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The Delusional hedge algorithm as a Model of Human Learning From Diverse Opinions

TOPICS IN COGNITIVE SCIENCE 2025年第1期17卷 73-87页

作者： Chuang, Yun-Shiuan Zhu, Xiaojin Rogers, Timothy T. Univ Wisconsin Madison Dept Psychol 1202 West Johnson St Madison WI 53706 USA Univ Wisconsin Madison Dept Comp Sci Madison WI USA

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground-truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm-suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

关键词： Semi-supervised learning hedge algorithm Online learning with expert advice

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hedge algorithm and Dual Averaging schemes

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2013年第3期77卷 279-289页

作者： Baes, Michel Buergisser, Michael ETH Inst Operat Res CH-8092 Zurich Switzerland

We show that the hedge algorithm, a method that is widely used in Machine Learning, can be interpreted as a particular instance of Dual Averaging schemes, which have recently been introduced by Nesterov for regret minimization. Based on this interpretation, we establish three alternative methods of the hedge algorithm: one in the form of the original method, but with optimal parameters, one that requires less a priori information, and one that is better adapted to the context of the hedge algorithm. All our modified methods have convergence results that are better or at least as good as the performance guarantees of the vanilla method. In numerical experiments, our methods significantly outperform the original scheme.

关键词： First-order methods hedge algorithm Dual Averaging methods Convex optimization

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Online variance minimization

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MACHINE LEARNING 2012年第1期87卷 1-32页

作者： Warmuth, Manfred K. Kuzmin, Dima UC Calif Santa Cruz CA USA Google Mountain View CA USA

We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C-t and try to select a parameter vector w(t-1) such that the total variance over a sequence of trials Sigma(T)(t=1)(w(t-1))(Tau)C(t)w(t-1) is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in R-n are considered-the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix Sigma C-T(t=1)t. For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu(Tau) and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.

关键词： hedge algorithm Weighted majority algorithm Online learning Expert setting Density matrix Matrix exponentiated gradient algorithm Quantum relative entropy

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Video Processing for Human Perceptual Visual Quality-Oriented Video Coding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第4期22卷 1524-1533页

作者： Oh, Hyungsuk Kim, Wonha Kyung Hee Univ Coll Elect & Informat Kyonggi Do 499701 South Korea

We have developed a video processing method that achieves human perceptual visual quality-oriented video coding. The patterns of moving objects are modeled by considering the limited human capacity for spatial-temporal resolution and the visual sensory memory together, and an online moving pattern classifier is devised by using the hedge algorithm. The moving pattern classifier is embedded in the existing visual saliency with the purpose of providing a human perceptual video quality saliency model. In order to apply the developed saliency model to video coding, the conventional foveation filtering method is extended. The proposed foveation filter can smooth and enhance the video signals locally, in conformance with the developed saliency model, without causing any artifacts. The performance evaluation results confirm that the proposed video processing method shows reliable improvements in the perceptual quality for various sequences and at various bandwidths, compared to existing saliency-based video coding methods.

关键词： hedge algorithm human visual perception video coding visual saliency

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Hedging static saliency models to predict dynamic saliency

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 81卷 1页

作者： Kavak, Yasin Erdem, Erkut Erdem, Aykut Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey

In recent years, many computational models for saliency prediction have been introduced. For dynamic scenes, the existing models typically combine different feature maps extracted from spatial and temporal domains either by following generic integration strategies such as averaging or winners take all or using machine learning techniques to set each feature's importance. Rather than resorting to these fixed feature integration schemes, in this paper, we propose a novel weakly supervised dynamic saliency model called hedgeSal, which is based on a decision-theoretic online learning scheme. Our framework uses two pretrained deep static saliency models as experts to extract individual saliency maps from appearance and motion streams, and then generates the final saliency map by weighted decisions of all these models. As visual characteristics of dynamic scenes constantly vary, the models providing consistently good predictions in the past are automatically assigned higher weights, allowing each expert to adjust itself to the current conditions. We demonstrate the effectiveness of our model on the CRCNS, UCFSports and CITIUS datasets.

