Tootfinder

Online Policy Learning and Inference by Matrix Completion
Congyuan Duan, Jingyang Li, Dong Xia
https://arxiv.org/abs/2404.17398 https://

Online Policy Learning and Inference by Matrix Completion
Making online decisions can be challenging when features are sparse and orthogonal to historical ones, especially when the optimal policy is learned through collaborative filtering. We formulate the problem as a matrix completion bandit (MCB), where the expected reward under each arm is characterized by an unknown low-rank matrix. The $ε$-greedy bandit and the online gradient descent algorithm are explored. Policy learning and regret performance are studied under a specific schedule for explor…

Sound Concurrent Traces for Online Monitoring Technical Report
Chukri Soueidi, Ylies Falcone
https://arxiv.org/abs/2402.18391 https://

Sound Concurrent Traces for Online Monitoring Technical Report
Monitoring concurrent programs typically rely on collecting traces to abstract program executions. However, existing approaches targeting general behavioral properties are either not tailored for online monitoring, are no longer maintained, or implement naive instrumentation that often leads to unsound verdicts. We first define the notion of when a trace is representative of a concurrent execution. We then present a non-blocking vector clock algorithm to collect sound concurrent traces on the f…

This https://arxiv.org/abs/2311.00656 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Online Signal Estimation on the Graph Edges via Line Graph Transformation
The processing of signals on graph edges is challenging considering that Graph Signal Processing techniques are defined only on the graph nodes. Leveraging the Line Graph to transform a graph edge signal onto the node of its edge-to-vertex dual, we propose the Line Graph Least Mean Square (LGLMS) algorithm for online time-varying graph edge signal prediction. By setting up an $l_2$-norm optimization problem, LGLMS forms an adaptive algorithm as the graph edge analogy of the classical adaptive L…

This https://arxiv.org/abs/2005.01867 has been replaced.
link: https://scholar.google.com/scholar?q=a

Removable Online Knapsack and Advice
In the knapsack problem, we are given a knapsack of some capacity and a set of items, each with a size and a value. The goal is to pack a selection of these items fitting the knapsack that maximizes the total value. The online version of this problem reveals the items one by one. For each item, the algorithm must decide immediately whether to pack it or not. We consider a natural variant of this problem, coined removable online knapsack. It differs from the classical variant by allowing the rem…

This https://arxiv.org/abs/2403.07728 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
We study the problem of post-selection predictive inference in an online fashion. To avoid devoting resources to unimportant units, a preliminary selection of the current individual before reporting its prediction interval is common and meaningful in online predictive tasks. Since the online selection causes a temporal multiplicity in the selected prediction intervals, it is important to control the real-time false coverage-statement rate (FCR) which measures the overall miscoverage level. We d…

This https://arxiv.org/abs/2211.14361 has been replaced.
link: https://scholar.google.com/scholar?q=a

gatekeeper: Online Safety Verification and Control for Nonlinear Systems in Dynamic Environments
This paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method that ensures that trajectories of a nonlinear system satisfy safety constraints despite sensing limitations. gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step to ensure that proposed trajectories can be executed safely, despite nonlinear dynamics subject to bounded disturbances, input constraints and partial knowledge of the env…

Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu, Kay Chen Tan
https://arxiv.org/abs/2402.15969 https://arxiv.org/pdf/2402.15969
arXiv:2402.15969v1 Announce Type: new
Abstract: The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals. Recently, a novel multi-compartment spiking neuron model, namely the Two-Compartment LIF (TC-LIF) model, has been proposed and exhibited a remarkable capacity for sequential modelling. However, training the TC-LIF model presents challenges stemming from the large memory consumption and the issue of gradient vanishing associated with the Backpropagation Through Time (BPTT) algorithm. To address these challenges, online learning methodologies emerge as a promising solution. Yet, to date, the application of online learning methods in SNNs has been predominantly confined to simplified Leaky Integrate-and-Fire (LIF) neuron models. In this paper, we present a novel online learning method specifically tailored for networks of TC-LIF neurons. Additionally, we propose a refined TC-LIF neuron model called Adaptive TC-LIF, which is carefully designed to enhance temporal information integration in online learning scenarios. Extensive experiments, conducted on various sequential benchmarks, demonstrate that our approach successfully preserves the superior sequential modeling capabilities of the TC-LIF neuron while incorporating the training efficiency and hardware friendliness of online learning. As a result, it offers a multitude of opportunities to leverage neuromorphic solutions for processing temporal signals.

> She worries the negative effects will spread as the technology does. "One biased human hiring manager can harm a lot of people in a year, and that's not great," she says. "But an algorithm that is maybe used in all incoming applications at a large company… that could harm hundreds of thousands of applicants."

AI hiring tools may be filtering out the best job applicants
As firms increasingly rely on artificial intelligence-driven hiring platforms, many highly qualified candidates are finding themselves on the cutting room floor.

This https://arxiv.org/abs/2302.01421 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Follower Agnostic Methods for Stackelberg Games
In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner. Unlike previous works, our approach works even when leader has no knowledge about the followers' utility functions or strategy space. Our algorithm introduces a unique gradient estimator, leveraging specially designed strategies to probe followers. In a departure from traditional assumptions of optimal play, we model followers' responses using a conver…

This anti-AOC tweet has over 9 MILLION views —with a pedestrian number of likes and retweets.
This isn’t normal. This is likely the result of platform and algorithm manipulation — exactly what Musk wanted, and why he bought Twitter. Musk is attacking AOC for political advantage.

