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检索条件"主题词=Consensus algorithm"

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Prospective Side Information for Latent MDPs 41

Prospective Side Information for Latent MDPs

引用

41st International Conference on Machine Learning, ICML 2024

作者： Kwon, Jeongyeol Efroni, Yonathan Mannor, Shie Caramanis, Constantine Wisconsin Institute for Discovery Wisconsin United States Meta AI New York United States Technion Haifa Israel NVIDIA United States University of Texas AustinTX United States

In many interactive decision-making problems, there is contextual side information that remains fixed within the course of an interaction. This problem has been studied quite extensively under the assumption the context is fully observed, as well as in the opposing limit when the context is unobserved, a special type of POMDP also referred to as a Latent MDP (LMDP). In this work, we consider a class of decision problems that interpolates between the settings, namely, between the case the context is fully observed, and the case the context is unobserved. We refer to this class of decision problems as LMDPs with prospective side information. In such an environment an agent receives additional, weakly revealing, information on the latent context at the beginning of each episode. We show that, surprisingly, this problem is not captured by contemporary POMDP settings and is not solved by RL algorithms designed for partially observed environments. We then establish that any sample efficient algorithm must suffer at least Ω(K2/3)-regret, as opposed to standard Ω(√K) lower bounds. We design an algorithm with a matching upper bound that depends only polynomially on the problem parameters. This establishes exponential improvement in the sample complexity relatively to the existing LMDP lower bound, when prospective information is not given (Kwon et al., 2021). Copyright 2024 by the author(s)

关键词： consensus algorithm

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Optimizing Comparisons in the Construction of Insertion Tableau for Permutations 8

Optimizing Comparisons in the Construction of Insertion Tabl...

引用

8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Godse, Shweta Jayapaul, Varunkumar School of Computing and Electrical Engineering Iit Mandi Himachal Pradesh Kamand India

ISBN: (纸本)9798331531539

Young Tableaux are combinatorial object which have found utility in areas such as combinatorics, cryptography, representation theory etc. Every application has an inherent need for constructing a Young Tableaux efficiently. Young Tableaux consists of two parts namely the Insertion Tableau(P) and the Recording Tableau(Q). The procedure of transforming the input permutation (σ) into tableau is known as the RSK algorithm. Each element from permutation is inserted into P, consequently displacing/bumping other elements from the tableau, whereas Q records the order in which these elements were inserted into P. As part of our work, we have devised a modified method for the construction of insertion tableau from the permutation (σ), The number of comparisons mainly take place in searching for a position where the next element can be inserted in P. In order to solve this problem, we consider P along with the pointer matrix which stores the pointers to the elements in P and tracks previous points of insertion to improve subsequent insertions. We use a combination of linear search and binary search on P to find the correct location for inserting an element (α) from the permutation (σ). Our randomized trials show that the devised algorithm beats the standard RSK algorithm which uses binary search and the Fast RSK algorithm (current theoretical best) devised by Tiskin [15] by approximately 30% on average. © 2024 IEEE.

关键词： consensus algorithm

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An Innovative Analysis to Improve the Performance by Blocking the Pop-ups of Web Applications with XGBoost algorithm over K-Means algorithm

An Innovative Analysis to Improve the Performance by Blockin...

引用

2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Saranya, J. Sivanantham, S. Chennai India

ISBN: (纸本)9798350309140

The focus of the research is to utilise the XGBoost algorithm in comparison to the K-Means algorithm is to prevent pop-up windows from showing up on a web application. The K-Means algorithm and the XGBoost algorithm are two examples of two groups that are used. The sample size for each group is 10, and the significant value of the data set is predicted using SPSS values with an 80% G-power. Their accuracy levels are determined and contrasted using various sample data sizes. Different sample data sizes are used to achieve and compare each accuracy. The difference between these groups has a significance value of 0.001, which is less than 0.05 (p © 2024 IEEE.

