Is Importance Weighting Incompatible with Interpolating Classifiers?

1 INTRODUCTION . Machine learning models are often evaluated on test data which differs from the data that they were trained on . A classic statistical technique to combat such distribution shift is to importance weight the loss function during training ( Shimodaira , 2000 ) . This procedure upweights points in the training data that are more likely to appear in the test data and downweights ones that are less likely . The reweighted training loss is an unbiased estimator of the test loss and can be minimized by standard algorithms , resulting in a simple and general procedure to address distribution shift . Surprisingly , recent papers ( Byrd & Lipton , 2019 ; Xu et al. , 2020 ) have found that importance weighting is ineffective in the current deep learning paradigm , where overparameterized models interpolate the training data or have vanishingly small train loss . In particular , Byrd & Lipton ( 2019 ) empirically showed that when no regularization is used , overparameterized linear and nonlinear models trained with the importance weighted cross-entropy loss ignore the importance weights . Xu et al . ( 2020 ) followed up and provided a theoretical justification for this observation in overparameterized linear and non-linear models . To build intuition about why importance weighting fails , consider linear classifiers as an example . Given linearly separable data ( x1 , y1 ) , . . . , ( xn , yn ) ∈ Rd × { −1 , 1 } , Soudry et al . ( 2018 ) showed that if gradient descent is applied to minimize an exponentially-tailed classification loss ( ∑ i∈ [ n ] ` exp ( yixi ) ) then the iterates converge in direction to the maximum margin classifier θ̂MM : = arg min‖θ‖=1 { γ : yixi · θ ≥ γ , for all i ∈ [ n ] } . Xu et al . ( 2020 ) showed that in this same setting , minimizing the importance weighted loss ( ∑ i∈ [ n ] wi ` exp ( yixi ) ) with gradient descent also results in convergence to the maximum margin classifier , regardless of the weights . To see why , consider the special case where the weights ( w1 , . . . , wn ) are positive integers . This reweighting is equivalent to simply repeating each datapoint wi times , and the maximum margin classifier over this “ new dataset ” remains unchanged . Thus , invoking the original result by Soudry et al . ( 2018 ) proves that the importance weights has no effect in correcting the distribution shift . This result can be seen in Figure 1 where we demonstrate this phenomenon in a simple toy problem . Such evidence has led some to wonder if importance weighting is fundamentally incompatible with overparameterized interpolating models . In this paper , we show that this is not the case . We find that the culprit behind the ineffectiveness of importance weighting is the exponential tail of popular losses such as the cross-entropy or the logistic . We propose altering the structure of the loss to have fatter , polynomially decaying tails instead . We theoretically and empirically demonstrate that importance weights do correct for distribution shift under such losses even for overparameterized classifiers . Our first contribution is to characterize the limiting direction of the iterates of gradient descent ( its implicit bias ) when minimizing reweighted polynomially-tailed losses with linear classifiers . We show that this limiting direction is a function of both the datapoints as well as the importance weights , unlike the maximum margin classifier that only depends on the data ( see the right half of Figure 1 ) . Next , we analyze the generalization behavior of this classifier in a label shift setting . We prove that when the weights are an exponentiation of the unbiased importance weights , the test error decays to zero in the large sample limit , regardless of the level of imbalance in the data . In contrast , we prove that the test error of the maximum margin classifier in this same setting must be at least 1/8 . Finally , we demonstrate the practical benefits of our framework by applying this approach to experiments with neural networks . In both a label shift dataset ( Imbalanced Binary CIFAR10 ) , and a subpopulation shift dataset with spurious correlations ( CelebA ( Sagawa et al. , 2019 ) ) , we find that using polynomially-tailed losses consistently leads to a gain of 2-3 % in test accuracy over using the cross-entropy loss . 2 RELATED WORK . Early work ( Shimodaira , 2000 ; Wen et al. , 2014 ) already warned against the potential ineffectiveness of importance weights on interpolating overparameterized models . Shimodaira ( 2000 ) showed that when the model is well-specified , importance weights can fail to have an effect , and that the ordinary maximum likelihood estimate is asymptotically optimal . Wen et al . ( 2014 ) showed that when there is a zero-loss minimizer of an unweighted convex loss minimization problem , then it is also a minimizer of the ( adversarially ) reweighted loss as well . Recent work ( Byrd & Lipton , 2019 ; Xu et al. , 2020 ) has shown that importance weighting fails to have an effect on neural networks trained with gradient descent , though always in the setting of exponentially-tailed losses . Sagawa et al . ( 2019 ) demonstrated that reweighting can fail to have the desired effect when unregularized distributionally robust optimization ( DRO ) methods are used in conjunction with the cross-entropy loss . They empirically showed that regularization is necessary to reap the benefits of reweighting , also observed by Byrd & Lipton ( 2019 ) . Our work also connects to literature that has studied the implicit bias of gradient descent ( Soudry et al. , 2018 ; Ji & Telgarsky , 2019 ; Nacson et al. , 2019 ) . Especially relevant is the work by Ji et al . ( 2020 ) who relate the implicit bias of gradient descent with exponentially and polynomially-tailed losses for linear classifiers to a solution of a regularized loss minimization problem . Finally , our generalization analysis draws from the growing literature focused on finite sample bounds on the test error of the maximum margin classifier in the overparameterized regime ( Chatterji & Long , 2021 ; Muthukumar et al. , 2020 ; Wang & Thrampoulidis , 2021 ; Cao et al. , 2021 ) . 