Online Submodular Welfare Maximization Meets Post-Allocation Stochasticity and Reusability
Rajan Udwani
https://arxiv.org/abs/2403.18059 https://

Online Submodular Welfare Maximization Meets Post-Allocation Stochasticity and Reusability
We generalize the problem of online submodular welfare maximization to incorporate a variety of new elements arising from reusability, stochastic rewards, combinatorial actions and similar features that have received significant attention in recent years. For our general formulation, we show that a non-adaptive Greedy algorithm achieves the highest possible competitive ratio against an adaptive offline benchmark in the adversarial arrival model and in the unknown IID stochastic arrival model. I…

This https://arxiv.org/abs/2302.01421 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Follower Agnostic Methods for Stackelberg Games
In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner. Unlike previous works, our approach works even when leader has no knowledge about the followers' utility functions or strategy space. Our algorithm introduces a unique gradient estimator, leveraging specially designed strategies to probe followers. In a departure from traditional assumptions of optimal play, we model followers' responses using a conver…

This anti-AOC tweet has over 9 MILLION views —with a pedestrian number of likes and retweets.
This isn’t normal. This is likely the result of platform and algorithm manipulation — exactly what Musk wanted, and why he bought Twitter. Musk is attacking AOC for political advantage.

This https://arxiv.org/abs/2304.06565 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

List Update with Delays or Time Windows
We consider the problem of List Update, one of the most fundamental problems in online algorithms. We are given a list of elements and requests for these elements that arrive over time. Our goal is to serve these requests, at a cost equivalent to their position in the list, with the option of moving them towards the head of the list. Sleator and Tarjan introduced the famous "Move to Front" algorithm (wherein any requested element is immediately moved to the head of the list) and showed that it …

This https://arxiv.org/abs/2201.10017 has been replaced.
link: https://scholar.google.com/scholar?q=a

Online Convex Optimization Using Coordinate Descent Algorithms
This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coo…

Has anybody written about the near-ubiquitous online trend of bowdlerizing text, either to avoid being penalized by the algorithm (real or imagined) or to avoid content being searchable by others? We have people who communicate about "s*x" instead of "sex" and... that's probably a very bad thing.

Subgame Optimal and Prior-Independent Online Algorithms
Jason Hartline, Aleck Johnsen, Anant Shah
https://arxiv.org/abs/2403.10451 https://

Subgame Optimal and Prior-Independent Online Algorithms
This paper takes a game theoretic approach to the design and analysis of online algorithms and illustrates the approach on the finite-horizon ski-rental problem. This approach allows beyond worst-case analysis of online algorithms. First, we define "subgame optimality" which is stronger than worst case optimality in that it requires the algorithm to take advantage of an adversary not playing a worst case input. Algorithms only focusing on the worst case can be far from subgame optimal. Second, …

This https://arxiv.org/abs/2402.09824 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the discrete-time origins of the replicator dynamics: From convergence to instability and chaos
We consider three distinct discrete-time models of learning and evolution in games: a biological model based on intra-species selective pressure, the dynamics induced by pairwise proportional imitation, and the exponential / multiplicative weights (EW) algorithm for online learning. Even though these models share the same continuous-time limit - the replicator dynamics - we show that second-order effects play a crucial role and may lead to drastically different behaviors in each model, even in …

From Batch to Stream: Automatic Generation of Online Algorithms
Ziteng Wang, Shankara Pailoor, Aaryan Prakash, Yuepeng Wang, Isil Dillig
https://arxiv.org/abs/2404.04743

From Batch to Stream: Automatic Generation of Online Algorithms
Online streaming algorithms, tailored for continuous data processing, offer substantial benefits but are often more intricate to design than their offline counterparts. This paper introduces a novel approach for automatically synthesizing online streaming algorithms from their offline versions. In particular, we propose a novel methodology, based on the notion of relational function signature (RFS), for deriving an online algorithm given its offline version. Then, we propose a concrete synthesi…

"So, what’s the fun of writing on the internet anymore? Well, if your aim is to be respected as an author, there’s probably not much fun to be had here at all. Don’t write online for fame & glory. Write here because ideas matter, not authorship. Write here because the more robots, pirates…the more we need independent writing in order to think new thoughts in the future—even if your words are getting dished up and plated by an algorithm."

What’s the fun in writing on the internet anymore?
The moment you release your words, you relinquish all control.

Unraveling Contagion Origins: Optimal Estimation through Maximum-Likelihood and Starlike Tree Approximation in Markovian Spreading Models
Pei-Duo Yu, Chee Wei Tan, Liang Zheng, Chao Zhao
https://arxiv.org/abs/2403.14890

Unraveling Contagion Origins: Optimal Estimation through Maximum-Likelihood and Starlike Tree Approximation in Markovian Spreading Models
Identifying the source of epidemic-like spread in networks is crucial for tasks like removing internet viruses or finding the rumor source in online social networks. The challenge lies in tracing the source from a snapshot observation of infected nodes. How do we accurately pinpoint the source? Utilizing snapshot data, we apply a probabilistic approach, focusing on the graph boundary and the observed time, to detect sources via an effective maximum likelihood algorithm. A novel starlike tree ap…

This https://arxiv.org/abs/2402.12734 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

On the Permutation Algorithm for Online Facility Assignment on a Line
In the online facility assignment on a line (OFAL) with a set $S$ of $k$ servers and a capacity $c:S\to\mathbb{N}$, each server $s\in S$ with a capacity $c(s)$ is placed on a line and a request arrives on a line one-by-one. The task of an online algorithm is to irrevocably assign a current request to one of the servers with vacancies before the next request arrives. An algorithm can assign up to $c(s)$ requests to each server $s\in S$. In this paper, we show that the competitive ratio of the …

