We present a principled framework to address resource allocation for realizing boosting algorithms on substrates with communication or computation noise. Boosting classifiers (e.g., AdaBoost) make a final decision via a weighted vote from the outputs of many base classifiers (weak classifiers). Suppose that the base classifiers' outputs are noisy or communicated over noisy channels; these noisy outputs will degrade the final classification accuracy. We show that this degradation can be effectively reduced by allocating more system resources for more important base classifiers. We formulate resource optimization problems in terms of importance metrics for boosting. Moreover, we show that the optimized noisy boosting classifiers can be more robust than bagging for the noise during inference (test stage). We provide numerical evidence to demonstrate the benefits of our approach.
University of Pittsburgh（匹兹堡大学）；Nordita, KTH（北欧理论物理研究所，瑞典皇家理工学院）；University of Toronto（多伦多大学）；ICSI（国际计算机科学研究所）；UC Berkeley（加州大学伯克利分校）
For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus robustness of neural networks is an increasingly important topic. Relatedly, data augmentation schemes have been shown to improve robustness with respect to input perturbations and domain shifts. Motivated by this, we introduce NoisyMix, a novel training scheme that promotes stability as well as leverages noisy augmentations in input and feature space to improve both model robustness and in-domain accuracy. NoisyMix pr…
In recent years, neural networks (NNs) have made giant leaps in a wide variety of domains. NNs are often referred to as black box algorithms due to how little we can explain their empirical success. Our foundational research seeks to explain why neural networks generalize. A recent advancement derived a mutual information measure for explaining the performance of deep NNs through a sequence of increasingly complex functions. We show deep NNs learn a series of boosted classifiers whose generalization is popularly attributed to self-averaging over an increasing number of interpolating sub-classi…
Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample. In the real world, it is more common to deal with noisy datasets than clean datasets, given how modern datasets are labeled by a large group of annotators on crowdsourcing platforms, but little attention has been given to evaluating multi-label classifiers with noisy labels. Exploiting label correlations now becomes a standard component of a multi-label classifier to achieve competitive performance. However, this component makes the classifier more prone to p…
Boosting is one of the most successful ideas in machine learning, achieving great practical performance with little fine-tuning. The success of boosted classifiers is most often attributed to improvements in margins. The focus on margin explanations was pioneered in the seminal work by Schapire et al. (1998) and has culminated in the $k$'th margin generalization bound by Gao and Zhou (2013), which was recently proved to be near-tight for some data distributions (Gronlund et al. 2019). In this work, we first demonstrate that the $k$'th margin bound is inadequate in explaining the performance of…
The University of Queensland（昆士兰大学）；The University of Adelaide（阿德莱德大学）；RIKEN（理化学研究所）
Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural networks, but little has been done to develop boosting algorithms for PU learning, despite boosting algorithms' strong performance on many fully supervised classification problems. In this paper, we propose a novel boosting algorithm, AdaPU, for PU learning. Similarly to AdaBoost, AdaPU aims to optimize an empirical exponential loss, but the loss is based on the PU data, rather than on positive-negative (PN) data. As …
Quantum Speedup in Adaptive Boosting of Binary Classification
Shenzhen Institute for Quantum Science and Engineering（深圳量子科学与工程研究院）；University of Science and Technology, Shenzhen（深圳理工大学）；University of Technology Sydney（悉尼科技大学）；Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院量子信息中心）；Central Research Institute, Huawei Technologies（华为技术有限公司中央研究院）
This paper addresses the problem of multiclass classification with corrupted or noisy bandit feedback. In this setting, the learner may not receive true feedback. Instead, it receives feedback that has been flipped with some non-zero probability. We propose a novel approach to deal with noisy bandit feedback based on the unbiased estimator technique. We further offer a method that can efficiently estimate the noise rates, thus providing an end-to-end framework. The proposed algorithm enjoys a mistake bound of the order of $O(\sqrt{T})$ in the high noise case and of the order of $O(T^{\nicefrac…
University of Chicago（芝加哥大学）；Harvard University（哈佛大学）
This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives. We consider a high-dimensional setting where the number of features (weak learners) $p$ scales with the sample size $n$, in an overparametrized regime. Under a class of statistical models, we provide an exact analysis of the generalization error of boosting when the algorithm interpolates the training data and maximizes the empirical $\ell_1$-margin. Further, we explicitly pin down the relation between the boosting test error and the optimal Bay…
An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit
Acute Care Solutions (ACS)（急性护理解决方案（ACS））；Philips Research North America（飞利浦北美研究院）；Cardon Children’s Medical Center（卡顿儿童医疗中心）；Banner Health System（班纳健康系统）