3 SETTING . We consider a distribution shift setting where the training samples { ( x1 , y1 ) , . . . , ( xn , yn ) } ∈ Rd × { −1 , 1 } are drawn i.i.d . from Ptrain , and the test samples are drawn from a different distribution Ptest that is absolutely continuous with respect to Ptrain . Let fθ denote a classifier parameterized by θ . Given a feature x , a classifier maps this feature to fθ ( x ) ∈ R. In this paper we shall consider cases where the classifier is either linear ( for our theory ) or a neural network ( for our experiments ) . Our goal is to find a classifier fθ that minimizes the 0-1 loss with respect to the test distribution : TestError [ fθ ] = P ( x , y ) ∼Ptest [ sign ( fθ ( x ) ) 6= y ] . To handle the mismatch between Ptrain and Ptest , we shall study importance weighting algorithms . Given a datapoint ( x , y ) ∈ Rd× { −1 , 1 } , the classical unbiased importance weight at this datapoint is given by the ratio of densities between the test and the train distributions Ptest ( x , y ) Ptrain ( x , y ) . Using these unbiased importance weights ensure that the reweighted training loss is an unbiased estimate of the test loss . However , as noted above , past work has shown that interpolating classifiers trained with gradient descent on importance weighted exponentially-tailed losses , such as the logistic loss ` log ( z ) : = log ( 1 + exp ( −z ) ) , the exponential loss ` exp ( z ) : = exp ( −z ) , and the cross-entropy loss , ignore the importance weights . For example , consider the case when the classifier is linear fθ ( x ) = x · θ , the weights are w1 , . . . , wn > 0 , and the reweighted loss function is L̂ ( θ ) = ∑n i=1 wi ` log ( yixi · θ ) . Xu et al . ( 2020 ) showed that if the data is linearly separable then the iterates of gradient descent converge in direction to the ` 2-maximum margin classifier , θ̂MM : = arg max θ : ‖θ2‖=1 { γ : yixi · θ ≥ γ for all i ∈ [ n ] } . ( 1 ) Observe that the maximum margin classifier does not depend on the set of importance weights ( w1 , . . . , wn ) and hence may suffer large test error when there is distribution shift . Xu et al . ( 2020 ) further showed that when separability assumptions hold , non-linear classifiers ( like multilayer neural networks ) trained with gradient descent on exponentially-tailed losses are also unaffected by importance weights . We initiate a study of polynomially-tailed losses in the distribution shift setting and show that they have improved behavior with respect to importance weighting even when the model is overparameterized . Given parameters α > 0 and β ∈ R define the polynomially-tailed loss as follows : ` α , β ( z ) : = { ` left ( z ) if z < β 1 [ z− ( β−1 ) ] α if z ≥ β , where ` left is any loss function such that the overall loss function ` α , β is convex , differentiable and strictly decreasing . Several natural choices for ` left include the scaled logistic ( c1 log ( 1 + exp ( −c2z ) ) ) , exponential ( c1 exp ( −c2z ) ) or linear ( −c1z + c2 ) losses . Given a training dataset { ( x1 , y1 ) , . . . , ( xn , yn ) } and a set of weights w1 , . . . , wn ≥ 0 we let L̂α , β ( fθ ) : = n∑ i=1 wi ` α , β ( yifθ ( xi ) ) be the reweighted empirical loss on this dataset . Notation . Given a vector v , let ‖v‖ denote its Euclidean norm . For any j ∈ N , we denote the set { 1 , . . . , j } by [ j ] . A random variable ξ is 1-sub-Gaussian if for any λ ∈ R , E [ eλξ ] ≤ eλ2/2 . 4 THEORETICAL RESULTS . In this section , we present several theoretical results that justify the use of polynomially-tailed losses in conjunction with importance weights to handle distribution shifts . To let the analysis proceed , we restrict our theoretical study to linear classifiers , fθ ( x ) = x · θ , for some θ ∈ Rd . First , in Section 4.1 we shall characterize the limiting direction of gradient descent on reweighted polynomially-tailed losses and show that this direction depends on both the weights as well as the datapoints . Next , in Section 4.2 , we upper bound the test error of this limiting solution in a label shift setting . We also show that choosing weights that are obtained by exponentiating the unbiased importance weights helps in reducing the test error . Finally , in this label shift setting , we show that the maximum margin classifier suffers an error that is at least 1/8 .

Summary: In this paper, the author investigates whether importance weighting is incompatible with the training of overparameterized neural networks. In contrast to the recent observation that  importance weighting is ineffective in current deep learning paradigm (Byrd & Lipton 2019), the authors show that it could actually be helpful using polynomially-tailed losses. Both theoretical justifications and empirical evidences are provided to support the claim.