This https://arxiv.org/abs/2308.08138 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Data-Driven Adversarial Online Control for Unknown Linear Systems
We consider the online control problem with an unknown linear dynamical system in the presence of adversarial perturbations and adversarial convex loss functions. Although the problem is widely studied in model-based control, it remains unclear whether data-driven approaches, which bypass the system identification step, can solve the problem. In this work, we present a novel data-driven online adaptive control algorithm to address this online control problem. Our algorithm leverages the behavio…

This https://arxiv.org/abs/2402.12734 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

On the Permutation Algorithm for Online Facility Assignment on a Line
In the online facility assignment on a line (OFAL) with a set $S$ of $k$ servers and a capacity $c:S\to\mathbb{N}$, each server $s\in S$ with a capacity $c(s)$ is placed on a line and a request arrives on a line one-by-one. The task of an online algorithm is to irrevocably assign a current request to one of the servers with vacancies before the next request arrives. An algorithm can assign up to $c(s)$ requests to each server $s\in S$. In this paper, we show that the competitive ratio of the …

Online Differentially Private Synthetic Data Generation
Yiyun He, Roman Vershynin, Yizhe Zhu
https://arxiv.org/abs/2402.08012 https://

Online Differentially Private Synthetic Data Generation
We present a polynomial-time algorithm for online differentially private synthetic data generation. For a data stream within the hypercube $[0,1]^d$ and an infinite time horizon, we develop an online algorithm that generates a differentially private synthetic dataset at each time $t$. This algorithm achieves a near-optimal accuracy bound of $O(t^{-1/d}\log(t))$ for $d\geq 2$ and $O(t^{-1}\log^{4.5}(t))$ for $d=1$ in the 1-Wasserstein distance. This result generalizes the previous work on the co…

(a) If you think "reply guys" are a problem, count yourself lucky that they can at least read for meaning
(b) Israel-Palestine makes some people completely lose their minds; don't take the crazies seriously
(c) It is not some evil algorithm or the mechanism of quote tweets (alone) that poison online discourse
(d) In my job, we've known for many decades that the letter-writers aren't reflective of the audience. Never mistake them for real life.

Action Model Learning with Guarantees
Diego Aineto, Enrico Scala
https://arxiv.org/abs/2404.09631 https://arxiv.org/pdf/2404.09631

Action Model Learning with Guarantees
This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical findings are instantiated in an online algorithm that maintains a compact representation of all solutions of the problem. Among these range of solutions, we bring attention to act…

Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm
Luca Beber, Edoardo Lamon, Giacomo Moretti, Daniele Fontanelli, Matteo Saveriano, Luigi Palopoli
https://arxiv.org/abs/2404.09542

Towards Robotised Palpation for Cancer Detection through Online Tissue Viscoelastic Characterisation with a Collaborative Robotic Arm
This paper introduces a new method for estimating the penetration of the end effector and the parameters of a soft body using a collaborative robotic arm. This is possible using the dimensionality reduction method that simplifies the Hunt-Crossley model. The parameters can be found without a force sensor thanks to the information of the robotic arm controller. To achieve an online estimation, an extended Kalman filter is employed, which embeds the contact dynamic model. The algorithm is tested …

Scene: A Google conference room.
Project manager: OK, we need something new here. What are we thinking?
Developer 1: How about glasses that …
Manager: No.
Developer 2: What if we make phones that drive cars?
Manager: Oh we’re coming back to that, but what can we do right now?
Developer3: (Nervously) You know how we’re the best in the world at searching things?
Manager: Yeah?
Developer3: Let’s ruin it.
Manager: Up top!

Google wants to redirect traffic from AI-generated spam blogs to legit websites
Google is one of the most important vectors for online spammers and scammers. The search engine's dominance means that, if you want to generate traffic — a.k.a. potential marks — you'll probably need to convince Google's algorithm to send it your way. And there's a good chance the resulting tra…

Online Policy Optimization in Unknown Nonlinear Systems
Yiheng Lin, James A. Preiss, Fengze Xie, Emile Anand, Soon-Jo Chung, Yisong Yue, Adam Wierman
https://arxiv.org/abs/2404.13009

Online Policy Optimization in Unknown Nonlinear Systems
We study online policy optimization in nonlinear time-varying dynamical systems where the true dynamical models are unknown to the controller. This problem is challenging because, unlike in linear systems, the controller cannot obtain globally accurate estimations of the ground-truth dynamics using local exploration. We propose a meta-framework that combines a general online policy optimization algorithm ($\texttt{ALG}$) with a general online estimator of the dynamical system's model parameters…

Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
Mohamed Abdalmoaty, Efe C. Balta, John Lygeros, Roy S. Smith
https://arxiv.org/abs/2403.05899 …

Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identificatio…

Numerical complexity of helix unraveling algorithm for charged particle tracking
Kacper Topolnicki, Tomasz Bold
https://arxiv.org/abs/2402.07142 https://…

Numerical complexity of helix unraveling algorithm for charged particle tracking
This paper describes a procedure for a realistic estimation of the number of iterations in the main loop of a recent particle detection algorithm from [1]. The calculations are based on a Monte Carlo simulation of the ATLAS inner detector. The resulting estimates of numerical complexity suggest that using the procedure from [1] for online triggering is not feasible. There are however some areas, such as triggering for particles in a specific sub-domain of the phase space, where using this proce…

A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
Hanxi Li, Zhengxun Zhang, Hao Chen, Lin Wu, Bo Li, Deyin Liu, Mingwen Wang
https://arxiv.org/abs/2402.19330

