Variance Based Sample Weighting for Supervised Deep Learning

In the context of supervised learning of a function by a Neural Network ( NN ) , we claim and empirically justify that a NN yields better results when the distribution of the data set focuses on regions where the function to learn is steeper . We first traduce this assumption in a mathematically workable way using Taylor expansion . Then , theoretical derivations allow to construct a methodology that we call Variance Based Samples Weighting ( VBSW ) . VBSW uses local variance of the labels to weight the training points . This methodology is general , scalable , cost effective , and significantly increases the performances of a large class of NNs for various classification and regression tasks on image , text and multivariate data . We highlight its benefits with experiments involving NNs from shallow linear NN to ResNet ( He et al. , 2015 ) or Bert ( Devlin et al. , 2019 ) . 1 INTRODUCTION . When a Machine Learning ( ML ) model is used to learn from data , the distribution of the training data set can have a strong impact on its performances . More specifically , in the context of Deep Learning ( DL ) , several works have hinted at the importance of the training set . In Bengio et al . ( 2009 ) ; Matiisen et al . ( 2017 ) , the authors exploit the observation that a human will benefit more from easy examples than from harder ones at the beginning of a learning task . They construct a curriculum , inducing a change in the distribution of the training data set that makes a Neural Network ( NN ) achieve better results in an ML problem . With a different approach , Active Learning ( Settles , 2012 ) modifies dynamically the distribution of the training data , by selecting the data points that will make the training more efficient . Finally , in Reinforcement Learning , the distribution of experiments is crucial for the agent to learn efficiently . Nonetheless , the challenge of finding a good distribution is not specific to ML . Indeed , in the context of Monte Carlo estimation of a quantity of interest based on a random variable X ∼ dPX , Importance Sampling owes its efficiency to the construction of a second random variable , X̄ ∼ dPX̄ that will be used instead of X to improve the estimation of this quantity . Jie & Abbeel ( 2010 ) even make a connection between the success of likelihood ratio policy gradients and importance sampling , which shows that ML and Monte Carlo estimation , both distribution based methods , are closely linked . In this paper , we leverage the importance of the training set distribution to improve performances of NNs in supervised DL . This task can be formalized as approximating a function f with a model fθ parametrized by θ . We build a new distribution from the training points and their labels , based on the observation that fθ needs more data points to approximate f on the regions where it is steep . We use Taylor expansion of a function f , which links the local behaviour of f to its derivatives , to build this distribution . We show that up to a certain order and locally , variance is an estimator of Taylor expansion . It allows constructing a methodology called Variance Based Sample Weighting ( VBSW ) that weights each training data points using the local variance of their neighbor labels to simulate the new distribution . Sample weighting has already been explored in many works and for various goals . Kumar et al . ( 2010 ) ; Jiang et al . ( 2015 ) use it to prioritize easier samples for the training , Shrivastava et al . ( 2016 ) for hard example mining , Cui et al . ( 2019 ) to avoid class imbalance , or ( Liu & Tao , 2016 ) to solve noisy label problem . In this work , the weights ’ construction relies on a more general claim that can be applied to any data set and whose goal is to improve the performances of the model . VBSW is general , because it can be applied to any supervised ML problem based on a loss function . In this work we specifically investigate VBSW application to DL . In that case , VBSW is applied within the feature space of a pre-trained NN . We validate VBSW for DL by obtaining performance improvement on various tasks like classification and regression of text , from Glue benchmark ( Wang et al. , 2019 ) , image , from MNIST ( LeCun & Cortes , 2010 ) and Cifar10 ( Krizhevsky et al . ) and multivariate data , from UCI ML repository ( Dua & Graff , 2017 ) , for several models ranging from linear regression to Bert ( Devlin et al. , 2019 ) or ResNet20 ( He et al. , 2015 ) . As a highlight , we obtain up to 1.65 % classification improvement on Cifar10 with a ResNet . Finally , we conduct analyses on the complementarity of VBSW with other weighting techniques and its robustness . Contributions : ( i ) We present and investigate a new approach of the learning problem , based on the variations of the function f to learn . ( ii ) We construct a new simple , scalable , versatile and cost effective methodology , VBSW , that exploits these findings in order to boost the performances of a NN . ( iii ) We validate VBSW on various ML tasks . 