我们的工作重点是预测儿科病人从普通病房的医院儿科重症监护病房的转移的问题。使用从两个医疗设施的电子健康记录中收集的超过5.5年的数据，我们开发了基于自适应提升和梯度树提升的分类器。我们进一步将这些学习到的分类器组合成一个集成模型，并将其性能与依赖于专家定义的指南的修改后的儿科早期预警评分（PEWS）基线进行比较。为了衡量模型的泛化能力，我们进行了一个设施间的评估，我们训练我们的算法上的数据从一个设施，并执行一个隐藏的测试数据集从一个单独的设施进行评估。我们发现，PEWS基线在准确性（0.77 vs. 0.69）、灵敏度（0.80 vs. 0.68）、特异性（0.74 vs. 0.70）和AUROC（0.85 vs. 0.73）方面都有所改善。
Clova AI Research, NAVER Corp.（NAVER公司Clova人工智能研究院）；Search Solution Inc.（搜索解决方案公司）；Seoul National University（首尔大学）
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models. In this paper, we present a new training pipeline boosting the conventional active learning approach targeting label-efficient learning to resolve the mentioned problem. Existing active learning methods only focus on selecting a set of informative samples under a labeling budget. One step further, we suggest that the training efficiency can be further improved by utilizing the unlabeled samples, exceeding…
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccur…
Multi-Class Classification from Noisy-Similarity-Labeled Data
University of Sydney（悉尼大学）；Xidian University（西安电子科技大学）；RIKEN（理化学研究所）；Hong Kong Baptist University（香港浸会大学）；The University of Melbourne（墨尔本大学）；Brain-Inspired Technology Co., Ltd.（脑启发科技有限公司）
Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work, we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii) empirically confirm this choice, obtaining state-of-the-art results in multiple settings. The key insight of our work is that the reduced variance of ensembles over the perturbations introduced in RS leads to significantly more consistent classifications for a given input. This, in turn, leads to substantially increased certifiable radii for samp…
Exact ground truth invariant polynomial systems can be written for arbitrarily correlated binary classifiers. Their solutions give estimates for sample statistics that require knowledge of the ground truth of the correct labels in the sample. Of these polynomial systems, only a few have been solved in closed form. Here we discuss the exact solution for independent binary classifiers - resolving an outstanding problem that has been presented at this conference and others. Its practical applicability is hampered by its sole remaining assumption - the classifiers need to be independent in their s…
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；ByteDance Inc.（字节跳动公司）；Zhejiang University of Technology（浙江工业大学）；Hong Kong Baptist University（香港浸会大学）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；University of Seoul（首尔大学）
Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused on modifying the classifier during the training procedure. It has two potential problems. First, these methods are not applicable to a pre-trained classifier without further access to training. Second, it is not easy to train a classifier and regularize all negative effects from noisy labels, simultaneously. We suggest a new branch of method, Noisy Prediction Calibration (NPC) in l…
Mutual Information (MI) based feature selection makes use of MI to evaluate each feature and eventually shortlists a relevant feature subset, in order to address issues associated with high-dimensional datasets. Despite the effectiveness of MI in feature selection, we notice that many state-of-the-art algorithms disregard the so-called unique relevance (UR) of features, and arrive at a suboptimal selected feature subset which contains a non-negligible number of redundant features. We point out that the heart of the problem is that all these MIBFS algorithms follow the criterion of Maximize Rel…
University of Technology Sydney（悉尼科技大学）；RIKEN（理化学研究所）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Shanghai Jiao Tong University（上海交通大学）；University of Tokyo（东京大学）
Boosting Fair Classifier Generalization through Adaptive Priority Reweighing
University of Science and Technology of China（中国科学技术大学）；Warwick Business School, University of Warwick（华威大学华威商学院）；New Jersey Institute of Technology（新泽西理工学院）；University of Oxford（牛津大学）；University of Edinburgh（爱丁堡大学）
We explore contemporary robust classification algorithms for overcoming class-dependant labelling noise: Forward, Importance Re-weighting and T-revision. The classifiers are trained and evaluated on class-conditional random label noise data while the final test data is clean. We demonstrate methods for estimating the transition matrix in order to obtain better classifier performance when working with noisy data. We apply deep learning to three data-sets and derive an end-to-end analysis with unknown noise on the CIFAR data-set from scratch. The effectiveness and robustness of the classifiers a…
Stony Brook University（石溪大学）；Rutgers University（罗格斯大学）；Bain & Company（贝恩公司）；City University of New York（纽约城市大学）
To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. To be robust against label noise, many successful methods rely on the noisy classifiers (i.e., models trained on the noisy training data) to determine whether a label is trustworthy. However, it remains unknown why this heuristic works well in practice. In this paper, we provide the first theoretical explanation for these methods. We prove that the prediction of a noisy classifier can indeed be a good indicator of whether the label of a training data is clean. Based on the theoretica…
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；Georgia Institute of Technology & Emory University（佐治亚理工学院与埃默里大学）
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress Disorder, or PTSD - and significantly boost performance on a small database of rare individuals. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without significant TWA, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology…