A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts. This paper introduces a novel algorithm designed to augment defective samples, thereby enhancing AD performance. The proposed method tailors the blended latent diffusion model for defect sample generation, employing a diffusion model to generate defective samples in the latent space. A feature editing p…

CAS: A General Algorithm for Online Selective Conformal Prediction with FCR Control
Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou
https://arxiv.org/abs/2403.07728

CAS: A General Algorithm for Online Selective Conformal Prediction with FCR Control
We study the problem of post-selection predictive inference in an online fashion. To avoid devoting resources to unimportant units, a preliminary selection of the current individual before reporting its prediction interval is common and meaningful in online predictive tasks. Since the online selection causes a temporal multiplicity in the selected prediction intervals, it is important to control the real-time false coverage-statement rate (FCR) to measure the averaged miscoverage error. We deve…

The Competition Complexity of Prophet Inequalities
Johannes Brustle, Jos\'e Correa, Paul D\"utting, Tomer Ezra, Michal Feldman, Victor Verdugo
https://arxiv.org/abs/2402.11084

The Competition Complexity of Prophet Inequalities
We study the classic single-choice prophet inequality problem through a resource augmentation lens. Our goal is to bound the $(1-\varepsilon)$-competition complexity of different types of online algorithms. This metric asks for the smallest $k$ such that the expected value of the online algorithm on $k$ copies of the original instance, is at least a $(1-\varepsilon)$-approximation to the expected offline optimum on a single copy. We show that block threshold algorithms, which set one threshol…

Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization
Aadirupa Saha, Pierre Gaillard
https://arxiv.org/abs/2402.18917

Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization
We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various real-world applications including ad placement, online retail, recommender systems, fine-tuning language models, amongst many. The problem, although has been studied in the past, lacks an intuitive and practical solution approach with simultaneously efficient algorithm and optimal …

Error Checking for Sparse Systolic Tensor Arrays
Christodoulos Peltekis, Dionysios Filippas, Giorgos Dimitrakopoulos
https://arxiv.org/abs/2402.10850 https…

Error Checking for Sparse Systolic Tensor Arrays
Structured sparsity is an efficient way to prune the complexity of modern Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. In such cases, the acceleration of structured-sparse ML models is handled by sparse systolic tensor arrays. The increasing prevalence of ML in safety-critical systems requires enhancing the sparse tensor arrays with online error detection for managing random hardware failures. Algorithm-based fault tolerance has been proposed as a …

This https://arxiv.org/abs/2310.02195 has been replaced.
link: https://scholar.google.com/scholar?q=a

Efficient Online Scheduling and Routing for Automated Guided Vehicles In Loop-Based Graphs
Automated guided vehicles (AGVs) are widely used in various industries, and scheduling and routing them in a conflict-free manner is crucial to their efficient operation. We propose a loop-based algorithm that solves the online, conflict-free scheduling and routing problem for AGVs with any capacity and ordered jobs in loop-based graphs. The proposed algorithm is compared against an exact method, a greedy heuristic and a metaheuristic. We experimentally show, using theoretical and real instance…

This https://arxiv.org/abs/2404.06834 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Solving Parametric PDEs with Radial Basis Functions and Deep Neural Networks
We propose the POD-DNN, a novel algorithm leveraging deep neural networks (DNNs) along with radial basis functions (RBFs) in the context of the proper orthogonal decomposition (POD) reduced basis method (RBM), aimed at approximating the parametric mapping of parametric partial differential equations on irregular domains. The POD-DNN algorithm capitalizes on the low-dimensional characteristics of the solution manifold for parametric equations, alongside the inherent offline-online computational …

Forward Direct Feedback Alignment for Online Gradient Estimates of Spiking Neural Networks
Florian Bacho, Dminique Chu
https://arxiv.org/abs/2403.08804 htt…

Forward Direct Feedback Alignment for Online Gradient Estimates of Spiking Neural Networks
There is an interest in finding energy efficient alternatives to current state of the art neural network training algorithms. Spiking neural network are a promising approach, because they can be simulated energy efficiently on neuromorphic hardware platforms. However, these platforms come with limitations on the design of the training algorithm. Most importantly, backpropagation cannot be implemented on those. We propose a novel neuromorphic algorithm, the \textit{Spiking Forward Direct Feedbac…

Cost-Driven Data Replication with Predictions
Tianyu Zuo, Xueyan Tang, Bu Sung Lee
https://arxiv.org/abs/2404.16489 https://arxiv.org…

Cost-Driven Data Replication with Predictions
This paper studies an online replication problem for distributed data access. The goal is to dynamically create and delete data copies in a multi-server system as time passes to minimize the total storage and network cost of serving access requests. We study the problem in the emergent learning-augmented setting, assuming simple binary predictions about inter-request times at individual servers. We develop an online algorithm and prove that it is ($\frac{5+α}{3}$)-consistent (competitiveness u…

Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments
Lidia Gianne Souza da Rocha, Kenny Anderson Queiroz Caldas, Marco Henrique Terra, Fabio Ramos, Kelen Cristiane Teixeira Vivaldini
https://arxiv.org/abs/2402.06297

Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments
Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms reported in the literature may not return reliable trajectories to solve online problems in these scenarios. The Q-Learning algorithm, a Reinforcement Learning Technique, can generate trajectories in real-time and has demonstrated fast and reliable results. This technique, however, has the disadvantage of defining th…

This https://arxiv.org/abs/2312.01996 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors
Online Feedback Optimization (OFO) controllers steer a system to its optimal operating point by treating optimization algorithms as auxiliary dynamic systems. Implementation of OFO controllers requires setting the parameters of the optimization algorithm that allows reaching convergence, posing a challenge because the convergence of the optimization algorithm is often decoupled from the performance of the controlled system. OFO controllers are also typically designed to ensure steady-state trac…

