The data augmentation algorithm

• URL: http://arxiv.org/abs/2406.10464v1
• Date: Sat, 15 Jun 2024 01:51:08 GMT
• Title: The data augmentation algorithm
• Authors: Vivekananda Roy, Kshitij Khare, James P. Hobert,
• Abstract summary: The data augmentation (DA) algorithms are popular Markov chain Monte Carlo (MCMC) algorithms used for sampling from intractable probability distributions. This review article comprehensively surveys DA MCMC algorithms, highlighting their theoretical foundations, methodological implementations, and diverse applications.
• Score: 1.588066191024883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The data augmentation (DA) algorithms are popular Markov chain Monte Carlo (MCMC) algorithms often used for sampling from intractable probability distributions. This review article comprehensively surveys DA MCMC algorithms, highlighting their theoretical foundations, methodological implementations, and diverse applications in frequentist and Bayesian statistics. The article discusses tools for studying the convergence properties of DA algorithms. Furthermore, it contains various strategies for accelerating the speed of convergence of the DA algorithms, different extensions of DA algorithms and outlines promising directions for future research. This paper aims to serve as a resource for researchers and practitioners seeking to leverage data augmentation techniques in MCMC algorithms by providing key insights and synthesizing recent developments.

Related papers

• MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming [3.2513035377783717]
  This paper presents a novel Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. Our numerical results show that not only a remarkable reduction in computational time is achieved by our algorithm but also an improvement in causal discovery accuracy is seen across diverse datasets.
  arXiv  Detail & Related papers  (2024-10-22T22:56:33Z)
• Evaluating Ensemble Methods for News Recommender Systems [50.90330146667386]
  This paper demonstrates how ensemble methods can be used to combine many diverse state-of-the-art algorithms to achieve superior results on the Microsoft News dataset (MIND) Our findings demonstrate that a combination of NRS algorithms can outperform individual algorithms, provided that the base learners are sufficiently diverse.
  arXiv  Detail & Related papers  (2024-06-23T13:40:50Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning [53.445068584013896]
  We study matrix estimation problems arising in reinforcement learning (RL) with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm rewards, and for low-rank Markov Decision Processes (MDPs), it may for example characterize the transition kernel of the MDP. We show that simple spectral-based matrix estimation approaches efficiently recover the singular subspaces of the matrix and exhibit nearly-minimal entry-wise error.
  arXiv  Detail & Related papers  (2023-10-10T17:06:41Z)
• Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation [7.6249291891777915]
  We use information-theoretic tools to derive a novel analysis of Multi-source Domain Adaptation (MDA) from the representation learning perspective. We propose a novel deep MDA algorithm, implicitly addressing the target shift through joint alignment. The proposed algorithm has comparable performance to the state-of-the-art on target-shifted MDA benchmark with improved memory efficiency.
  arXiv  Detail & Related papers  (2023-04-04T18:32:20Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• Identifying Co-Adaptation of Algorithmic and Implementational Innovations in Deep Reinforcement Learning: A Taxonomy and Case Study of Inference-based Algorithms [15.338931971492288]
  We focus on a series of inference-based actor-critic algorithms to decouple their algorithmic innovations and implementation decisions. We identify substantial performance drops whenever implementation details are mismatched for algorithmic choices. Results show which implementation details are co-adapted and co-evolved with algorithms.
  arXiv  Detail & Related papers  (2021-03-31T17:55:20Z)
• DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning [0.0]
  We propose DAC, Deep Autoencoder-based Clustering, a data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the KMeans clustering algorithm on a variety of datasets.
  arXiv  Detail & Related papers  (2021-02-15T11:31:00Z)
• A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle [4.246818236277977]
  A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. We propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method.
  arXiv  Detail & Related papers  (2021-02-07T13:16:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.