关键词： Dynamic saliency hedge algorithm Decision theoretic online learning Feature integration

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Learning Permutations with Exponential Weights

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JOURNAL OF MACHINE LEARNING RESEARCH 2009年第7期10卷 1705-1736页

作者： Helmbold, David P. Warmuth, Manfred K. Univ Calif Santa Cruz Dept Comp Sci Santa Cruz CA 95064 USA

We give an algorithm for the on-line learning of permutations. The algorithm maintains its uncertainty about the target permutation as a doubly stochastic weight matrix, and makes predictions using an efficient method for decomposing the weight matrix into a convex combination of permutations. The weight matrix is updated by multiplying the current matrix entries by exponential factors, and an iterative procedure is needed to restore double stochasticity. Even though the result of this procedure does not have a closed form, a new analysis approach allows us to prove an optimal (up to small constant factors) bound on the regret of our algorithm. This regret bound is significantly better than that of either Kalai and Vempala's more efficient Follow the Perturbed Leader algorithm or the computationally expensive method of explicitly representing each permutation as an expert.

关键词： permutation ranking on-line learning hedge algorithm doubly stochastic matrix relative entropy projection Sinkhorn balancing

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Deep hedged LSTM Network for Short Term Offshore Wind Power Forecasting

Deep Hedged LSTM Network for Short Term Offshore Wind Power ...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Pan, Chunyang Wen, Shuli Ye, Huili Zhu, Miao Jiang, Sheng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781665464413

Precise wind power forecasting is a significant technology to support grid operations, due to the severe volatility of offshore wind power generation. In recent studies, deep neural networks show outstanding potential for wind power forecasting. However, overfitting issue hinders deep neural networks from more accurate prediction performance. In this paper, a deep hedged long short-term memory (LSTM) network (DHnet) is proposed to predict short-term offshore wind power. The proposed DHnet builds layer-connections for deep LSTM networks and considers the weighted summation result of each layer as forecasting outcome. Comprehensive comparisons with shallow-to-deep LSTM networks demonstrate that DHnet is robust to overfitting problem. Furthermore, several forecasting models such as persistence model, extreme learning machine, deep feedforward neural network, and gated recurrent unit network are compared. The numerical results indicate that proposed DHnet yields the best predictive performance.

关键词： Short-term wind power forecasting deep learning hedge algorithm long short-term memory

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Online allocation with risk information

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2006年第8期E89D卷 2340-2347页

作者： Harada, Shigeaki Takimoto, Eiji Maruoka, Akira Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan

We consider the problem of dynamically apportioning resources among a set of options in a worst-case online framework. The model we investigate is a generalization of the well studied online learning model. In particular, we allow the learner to see as additional information how high the risk of each option is. This assumption is natural in many applications like horse-race betting, where gamblers know odds for all options before placing bets. We apply Vovk's Aggregating algorithm to this problem and give a tight performance bound. The results support our intuition that it is safe to bet more on low-risk options. Surprisingly, the loss bound of the algorithm does not depend on the values of relatively small risks.

关键词： online learning resource allocation hedge algorithm aggregating algorithm

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Sequential Ski Rental Problem 21

Sequential Ski Rental Problem

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International Conference on Autonomous Agents and Multiagent Systems

作者： Anant Shah Arun Rajkumar Department of Electrical Engineering Indian Institute of Technology Madras Department of Computer Science and Engineering Indian Institute of Technology Madras

ISBN: (纸本)9781713832621

The classical 'buy or rent' ski-rental problem was recently considered in the setting where multiple experts (such as Machine learning algorithms) advice on the length of the ski season. Here, robust algorithms were developed with improved theoretical performance over adversarial scenarios where such expert predictions were unavailable. We consider a variant of this problem which we call the 'sequential ski-rental' problem. Here, a sequence of ski-rental problems has to be solved in an online fashion where both the buy cost and the length of ski season are unknown to the learner. The learner has access to two sets of experts, one set who advise on the true cost of buying the ski and another set who advise on the length of the ski season. Under certain stochastic assumptions on the experts who predict the buy costs, we develop online algorithms and prove regret bounds for the same. Our experimental evaluations confirm our theoretical results.

关键词： Online learning hedge algorithm Ski-Rental problem e-learning Professional personnel Property Leasing solidification Regret Sequential Purchasing Not Available Costs

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