关键词： consensus algorithm

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A Path Planning Method Based on Improved Beetle Antennae Search algorithm for Intelligent Agent 5

A Path Planning Method Based on Improved Beetle Antennae Sea...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Ma, Junjie Zhang, Wenbo Guo, Dongsheng Xue, Shan Jia, Zehua Li, Shuai Hainan University School of Information and Communication Engineering Haikou570228 China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu90570 Finland

ISBN: (纸本)9798350366174

The traditional Beetle Antennae Search (BAS) algorithm exhibits strong randomness in intelligent agent path planning, resulting in issues such as low search efficiency and poor path practicability. To address these issues, a path planning method based on Improved Beetle Antennae Search (IBAS) algorithm for intelligent agent is proposed. The method constrains the search direction of the beetle, reducing the randomness of the search. The obstacle expansion strategy is employed, which not only simplifies the difficulty of obstacle processing but also improves path security. In addition, the generated path is optimized by eliminating redundant nodes and oscillating nodes, significantly enhancing the practicability of the path. The results of the algorithm simulation experiments fully demonstrate that the IBAS algorithm exhibits superior optimization speed, shorter path length, and better path practicability while effectively achieving obstacle avoidance. © 2024 IEEE.

关键词： consensus algorithm

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A Subquadratic Time algorithm for Robust Sparse Mean Estimation 41

A Subquadratic Time Algorithm for Robust Sparse Mean Estimat...

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41st International Conference on Machine Learning, ICML 2024

作者： Pensia, Ankit IBM Research United States

We study the algorithmic problem of sparse mean estimation in the presence of adversarial outliers. Specifically, the algorithm observes a corrupted set of samples from N(µ, Id), where the unknown mean µ ∈ Rd is constrained to be k-sparse. A series of prior works has developed efficient algorithms for robust sparse mean estimation with sample complexity poly(k, log d, 1/ϵ) and runtime d2poly(k, log d, 1/ϵ), where ϵ is the fraction of contamination. In particular, the fastest runtime of existing algorithms is quadratic in the dimension, which can be prohibitive in high dimensions. This quadratic barrier in the runtime stems from the reliance of these algorithms on the sample covariance matrix, which is of size d2. Our main contribution is an algorithm for robust sparse mean estimation which runs in subquadratic time using poly(k, log d, 1/ϵ) samples, with similar results for robust sparse PCA. Our results build on algorithmic advances in detecting weak correlations, a generalized version of the light-bulb problem by Valiant (Valiant, 2015). Copyright 2024 by the author(s)

关键词： consensus algorithm

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How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization 41

How to Make the Gradients Small Privately: Improved Rates fo...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Lowy, Andrew Ullman, Jonathan Wright, Stephen J. University of Wisconsin-Madison Wisconsin Institute of Discovery MadisonWI United States Northeastern University Khoury College of Computer Sciences BostonMA United States

We provide a simple and flexible framework for designing differentially private algorithms to find approximate stationary points of non-convex loss functions. Our framework is based on using a private approximate risk minimizer to "warm start" another private algorithm for finding stationary points. We use this framework to obtain improved, and sometimes optimal, rates for several classes of non-convex loss functions. First, we obtain improved rates for finding stationary points of smooth non-convex empirical loss functions. Second, we specialize to quasar-convex functions, which generalize star-convex functions and arise in learning dynamical systems and training some neural nets. We achieve the optimal rate for this class. Third, we give an optimal algorithm for finding stationary points of functions satisfying the Kurdyka-Lojasiewicz (KL) condition. For example, over-parameterized neural networks often satisfy this condition. Fourth, we provide new state-of-the-art rates for stationary points of nonconvex population loss functions. Fifth, we obtain improved rates for non-convex generalized linear models. A modification of our algorithm achieves nearly the same rates for second-order stationary points of functions with Lipschitz Hessian, improving over the previous state-of-the-art for each of the above problems. Copyright 2024 by the author(s)

关键词： consensus algorithm

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Online Bottleneck Matching on a Star 18th

Online Bottleneck Matching on a Star

引用

18th International Conference on algorithmic Aspects in Information and Management, AAIM 2024

作者： Xiao, Man Li, Weidong School of Mathematics and Statistics Yunnan University Kunming650504 China

ISBN: (纸本)9789819778003

In this study, we focus on the problem of online bottleneck matching on a star graph. Given m servers fixed on m leaf nodes on the star graph, m requests arrive one by one in an online fashion. Upon the arrival of each request, the algorithm must immediately match it to an unmatched server with the objective of minimizing the maximum matching distance. For the star, the ratio of the length of the longest edge to the length of the shortest edge is α. When m≥3, we establish a competitive ratio lower bound of 2α2+2+2αα+1 and prove that the competitive ratio of the permutation algorithm is 4αα+1, this ratio is tight. Additionally, we design an optimal online algorithm with a competitive ratio of 3α+1α+1 for the case in which the star graph has only two types of edges. When m=2, we prove that the greedy algorithm achieves an optimal competitive ratio of 2. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： consensus algorithm

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A Corridor Model Evolutionary algorithm for Fast Converging Green Vehicle Routing Problem

A Corridor Model Evolutionary Algorithm for Fast Converging ...

