Search Results for author:
Found 18 papers, 3 papers with code
Policy Optimization via Stochastic Recursive Gradient Algorithm
In this paper, we propose the StochAstic Recursive grAdient Policy Optimization (SARAPO) algorithm which is a novel variance reduction method on Trust Region Policy Optimization (TRPO).
Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms
An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels.
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples.
Contextual Dictionary Lookup for Knowledge Graph Completion
We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.
Revisit PCA-based Technique for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is a desired ability to ensure the reliability and safety of intelligent systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Clustering and Analysis of GPS Trajectory Data using Distance-based Features
We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features.
Distributed Evolution Strategies for Black-box Stochastic Optimization
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.
MMES: Mixture Model based Evolution Strategy for Large-Scale Optimization
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings.
WiFi Fingerprint Clustering for Urban Mobility Analysis
We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users.
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It
Limerick generation exemplifies some of the most difficult challenges faced in poetry generation, as the poems must tell a story in only five lines, with constraints on rhyme, stress, and meter.
Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data
Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications.
Stochastic Recursive Momentum for Policy Gradient Methods
In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced policy gradient in an exponential moving average fashion.
Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data Mining
In this paper, therefore, we propose a comprehensive data analysis framework to fully analyze the collected probe requests to extract three types of patterns related to crowd behaviors in a large social event, with the help of statistics, visualization, and unsupervised machine learning.
Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques
In this paper, we propose a data-driven approach to fairly benchmark the AC energy performance of residential rooms.
A Theoretical Framework of Approximation Error Analysis of Evolutionary Algorithms
In the empirical study of evolutionary algorithms, the solution quality is evaluated by either the fitness value or approximation error.
Analysis of Solution Quality of a Multiobjective Optimization-based Evolutionary Algorithm for Knapsack Problem
Multi-objective optimisation is regarded as one of the most promising ways for dealing with constrained optimisation problems in evolutionary optimisation.
Performance Analysis on Evolutionary Algorithms for the Minimum Label Spanning Tree Problem
We also show that GSEMO achieves a $(2ln(n))$-approximation ratio for the MLST problem in expected polynomial time of $n$ and $k$.
A Theoretical Assessment of Solution Quality in Evolutionary Algorithms for the Knapsack Problem
Nonetheless, few rigorous investigations address the quality of solutions that evolutionary algorithms may produce for the knapsack problem.