Cost-Driven Data Replication with Predictions
Tianyu Zuo, Xueyan Tang, Bu Sung Lee
https://arxiv.org/abs/2404.16489 https://arxiv.org…

Cost-Driven Data Replication with Predictions
This paper studies an online replication problem for distributed data access. The goal is to dynamically create and delete data copies in a multi-server system as time passes to minimize the total storage and network cost of serving access requests. We study the problem in the emergent learning-augmented setting, assuming simple binary predictions about inter-request times at individual servers. We develop an online algorithm and prove that it is ($\frac{5+α}{3}$)-consistent (competitiveness u…

Online Flexible Busy Time Scheduling on Heterogeneous Machines
Gruia Calinescu, Sami Davies, Samir Khuller, Shirley Zhang
https://arxiv.org/abs/2402.11109 …

Online Flexible Busy Time Scheduling on Heterogeneous Machines
We study the online busy time scheduling model on heterogeneous machines. In our setting, unit-length jobs arrive online with a deadline that is known to the algorithm at the job's arrival time. An algorithm has access to machines, each with different associated capacities and costs. The goal is to schedule jobs on machines before their deadline, so that the total cost incurred by the scheduling algorithm is minimized. Relatively little is known about online busy time scheduling when machines a…

This https://arxiv.org/abs/2309.15498 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Control Theoretical Approach to Online Constrained Optimization
In this paper we focus on the solution of online problems with time-varying, linear equality and inequality constraints. Our approach is to design a novel online algorithm by leveraging the tools of control theory. In particular, for the case of equality constraints only, using robust control we design an online algorithm with asymptotic convergence to the optimal trajectory, differently from the alternatives that achieve non-zero tracking error. When also inequality constraints are present, we…

This https://arxiv.org/abs/2402.09495 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

On the Potential of Network-Based Features for Fraud Detection
Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer a promising solution by leveraging historical data to identify fraudulent patterns. This article explores using the personalised PageRank (PPR) algorithm to capture the social dynamics of fraud by a…

Numerical complexity of helix unraveling algorithm for charged particle tracking
Kacper Topolnicki, Tomasz Bold
https://arxiv.org/abs/2402.07142 https://…

Numerical complexity of helix unraveling algorithm for charged particle tracking
This paper describes a procedure for a realistic estimation of the number of iterations in the main loop of a recent particle detection algorithm from [1]. The calculations are based on a Monte Carlo simulation of the ATLAS inner detector. The resulting estimates of numerical complexity suggest that using the procedure from [1] for online triggering is not feasible. There are however some areas, such as triggering for particles in a specific sub-domain of the phase space, where using this proce…

Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework
Jiyuan Pei, Jialin Liu, Yi Mei
https://arxiv.org/abs/2404.10252

Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework
In many practical applications, usually, similar optimisation problems or scenarios repeatedly appear. Learning from previous problem-solving experiences can help adjust algorithm components of meta-heuristics, e.g., adaptively selecting promising search operators, to achieve better optimisation performance. However, those experiences obtained from previously solved problems, namely offline experiences, may sometimes provide misleading perceptions when solving a new problem, if the characterist…

This https://arxiv.org/abs/2304.00707 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
We derive high-dimensional scaling limits and fluctuations for the online least-squares Stochastic Gradient Descent (SGD) algorithm by taking the properties of the data generating model explicitly into consideration. Our approach treats the SGD iterates as an interacting particle system, where the expected interaction is characterized by the covariance structure of the input. Assuming smoothness conditions on moments of order up to eight orders, and without explicitly assuming Gaussianity, we e…

This https://arxiv.org/abs/2303.00047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Online On-Demand Multi-Robot Coverage Path Planning
We present an online centralized path planning algorithm to cover a large, complex, unknown workspace with multiple homogeneous mobile robots. Our algorithm is horizon-based, synchronous, and on-demand. The recently proposed horizon-based synchronous algorithms compute all the robots' paths in each horizon, significantly increasing the computation burden in large workspaces with many robots. As a remedy, we propose an algorithm that computes the paths for a subset of robots that have traversed …

Nonlinear identification algorithm for online and offline study of pulmonary mechanical ventilation
Diego A. Riva, Carolina A. Evangelista, Paul F. Puleston, Luis Corsiglia, Nahuel Dargains
https://arxiv.org/abs/2402.18709

Nonlinear identification algorithm for online and offline study of pulmonary mechanical ventilation
This work presents an algorithm for determining the parameters of a nonlinear dynamic model of the respiratory system in patients undergoing assisted ventilation. Using the pressure and flow signals measured at the mouth, the model's quadratic pressure-volume characteristic is fit to this data in each respiratory cycle by appropriate estimates of the model parameters. Parameter changes during ventilation can thus also be detected. The algorithm is first refined and assessed using data derived f…

This https://arxiv.org/abs/2305.07164 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to…

This https://arxiv.org/abs/2309.15498 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Control Theoretical Approach to Online Constrained Optimization
In this paper we focus on the solution of online problems with time-varying, linear equality and inequality constraints. Our approach is to design a novel online algorithm by leveraging the tools of control theory. In particular, for the case of equality constraints only, using robust control we design an online algorithm with asymptotic convergence to the optimal trajectory, differently from the alternatives that achieve non-zero tracking error. When also inequality constraints are present, we…

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta
https://arxiv.org/abs/2403.03552

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a deep reinforcement learning (DRL) algorithm that achieves population-dependent Nash equilibrium without the need for averaging or sampling from history, inspired by Munchausen RL and Online Mirror Descent. Through the design of an additional inner-loop replay buffer, the agents can effectively learn to achieve Nash equil…