引用

2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Shahane, Ananta Van Stein, Niki Fan, Yingjie Leiden University Leiden Netherlands LIACS Leiden University Leiden Netherlands

ISBN: (纸本)9798400704956

In this paper, we introduce an innovative evolutionary algorithm for the Green Vehicle Routing Problem (GVRP). We use the relative location of the customers with respect to the depot to estimate whether a mutation is exploitation or exploration. By introducing a probabilistic corridor model, our mutation strategy converges faster to optimal solutions compared to random mutation strategies in existing research. We then introduce our evolutionary algorithms that use this mutation strategy to reduce the number of hyper-parameters to two or three. We test our algorithms across 92 benchmarks. Results show that our algorithms are robust and efficient. © 2024 Copyright held by the owner/author(s).

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A Faster algorithm for Finding Negative Cycles in Simple Temporal Networks with Uncertainty 31

A Faster Algorithm for Finding Negative Cycles in Simple Tem...

引用

31st International Symposium on Temporal Representation and Reasoning, TIME 2024

作者： Hunsberger, Luke Posenato, Roberto Vassar College PoughkeepsieNY United States University of Verona Italy

ISBN: (纸本)9783959773492

Temporal constraint networks are data structures for representing and reasoning about time (e.g., temporal constraints among actions in a plan). Finding and computing negative cycles in temporal networks is important for planning and scheduling applications since it is the first step toward resolving inconsistent networks. For Simple Temporal Networks (STNs), the problem reduces to finding simple negative cycles (i.e., no repeat nodes), resulting in numerous efficient algorithms. For Simple Temporal Networks with Uncertainty (STNUs), which accommodate actions with uncertain durations, the situation is more complex because the characteristic of a non-dynamically controllable (non-DC) network is a so-called semi-reducible negative (SRN) cycle, which can have repeat edges and, in the worst case, an exponential number of occurrences of such edges. algorithms for computing SRN cycles in non-DC STNUs that have been presented so far are based on older, less efficient DC-checking algorithms. In addition, the issue of repeated edges has either been ignored or given scant attention. This paper presents a new, faster algorithm for identifying SRN cycles in non-DC STNUs. Its worst-case time complexity is O(mn + k2n + kn log n), where n is the number of timepoints, m is the number of constraints, and k is the number of actions with uncertain durations. This complexity is the same as that of the fastest DC-checking algorithm for STNUs. It avoids an exponential blow-up by efficiently dealing with repeated structures and outputting a compact representation of the SRN cycle it finds. The space required to compactly store accumulated path information while avoiding redundant storage of repeated edges is O(mk + k2n). An empirical evaluation demonstrates the effectiveness of the new algorithm on an existing benchmark. © Luke Hunsberger and Roberto Posenato.

关键词： consensus algorithm

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Convergence of Adaptive Identification for Phase Retrieval Problem 63

Convergence of Adaptive Identification for Phase Retrieval P...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Yujing Liu, Zhixin Guo, Lei University of Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences Beijing China

ISBN: (纸本)9798350316339

The phase retrieval problem is of fundamental importance in various fields including computer science, physics, and engineering, where only the magnitude measurements are variable. For this NP-hard problem, previous work has mainly focused on the noiseless case, off-line algorithms, i.i.d. standard Gaussian assumptions on the regressor and convergence results in a high probability sense. To overcome these limitations, we consider the phase retrieval problem in the presence of noise disturbances. Based on the maximum likelihood (ML) criterion, we propose a novel adaptive identification algorithm to estimate the true signal vector. For the first time, we establish the global convergence and convergence rate results for the proposed algorithm without relying on the i.i.d. assumption on the regressor. Finally, we provide a numerical example to illustrate the effectiveness of our adaptive algorithm. © 2024 IEEE.

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