2 RELATED WORKS . Active Learning - Our methodology is based on the consideration that not every sample bring the same amount of information . Active learning ( AL ) exploits the same idea , in the sense that it adapts the training strategy to the problem by introducing a data point selection rule . In ( Gal et al. , 2017 ) , the authors introduce a methodology based on Bayesian Neural Networks ( BNN ) to adapt the selection of points used for the training . Using the variational properties of BNN , they design a rule to focus the training on points that will reduce the prediction uncertainty of the NN . In ( Konyushkova et al. , 2017 ) , the construction of the selection rule is taken as a ML problem itself . See ( Settles , 2012 ) for a review of more classical AL methods . While AL selects the data points , so modifies the distribution of the initial training data set , VBSW is applied independently of the training so the distribution of the weights can not change throughout the training . Examples Weighting - VBSW can be categorized as an example weighting algorithm . The idea of weighting the data set has already been explored in different ways and for different purposes . While curriculum learning ( Bengio et al. , 2009 ; Matiisen et al. , 2017 ) starts the training with easier examples , Self paced learning ( Kumar et al. , 2010 ; Jiang et al. , 2015 ) downscales harder examples . However , some works have proven that focusing on harder examples at the beginning of the learning could accelerate it . In ( Shrivastava et al. , 2016 ) , hard example mining is performed to give more importance to harder examples by selecting them primarily . Example weighting is used in ( Cui et al. , 2019 ) to tackle the class imbalance problem by weighting rarer , so harder examples . At the contrary , in ( Liu & Tao , 2016 ) it is used to solve the noisy label problem by focusing on cleaner , so easier examples . All these ideas show that depending on the application , example weighting can be performed in an opposed manner . Some works aim at going beyond this opposition by proposing more general methodologies . In ( Chang et al. , 2017 ) , the authors use the variance of the prediction of each point throughout the training to decide whether it should be weighted or not . A meta learning approach is proposed in ( Ren et al. , 2018 ) , where the authors choose the weights after an optimization loop included in the training . VBSW stands out from the previously mentioned example weighting methods because it is built on a more general assumption that a model would simply need more points to learn more complicated functions . Its effect is to improve the performances of a NN , without solving data set specific problems like class imbalance or noisy labels . Importance Sampling - Some of the previously mentioned methods use importance sampling to design the weights of the data set or to correct the bias induced by the sample selection ( Katharopoulos & Fleuret , 2018 ) . Here , we construct a new distribution that could be interpreted as an importance distribution . However , we weight the data points to simulate this distribution , not to correct a bias induced by this distribution . Generalization Bound - Generalization bound for the learning theory of NN have motivated many works , most of which are reviewed in ( Jakubovitz et al. , 2018 ) . In Bartlett et al . ( 1998 ) , Bartlett et al . ( 2019 ) , the authors focus on VC-dimension , a measure which depends on the number of parameters of NNs . Arora et al . ( 2018 ) introduces a compression approach that aims at reducing the number of model parameters to investigate its generalization capacities . PAC-Bayes analysis constructs generalization bounds using a priori and a posteriori distributions over the possible models . It is investigated for example in Neyshabur et al . ( 2018 ) ; Bartlett et al . ( 2017 ) , and Neyshabur et al . ( 2017 ) ; Xu & Mannor ( 2012 ) links PAC-Bayes theory to the notion of sharpness of a NN , i.e . its robustness to small perturbation . While sharpness of the model is often mentioned in the previous works , our bound includes the derivatives of f , which can be seen as an indicator of the sharpness of the function to learn . Even if it uses elements of previous works , like the Lipschitz constant of fθ , our work does not pretend to tighten and improve the already existing generalization bounds , but only emphasizes the intuition that the NN would need more points to capture sharper functions . In a sense , it investigates the robustness to perturbations in the input space , not in the parameter space . 3 A NEW TRAINING DISTRIBUTION BASED ON TAYLOR EXPANSION . In this section , we first illustrate why a NN may need more points where f is steep by deriving a generalization bound that involves the derivatives of f . Then , using Taylor expansion , we build a new training distribution that improves the performances of a NN on simple functions . 3.1 PROBLEM FORMULATION . We formalize the supervised ML task as approximating a function f : S ⊂ Rni → Rno with an ML model fθ parametrized by θ , where S is a measured sub-space of Rni depending on the application . To this end , we are given a training data set of N points , { X1 , ... , XN } ∈ S , drawn from X ∼ dPX and their point-wise values , or labels { f ( X1 ) , ... , f ( XN ) } . Parameters θ have to be found in order to minimize an integrated loss function JX ( θ ) = EX [ L ( fθ ( X ) , f ( X ) ) ] , with L the loss function , L : Rno × Rno → R. The data allow estimating JX ( θ ) by ĴX ( θ ) = 1N ∑N i=1 L ( fθ ( Xi ) , f ( Xi ) ) . Then , an optimization algorithm is used to find a minimum of ĴX ( θ ) w.r.t . θ .

Summary: A method for computing sample learning weights based on variance is proposed. The method is model independent and a simple k-NN based estimator for the weights is derived. The authors justify their work by appealing to a novel generalisation bound. Overall the idea is interesting but the exposition needs to be significantly improved as proofs are difficult to follow as it currently stands.