HashChain development by creating an account on GitHub.

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Zhirui Sun, Xingrong Diao, Yao Wang, Bi-Ke Zhu, Jiankun Wang
https://arxiv.org/abs/2506.14305

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Navigation in human-robot shared crowded environments remains challenging, as robots are expected to move efficiently while respecting human motion conventions. However, many existing approaches emphasize safety or efficiency while overlooking social awareness. This article proposes Learning-Risk Model Predictive Control (LR-MPC), a data-driven navigation algorithm that balances efficiency, safety, and social awareness. LR-MPC consists of two phases: an offline risk learning phase, where a Prob…

This https://arxiv.org/abs/2306.01952 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Online Control with Adversarial Disturbance for Continuous-time Linear Systems
We study online control for continuous-time linear systems with finite sampling rates, where the objective is to design an online procedure that learns under non-stochastic noise and performs comparably to a fixed optimal linear controller. We present a novel two-level online algorithm, by integrating a higher-level learning strategy and a lower-level feedback control strategy. This method offers a practical and robust solution for online control, which achieves sublinear regret. Our work provi…

Online Job Assignment
Farbod Ekbatani, Yiding Feng, Ian Kash, Rad Niazadeh
https://arxiv.org/abs/2506.06893 https://arxiv.org/pdf/250…

Online Job Assignment
Motivated primarily by applications in cloud computing, we study a simple, yet powerful, online allocation problem in which jobs of varying durations arrive over continuous time and must be assigned immediately and irrevocably to one of the available offline servers. Each server has a fixed initial capacity, with assigned jobs occupying one unit for their duration and releasing it upon completion. The algorithm earns a reward for each assignment upon completion. We consider a general heterogene…

e-GAI: e-value-based Generalized $\alpha$-Investing for Online False Discovery Rate Control
Yifan Zhang, Zijian Wei, Haojie Ren, Changliang Zou
https://arxiv.org/abs/2506.01452

e-GAI: e-value-based Generalized $α$-Investing for Online False Discovery Rate Control
Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $α$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from …

Optimized projection-free algorithms for online learning: construction and worst-case analysis
Julien Weibel (SIERRA), Pierre Gaillard (Thoth), Wouter M. Koolen (CWI), Adrien Taylor (SIERRA)
https://arxiv.org/abs/2506.05855

Optimized projection-free algorithms for online learning: construction and worst-case analysis
This work studies and develop projection-free algorithms for online learning with linear optimization oracles (a.k.a. Frank-Wolfe) for handling the constraint set. More precisely, this work (i) provides an improved (optimized) variant of an online Frank-Wolfe algorithm along with its conceptually simple potential-based proof, and (ii) shows how to leverage semidefinite programming to jointly design and analyze online Frank-Wolfe-type algorithms numerically in a variety of settings-that include …

Data-Driven Nonlinear Regulation: Gaussian Process Learning
Telema Harry, Martin Guay, Shimin Wang, Richard D. Braatz
https://arxiv.org/abs/2506.09273 http…

Data-Driven Nonlinear Regulation: Gaussian Process Learning
This article addresses the output regulation problem for a class of nonlinear systems using a data-driven approach. An output feedback controller is proposed that integrates a traditional control component with a data-driven learning algorithm based on Gaussian Process (GP) regression to learn the nonlinear internal model. Specifically, a data-driven technique is employed to directly approximate the unknown internal model steady-state map from observed input-output data online. Our method does …

Online Budget-Feasible Mechanism Design with Predictions
Georgios Amanatidis, Evangelos Markakis, Christodoulos Santorinaios, Guido Sch\"afer, Panagiotis Tsamopoulos, Artem Tsikiridis
https://arxiv.org/abs/2505.24624

Online Budget-Feasible Mechanism Design with Predictions
Augmenting the input of algorithms with predictions is an algorithm design paradigm that suggests leveraging a (possibly erroneous) prediction to improve worst-case performance guarantees when the prediction is perfect (consistency), while also providing a performance guarantee when the prediction fails (robustness). Recently, Xu and Lu [2022] and Agrawal et al. [2024] proposed to consider settings with strategic agents under this framework. In this paper, we initiate the study of budget-feasib…

This https://arxiv.org/abs/2501.19277 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

On Pareto Optimality for the Multinomial Logistic Bandit
We provide a new online learning algorithm for tackling the Multinomial Logit Bandit (MNL-Bandit) problem. Despite the challenges posed by the combinatorial nature of the MNL model, we develop a novel Upper Confidence Bound (UCB)-based method that achieves Pareto optimality by balancing regret minimization and estimation error of the assortment revenues and the MNL parameters. We develop theoretical guarantees characterizing the tradeoff between regret and estimation error for the MNL-Bandit pr…

Standing Tall: Robust Fall Prediction for Bipedal Robots
Gokul Prabhakaran, Jessy W. Grizzle, M. Eva Mungai
https://arxiv.org/abs/2506.01141 https://

Standing Tall: Robust Fall Prediction for Bipedal Robots
This paper extends the fall prediction algorithm from Mungai et al.(2024) to a real-time/online setting, implemented in both hardware and simulation. This yields results comparable to the offline version, maintaining a zero false positive rate, sufficient lead time, and accurate lead time prediction. Additionally, it achieves a high recovery rate. The paper also evaluates the fall prediction algorithm against omnidirectional faults and introduces an improved algorithm capable of reliably predic…

This https://arxiv.org/abs/2407.20941 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Random-Order Interval Selection
In the problem of online unweighted interval selection, the objective is to maximize the number of non-conflicting intervals accepted by the algorithm. In the conventional online model of irrevocable decisions, there is an Omega(n) lower bound on the competitive ratio, even for randomized algorithms [Bachmann et al. 2013]. In a line of work that allows for revocable acceptances, [Faigle and Nawijn 1995] gave a greedy 1-competitive (i.e. optimal) algorithm in the real-time model, where intervals…

This https://arxiv.org/abs/2411.07651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
The Poisson compound decision problem is a long-standing problem in statistics, where empirical Bayes methodologies are commonly used to estimate Poisson's means in static or batch domains. In this paper, we study the Poisson compound decision problem in a streaming or online domain. Adopting a quasi-Bayesian approach, referred to as Newton's algorithm, we obtain a sequential estimate that is easy to evaluate, computationally efficient, and maintain a constant per-observation computational cost…

Human-in-the-loop: Real-time Preference Optimization
Wenbin Wang, Wenjie Xu, Colin N. Jones
https://arxiv.org/abs/2506.02225 https://…

Human-in-the-loop: Real-time Preference Optimization
Human-aware controllers play an important role in engineering systems for improving productivity, efficiency, and sustainability. It is essential to design such a controller that optimizes user utility while adhering to plant dynamics. While most online optimization algorithms rely on first-order or zeroth-order oracles, human feedback often appears as pairwise comparisons. In this work, we propose an online feedback optimization algorithm that leverages such preference feedback. We design a co…