Statistical Methods for Prediction of Individual Sequences

预测个体序列的统计方法

基本信息

  • 批准号:
    0707060
  • 负责人:
  • 金额:
    $ 23.72万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2007
  • 资助国家:
    美国
  • 起止时间:
    2007-07-01 至 2011-06-30
  • 项目状态:
    已结题

项目摘要

In many prediction problems that arise, for example, in computer security and computational finance, the process generating the data is best modeled as an adversary with whom the predictor competes. The broad goal of this research project is the analysis and design of statistical methods for complex prediction problems in an adversarial setting: a prediction strategy must predict any individual sequence almost as accurately as the best strategy in some comparison class. The research focuses on statistical methods, which are based on probabilistic models of the data, since (a) these methods are commonly used in practice; (b) there is evidence that these methods perform well in adversarial settings; (c) there are good prospects of exploiting the computationally efficient approaches that have been developed for probabilistic settings, which is especially important for high-dimensional problems; and (d) positive results in adversarial settings should provide better understanding of the robustness of statistical methods in probabilistic settings. The research aims are: to develop techniques for analyzing the performance of statistical methods, such as Bayesian methods, for the prediction of individual sequences; to improve the understanding of appropriate ways to measure the complexity of a probability model used by these methods; to elucidate the impact of computational simplifications (such as empirical Bayes approaches and MAP estimates) on the performance of these prediction strategies; and hence to develop design methodologies for computationally efficient statistical methods for complex adversarial prediction problems.There are many estimation and prediction problems for which it is appropriate to model the process generating the data as an adversary with whom the predictor competes. Such problems are common in information technology. For instance, in the problem of spam filtering, the aim is to label incoming email as either legitimate or spam, but at the same time, spammers try to design email messages that slip past these spam filters. Thus, the prediction problem is a two-player repeated game. Similar decision problems arise in computer network security (for instance, the problem of deciding whether network traffic is normal or the result of a denial-of-service attack) and in internet search (for instance, deciding if a highly linked web page is genuinely authoritative and should have a high page rank). In these problems, some fraction of the data seen by the prediction strategy is chosen by an adversary who aims to fool the prediction strategy. These adversarial problems are also common in financial applications. Suppose that the predictions that emerge from a financial time series analysis are used to optimize the allocation of capital across a portfolio. Then, in the short term, it is in the interests of other market players to act so as to diminish the returns of the portfolio, and thus render the predictions inaccurate. It is common for statistical methods, such as Bayesian methods, to be applied to these adversarial prediction problems, despite the fact that they are designed for probabilistic, rather than adversarial, models of the data. This research project aims to develop techniques to understand the inherent limitations on the performance of these prediction methods, and hence inform the design of more powerful prediction methods. It focuses on the predictive accuracy and computational efficiency of methods that are suitable for the complex and high-dimensional prediction problems that arise in practise.
在计算机安全和计算金融等领域出现的许多预测问题中,生成数据的过程最好建模为预测器与之竞争的对手。该研究项目的主要目标是分析和设计对抗环境中复杂预测问题的统计方法:预测策略必须几乎与某些比较类中的最佳策略一样准确地预测任何单个序列。 研究的重点是基于数据概率模型的统计方法,因为(a)这些方法在实践中通常使用;(B)有证据表明,这些方法在对抗环境中表现良好;(c)利用为概率环境开发的计算效率高的方法有很好的前景,这对高维问题特别重要;以及(d)在对抗性环境中取得的积极结果应使人们更好地了解统计方法在概率性环境中的稳健性。研究目的是:开发分析统计方法性能的技术,如贝叶斯方法,用于预测单个序列;提高对测量这些方法所用概率模型复杂性的适当方法的理解;阐明计算简化的影响(例如经验贝叶斯方法和MAP估计)对这些预测策略的性能的影响;并因此为复杂的对抗性预测问题开发计算效率高的统计方法的设计方法。有许多估计和预测问题,它适合于将生成数据的过程建模为对手,预测器竞争这类问题在信息技术中很常见。例如,在垃圾邮件过滤问题中,目标是将传入的电子邮件标记为合法或垃圾邮件,但同时,垃圾邮件发送者试图设计通过这些垃圾邮件过滤器的电子邮件消息。因此,预测问题是一个两人重复博弈。类似的决策问题出现在计算机网络安全(例如,决定网络流量是否正常或拒绝服务攻击的结果)和互联网搜索(例如,决定一个高度链接的网页是否真正权威,应该有一个高页面排名)。 在这些问题中,预测策略所看到的数据中的一部分是由旨在欺骗预测策略的对手选择的。 这些对抗性问题在金融应用中也很常见。假设从金融时间序列分析中得出的预测被用来优化投资组合中的资本配置。 然后,在短期内,其他市场参与者的利益是采取行动,以减少投资组合的回报,从而使预测不准确。 统计方法(如贝叶斯方法)通常被应用于这些对抗性预测问题,尽管它们是为数据的概率模型而不是对抗性模型设计的。 该研究项目旨在开发技术来了解这些预测方法性能的固有局限性,从而为设计更强大的预测方法提供信息。 它侧重于预测的准确性和计算效率的方法,适用于在实践中出现的复杂和高维的预测问题。