This https://arxiv.org/abs/2305.07164 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to…

This https://arxiv.org/abs/2303.00047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Online On-Demand Multi-Robot Coverage Path Planning
We present an online centralized path planning algorithm to cover a large, complex, unknown workspace with multiple homogeneous mobile robots. Our algorithm is horizon-based, synchronous, and on-demand. The recently proposed horizon-based synchronous algorithms compute all the robots' paths in each horizon, significantly increasing the computation burden in large workspaces with many robots. As a remedy, we propose an algorithm that computes the paths for a subset of robots that have traversed …

Time Efficient Implementation for Online $k$-server Problem on Trees
Kamil Khadiev, Maxim Yagafarov
https://arxiv.org/abs/2402.14633 https://

Time Efficient Implementation for Online $k$-server Problem on Trees
We consider online algorithms for the $k$-server problem on trees of size $n$. Chrobak and Larmore proposed a $k$-competitive algorithm for this problem that has the optimal competitive ratio. However, the existing implementations have $O\left(k^2 + k\cdot \log n\right)$ or $O\left(k(\log n)^2\right)$ time complexity for processing a query, where $n$ is the number of nodes. We propose a new time-efficient implementation of this algorithm that has $O(n)$ time complexity for preprocessing and $O\…

Solving Parametric PDEs with Radial Basis Functions and Deep Neural Networks
Guanhang Lei, Zhen Lei, Lei Shi, Chenyu Zeng
https://arxiv.org/abs/2404.06834 …

Solving Parametric PDEs with Radial Basis Functions and Deep Neural Networks
We propose the POD-DNN, a novel algorithm leveraging deep neural networks (DNNs) along with radial basis functions (RBFs) in the context of the proper orthogonal decomposition (POD) reduced basis method (RBM), aimed at approximating the parametric mapping of parametric partial differential equations on irregular domains. The POD-DNN algorithm capitalizes on the low-dimensional characteristics of the solution manifold for parametric equations, alongside the inherent offline-online computational …

Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
Yu Zhang, Long Wen, Xiangtong Yao, Zhenshan Bing, Linghuan Kong, Wei He, Alois Knoll
https://arxiv.org/abs/2402.18946

Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, …

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta
https://arxiv.org/abs/2403.03552

Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a deep reinforcement learning (DRL) algorithm that achieves population-dependent Nash equilibrium without the need for averaging or sampling from history, inspired by Munchausen RL and Online Mirror Descent. Through the design of an additional inner-loop replay buffer, the agents can effectively learn to achieve Nash equil…

Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots
Taku Okawara, Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno, Kentaro Uno, Kazuya Yoshida
https://arxiv.org/abs/2404.02515

Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots
Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments. To tackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm with an online calibration for skid-steering robots. We propose a full linear wheel odometry factor, which not only serves as a motion constraint but also performs the online calibration of kinematic models for skid-steering robots. Despite the dy…

Unweighted Layered Graph Traversal
Xingjian Bai, Christian Coester, Romain Cosson
https://arxiv.org/abs/2404.16176 https://arxiv.org/…

Unweighted Layered Graph Traversal
Introduced by Papadimitriou and Yannakakis in 1989, layered graph traversal is an important problem in online algorithms and mobile computing that has been studied for several decades, and which now is essentially resolved in its original formulation. In this paper, we demonstrate that what appears to be an innocuous modification of the problem actually leads to a drastic (exponential) reduction of the competitive ratio. Specifically, we present an algorithm that is $O(\log^2 w)$-competitive fo…

This https://arxiv.org/abs/2210.08886 has been replaced.
link: https://scholar.google.com/scholar?q=a

Learning Decentralized Linear Quadratic Regulator with $\sqrt{T}$ Regret
We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. Under the assumption that the system is stable or given a known stabilizing controller, we show that our…

Unweighted Layered Graph Traversal
Xingjian Bai, Christian Coester, Romain Cosson
https://arxiv.org/abs/2404.16176 https://arxiv.org/…

Unweighted Layered Graph Traversal
Introduced by Papadimitriou and Yannakakis in 1989, layered graph traversal is an important problem in online algorithms and mobile computing that has been studied for several decades, and which now is essentially resolved in its original formulation. In this paper, we demonstrate that what appears to be an innocuous modification of the problem actually leads to a drastic (exponential) reduction of the competitive ratio. Specifically, we present an algorithm that is $O(\log^2 w)$-competitive fo…

This https://arxiv.org/abs/2210.08886 has been replaced.
link: https://scholar.google.com/scholar?q=a

Learning Decentralized Linear Quadratic Regulator with $\sqrt{T}$ Regret
We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. Under the assumption that the system is stable or given a known stabilizing controller, we show that our…

This https://arxiv.org/abs/2304.01901 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control
In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling our approach is that the parameter estimate generated by the continuous-time recursive least squares (RLS) algorithm at any point in time is an affine transformation of the initial parameter estimate. This property allows for parameterizing such estimates using objects that are closed under affine transformation, such a…

This https://arxiv.org/abs/2305.01420 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

A Subquadratic Bound for Online Bisection
The online bisection problem is a natural dynamic variant of the classic optimization problem, where one has to dynamically maintain a partition of $n$ elements into two clusters of cardinality $n/2$. During runtime, an online algorithm is given a sequence of requests, each being a pair of elements: an inter-cluster request costs one unit while an intra-cluster one is free. The algorithm may change the partition, paying a unit cost for each element that changes its cluster. This natural probl…

