Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound

• URL: http://arxiv.org/abs/2106.12535v1
• Date: Wed, 23 Jun 2021 16:57:23 GMT
• Title: Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
• Authors: Valentina Zantedeschi, Paul Viallard, Emilie Morvant, R\'emi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj
• Abstract summary: We investigate a counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. We instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk. The resulting majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight bounds.
• Score: 15.557653926558638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective. The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives -- both with uninformed (data-independent) and informed (data-dependent) priors.

Related papers

• A naive aggregation algorithm for improving generalization in a class of learning problems [0.0]
  We present a naive aggregation algorithm for a typical learning problem with expert advice setting. In particular, we consider a class of learning problem of point estimations for modeling high-dimensional nonlinear functions.
  arXiv  Detail & Related papers  (2024-09-06T15:34:17Z)
• On Policy Evaluation Algorithms in Distributional Reinforcement Learning [0.0]
  We introduce a novel class of algorithms to efficiently approximate the unknown return distributions in policy evaluation problems from distributional reinforcement learning (DRL) For a plain instance of our proposed class of algorithms we prove error bounds, both within Wasserstein and Kolmogorov--Smirnov distances. For return distributions having probability density functions the algorithms yield approximations for these densities; error bounds are given within supremum norm.
  arXiv  Detail & Related papers  (2024-07-19T10:06:01Z)
• Distributionally Robust Skeleton Learning of Discrete Bayesian Networks [9.46389554092506]
  We consider the problem of learning the exact skeleton of general discrete Bayesian networks from potentially corrupted data. We propose to optimize the most adverse risk over a family of distributions within bounded Wasserstein distance or KL divergence to the empirical distribution. We present efficient algorithms and show the proposed methods are closely related to the standard regularized regression approach.
  arXiv  Detail & Related papers  (2023-11-10T15:33:19Z)
• Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning [83.41487567765871]
  Skipper is a model-based reinforcement learning framework. It automatically generalizes the task given into smaller, more manageable subtasks. It enables sparse decision-making and focused abstractions on the relevant parts of the environment.
  arXiv  Detail & Related papers  (2023-09-30T02:25:18Z)
• Correcting Underrepresentation and Intersectional Bias for Classification [49.1574468325115]
  We consider the problem of learning from data corrupted by underrepresentation bias. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out rates. We show that our algorithm permits efficient learning for model classes of finite VC dimension.
  arXiv  Detail & Related papers  (2023-06-19T18:25:44Z)
• Diverse Projection Ensembles for Distributional Reinforcement Learning [6.754994171490016]
  This work studies the combination of several different projections and representations in a distributional ensemble. We derive an algorithm that uses ensemble disagreement, measured by the average $1$-Wasserstein distance, as a bonus for deep exploration.
  arXiv  Detail & Related papers  (2023-06-12T13:59:48Z)
• Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality [94.89246810243053]
  This paper studies offline policy learning, which aims at utilizing observations collected a priori to learn an optimal individualized decision rule. Existing policy learning methods rely on a uniform overlap assumption, i.e., the propensities of exploring all actions for all individual characteristics must be lower bounded. We propose Pessimistic Policy Learning (PPL), a new algorithm that optimize lower confidence bounds (LCBs) instead of point estimates.
  arXiv  Detail & Related papers  (2022-12-19T22:43:08Z)
• An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization [53.592597682854944]
  We recast generalization over sub-groups as an online game between a player minimizing risk and an adversary presenting new test. We show that ERM is provably minimax-optimal for both tasks.
  arXiv  Detail & Related papers  (2021-02-25T19:06:48Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)
• A General Method for Robust Learning from Batches [56.59844655107251]
  We consider a general framework of robust learning from batches, and determine the limits of both classification and distribution estimation over arbitrary, including continuous, domains. We derive the first robust computationally-efficient learning algorithms for piecewise-interval classification, and for piecewise-polynomial, monotone, log-concave, and gaussian-mixture distribution estimation.
  arXiv  Detail & Related papers  (2020-02-25T18:53:25Z)

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.