项目成果

期刊论文数量(0)
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会议论文数量(0)
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Peter Bartlett其他文献

Mathematical Foundations of Machine Learning
机器学习的数学基础
  • DOI:
    10.4171/owr/2021/15
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Peter Bartlett;Cristina Butucea;Johannes Schmidt
  • 通讯作者:
    Johannes Schmidt
Minimax Fixed-Design Linear Regression
极小极大固定设计线性回归
Sex and Capacity: Introduction to Special Edition of the Liverpool Law Review
  • DOI:
    10.1007/s10991-010-9074-9
  • 发表时间:
    2010-10-22
  • 期刊:
  • 影响因子:
    0.300
  • 作者:
    Peter Bartlett
  • 通讯作者:
    Peter Bartlett
Mental health law in the community: thinking about Africa
Articulating future directions of law reform for compulsory mental health admission and treatment in Hong Kong
  • DOI:
    10.1016/j.ijlp.2019.101513
  • 发表时间:
    2020-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Daisy Cheung;Michael Dunn;Elizabeth Fistein;Peter Bartlett;John McMillan;Carole J. Petersen
  • 通讯作者:
    Carole J. Petersen

Peter Bartlett的其他文献

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{{ truncateString('Peter Bartlett', 18)}}的其他基金

Conference: Women-in-Theory Workshop
会议:女性理论研讨会
  • 批准号:
    2227705
  • 财政年份:
    2022
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Standard Grant
Collaboration on the Theoretical Foundations of Deep Learning
深度学习理论基础的合作
  • 批准号:
    2031883
  • 财政年份:
    2020
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Continuing Grant
Foundations of Data Science Institute
数据科学研究所基础
  • 批准号:
    2023505
  • 财政年份:
    2020
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Continuing Grant
RI: AF: Small: Optimizing probabilities for learning: sampling meets optimization
RI:AF:小:优化学习概率:采样满足优化
  • 批准号:
    1909365
  • 财政年份:
    2019
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Continuing Grant
RI: AF: Small: Deep Learning Theory
RI:AF:小:深度学习理论
  • 批准号:
    1619362
  • 财政年份:
    2016
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Standard Grant
MCS: AF: Small: Algorithms for Large Scale Prediction Problems
MCS:AF:小型:大规模预测问题的算法
  • 批准号:
    1115788
  • 财政年份:
    2011
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Standard Grant
Regularization Methods for Online Learning
在线学习的正则化方法
  • 批准号:
    0830410
  • 财政年份:
    2008
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Standard Grant
MSPA-MCS: Collaborative Research: Statistical Learning Methods for Complex Decision Problems in Natural Language Processing
MSPA-MCS:协作研究:自然语言处理中复杂决策问题的统计学习方法
  • 批准号:
    0434383
  • 财政年份:
    2004
  • 资助金额:
    $ 23.72万
  • 项目类别:
    Standard Grant

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Computational Methods for Analyzing Toponome Data
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