Body Schema Acquisition through Active Learning
Ruben Martinez-Cantin, Manuel Lopes, Luis Montesano
https://arxiv.org/abs/2402.06067 https://

Body Schema Acquisition through Active Learning
We present an active learning algorithm for the problem of body schema learning, i.e. estimating a kinematic model of a serial robot. The learning process is done online using Recursive Least Squares (RLS) estimation, which outperforms gradient methods usually applied in the literature. In addiction, the method provides the required information to apply an active learning algorithm to find the optimal set of robot configurations and observations to improve the learning process. By selecting the…

Random-Order Online Interval Scheduling and Geometric Generalizations
Mohit Garg, Debajyoti Kar, Arindam Khan
https://arxiv.org/abs/2402.14201 https://

Random-Order Online Interval Scheduling and Geometric Generalizations
In the Maximum Independent Set of Hyperrectangles problem, we are given a set of $n$ (possibly overlapping) $d$-dimensional axis-aligned hyperrectangles, and the goal is to find a subset of non-overlapping hyperrectangles of maximum cardinality. For $d=1$, this corresponds to the classical Interval Scheduling problem, where a simple greedy algorithm returns an optimal solution. In the offline setting, for $d$-dimensional hyperrectangles, polynomial time $(\log n)^{O(d)}$-approximation algorithm…

An Online Algorithm for Solving Feedback Optimal Control Problems with Partial Observations
Siming Liang, Ruoyu Hu, Feng Bao, Richard Archibald, Guannan Zhang
https://arxiv.org/abs/2404.05734

An Online Algorithm for Solving Feedback Optimal Control Problems with Partial Observations
This paper presents a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial observation of the state process, a common occurrence in practical scenarios. Traditional stochastic optimal control methods assume full state observation, which is often not feasible in real-world applications. Our approach underscores the significance…

This https://arxiv.org/abs/2304.01901 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control
In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling our approach is that the parameter estimate generated by the continuous-time recursive least squares (RLS) algorithm at any point in time is an affine transformation of the initial parameter estimate. This property allows for parameterizing such estimates using objects that are closed under affine transformation, such a…

This https://arxiv.org/abs/2303.02407 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Local Path Planning among Pushable Objects based on Reinforcement Learning
In this paper, we introduce a method to deal with the problem of robot local path planning among pushable objects -- an open problem in robotics. In particular, we achieve that by training multiple agents simultaneously in a physics-based simulation environment, utilizing an Advantage Actor-Critic algorithm coupled with a deep neural network. The developed online policy enables these agents to push obstacles in ways that are not limited to axial alignments, adapt to unforeseen changes in obstac…

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
Jonathan Frey, Armin Nurkanovic, Moritz Diehl
https://arxiv.org/abs/2403.07101

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
The Real-Time Iteration (RTI) is an online nonlinear model predictive control algorithm that performs a single Sequential Quadratic Programming (SQP) per sampling time. The algorithm is split into a preparation and a feedback phase, where the latter one performs as little computations as possible solving a single prepared quadratic program. To further improve the accuracy of this method, the Advanced-Step RTI (AS-RTI) performs additional Multi-Level Iterations (MLI) in the preparation phase, su…

This https://arxiv.org/abs/2306.16674 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Online learning for robust voltage control under uncertain grid topology
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is challenging for existing methods, especially as the grid is subject to increasingly frequent reconfiguration due to the adoption of renewable energy. In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online …

This https://arxiv.org/abs/2208.03367 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sublinear Time Algorithm for Online Weighted Bipartite Matching
Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. Currently, in the practical recommendation system or search engine, the weights are decided by the inner product between the deep representation of a user and the deep representation of an item. The standard online matching needs to pay $nd$ t…

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
Jonathan Frey, Armin Nurkanovic, Moritz Diehl
https://arxiv.org/abs/2403.07101

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
The Real-Time Iteration (RTI) is an online nonlinear model predictive control algorithm that performs a single Sequential Quadratic Programming (SQP) per sampling time. The algorithm is split into a preparation and a feedback phase, where the latter one performs as little computations as possible solving a single prepared quadratic program. To further improve the accuracy of this method, the Advanced-Step RTI (AS-RTI) performs additional Multi-Level Iterations (MLI) in the preparation phase, su…

This https://arxiv.org/abs/2208.03367 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sublinear Time Algorithm for Online Weighted Bipartite Matching
Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. Currently, in the practical recommendation system or search engine, the weights are decided by the inner product between the deep representation of a user and the deep representation of an item. The standard online matching needs to pay $nd$ t…

Learning-augmented Online Algorithm for Two-level Ski-rental Problem
Keyuan Zhang, Zhongdong Liu, Nakjung Choi, Bo Ji
https://arxiv.org/abs/2402.06715 http…

Learning-augmented Online Algorithm for Two-level Ski-rental Problem
In this paper, we study the two-level ski-rental problem,where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user aims to minimize the total cost (i.e., the sum of the rental, single purchase, and combo purchase costs) by balancing the trade-off between t…

This https://arxiv.org/abs/2309.14737 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Volumetric Semantically Consistent 3D Panoptic Mapping
We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments. The proposed approach is based on a Voxel-TSDF representation used in recent algorithms. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. Further improvements are achieved by graph optimization-based s…

Learning-augmented Online Algorithm for Two-level Ski-rental Problem
Keyuan Zhang, Zhongdong Liu, Nakjung Choi, Bo Ji
https://arxiv.org/abs/2402.06715 http…

Learning-augmented Online Algorithm for Two-level Ski-rental Problem
In this paper, we study the two-level ski-rental problem,where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user aims to minimize the total cost (i.e., the sum of the rental, single purchase, and combo purchase costs) by balancing the trade-off between t…

This https://arxiv.org/abs/2205.02160 has been replaced.
link: https://scholar.google.com/scholar?q=a

Making SGD Parameter-Free
We develop an algorithm for parameter-free stochastic convex optimization (SCO) whose rate of convergence is only a double-logarithmic factor larger than the optimal rate for the corresponding known-parameter setting. In contrast, the best previously known rates for parameter-free SCO are based on online parameter-free regret bounds, which contain unavoidable excess logarithmic terms compared to their known-parameter counterparts. Our algorithm is conceptually simple, has high-probability guara…

Safe Returning FaSTrack with Robust Control Lyapunov-Value Functions
Zheng Gong, Boyang Li, Sylvia Herbert
https://arxiv.org/abs/2404.02472 https://…

Safe Returning FaSTrack with Robust Control Lyapunov-Value Functions
Real-time navigation in a priori unknown environment remains a challenging task, especially when an unexpected (unmodeled) disturbance occurs. In this paper, we propose the framework Safe Returning Fast and Safe Tracking (SR-F) that merges concepts from 1) Robust Control Lyapunov-Value Functions (R-CLVF), and 2) the Fast and Safe Tracking (FaSTrack) framework. The SR-F computes an R-CLVF offline between a model of the true system and a simplified planning model. Online, a planning algorithm is …

On Approximate Fully-Dynamic Matching and Online Matrix-Vector Multiplication
Yang P. Liu
https://arxiv.org/abs/2403.02582 https://ar…

On Approximate Fully-Dynamic Matching and Online Matrix-Vector Multiplication
We study connections between the problem of fully dynamic $(1-ε)$-approximate maximum bipartite matching, and the dual $(1+ε)$-approximate vertex cover problem, with the online matrix-vector ($\mathsf{OMv}$) conjecture which has recently been used in several fine-grained hardness reductions. We prove that there is an online algorithm that maintains a $(1+ε)$-approximate vertex cover in amortized $n^{1-c}ε^{-C}$ time for constants $c, C > 0$ for fully dynamic updates if and only if the $\mat…

Optimizing Inventory Placement for a Downstream Online Matching Problem
Boris EpsteinColumbia University, Will MaColumbia University
https://arxiv.org/abs/2403.04598

Optimizing Inventory Placement for a Downstream Online Matching Problem
We study the inventory placement problem of splitting $Q$ units of a single item across warehouses, in advance of a downstream online matching problem that represents the dynamic fulfillment decisions of an e-commerce retailer. This is a challenging problem both in theory, because the downstream matching problem itself is computationally hard, and in practice, because the fulfillment team is constantly updating its algorithm and the placement team cannot directly evaluate how a placement decisi…

This https://arxiv.org/abs/2404.05271 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Scheduling Multi-Server Jobs is Not Easy
The problem of online scheduling of multi-server jobs is considered, where there are a total of $K$ servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its processing time, the number of servers from which it needs concurrent service and an online algorithm has to make scheduling decisions using only causal information, with the goal of minimizing the response/flow time. The worst case input model is considered and the …

Scheduling Multi-Server Jobs is Not Easy
Rahul Vaze
https://arxiv.org/abs/2404.05271 https://arxiv.org/pdf/2404.05271

Scheduling Multi-Server Jobs is Not Easy
The problem of online scheduling of multi-server jobs is considered, where there are a total of $K$ servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its processing time, the number of servers from which it needs concurrent service and an online algorithm has to make scheduling decisions using only causal information, with the goal of minimizing the response/flow time. The worst case input model is considered and the …

Scheduling Multi-Server Jobs is Not Easy
Rahul Vaze
https://arxiv.org/abs/2404.05271 https://arxiv.org/pdf/2404.05271

Scheduling Multi-Server Jobs is Not Easy
The problem of online scheduling of multi-server jobs is considered, where there are a total of $K$ servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its processing time, the number of servers from which it needs concurrent service and an online algorithm has to make scheduling decisions using only causal information, with the goal of minimizing the response/flow time. The worst case input model is considered and the …

This https://arxiv.org/abs/2311.09408 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Decentralized Feedback Optimization via Sensitivity Decoupling: Stability and Sub-optimality
Online feedback optimization is a controller design paradigm for optimizing the steady-state behavior of a dynamical system. It employs an optimization algorithm as a dynamic feedback controller and utilizes real-time measurements to bypass knowing exact plant dynamics and disturbances. Different from existing centralized settings, we present a fully decentralized feedback optimization controller for networked systems to lift the communication burden and improve scalability. We approximate the …

This https://arxiv.org/abs/2404.01613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Identification of High-dimensional ARMA Models with Binary-Valued Observations
This paper studies system identification of high-dimensional ARMA models with binary-valued observations. Compared with existing quantized identification of ARMA models, this problem is more challenging since the accessible information is much less. Different from the identification of FIR models with binary-valued observations, the prediction of original system output and the parameter both need to be estimated in ARMA models. We propose an online identification algorithm consisting of paramet…

This https://arxiv.org/abs/2305.08353 has been replaced.
link: https://scholar.google.com/scholar?q=a

Fast and Efficient Matching Algorithm with Deadline Instances
The online weighted matching problem is a fundamental problem in machine learning due to its numerous applications. Despite many efforts in this area, existing algorithms are either too slow or don't take $\mathrm{deadline}$ (the longest time a node can be matched) into account. In this paper, we introduce a market model with $\mathrm{deadline}$ first. Next, we present our two optimized algorithms (\textsc{FastGreedy} and \textsc{FastPostponedGreedy}) and offer theoretical proof of the time com…