Title: ON THE MINIMAL SUPERVISION FOR TRAINING ANY BINARY CLASSIFIER FROM ONLY UNLABELED DATA

Abstract: Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM. We prove that it is impossible to estimate the risk of an arbitrary binary classifier in an unbiased manner given a single set of U data, but it becomes possible given two sets of U data with different class priors. These two facts answer a fundamental question-what the minimal supervision is for training any binary classifier from only U data. Following these findings, we propose an ERM-based learning method from two sets of U data, and then prove it is consistent. Experiments demonstrate the proposed method could train deep models and outperform state-of-the-art methods for learning from two sets of U data. -4 -2 0 2 4 (a) P component -4

1. INTRODUCTION:

With some properly chosen loss function (e.g., Bartlett et al., 2006; Tewari & Bartlett, 2007; Reid & Williamson, 2010) and regularization (e.g., Tikhonov, 1943; Srivastava et al., 2014) , empirical risk minimization (ERM) is the common practice of supervised classification (Vapnik, 1998) . Actually, ERM is used in not only supervised learning but also weakly-supervised learning. For example, in semi-supervised learning (Chapelle et al., 2006) , we have very limited labeled (L) data and a lot of unlabeled (U) data, where L data share the same form with supervised learning. Thus, it is easy to estimate the risk from only L data in order to carry out ERM, and U data are needed exclusively in regularization (including but not limited to Grandvalet & Bengio, 2004; Belkin et al., 2006; Mann & McCallum, 2007; Niu et al., 2013; Miyato et al., 2016; Laine & Aila, 2017; Tarvainen & Valpola, 2017; Luo et al., 2018; Kamnitsas et al., 2018) .

Nevertheless, L data may differ from supervised learning in not only the amount but also the form. For instance, in positive-unlabeled learning (Elkan & Noto, 2008; Ward et al., 2009) , all L data are from the positive class, and due to the lack of L data from the negative class it becomes impossible to estimate the risk from only L data. To this end, a two-step approach to ERM has been considered (du Plessis et al., 2014; 2015; Niu et al., 2016; Kiryo et al., 2017) . Firstly, the risk is rewritten into an equivalent expression, such that it just involves the same distributions from which L and U data are sampled-this step leads to certain risk estimators. Secondly, the risk is estimated from both L and U data, and the resulted empirical training risk is minimized (e.g. by Robbins & Monro, 1951; Kingma & Ba, 2015) . In this two-step approach, U data are needed absolutely in ERM itself. This indicates that risk rewrite (i.e., the technique of making the risk estimable from observable data via an equivalent expression) enables ERM in positive-unlabeled learning and is the key of success.

One step further from positive-unlabeled learning is learning from only U data without any L data. This is significantly harder than previous learning problems (cf. Figure 1 ). However, we would still like to train arbitrary binary classifier, in particular, deep networks (Goodfellow et al., 2016) . Note that for this purpose clustering is suboptimal for two major reasons. First, successful translation of clusters into meaningful classes completely relies on the critical assumption that one cluster exactly

In the left panel, (a) and (b) show positive (P) and negative (N) components of the Gaussian mixture; (c) and (d) show two distributions (with class priors 0.9 and 0.4) where U training data are drawn (marked as black points). The right panel shows the test distribution (with class prior 0.3) and data (marked as blue for P and red for N), as well as four learned classifiers. In the legend, "CCN" refers to Natarajan et al. (2013) , "UU-biased" means supervised learning taking larger-/smaller-class-prior U data as P/N data, "UU" is the proposed method, and "Oracle" means supervised learning from the same amount of L data. See Appendix B for more information. We can see that UU is almost identical to Oracle and much better than the other two methods. corresponds to one class, and hence even perfect clustering might still result in poor classification. Second, clustering must introduce additional geometric or information-theoretic assumptions upon which the learning objectives of clustering are built (e.g., Xu et al., 2004; Gomes et al., 2010) . As a consequence, we prefer ERM to clustering and then no more assumption is required.

The difficulty is how to estimate the risk from only U data, and our solution is again ERM-enabling risk rewrite in the aforementioned two-step approach. The first step should lead to an unbiased risk estimator that will be used in the second step. Subsequently, we can evaluate the empirical training and/or validation risk by plugging only U training/validation data into the risk estimator. Thus, this two-step ERM needs no L validation data for hyperparameter tuning, which is a huge advantage in training deep models nowadays. Note that given only U data, by no means could we learn the class priors (Menon et al., 2015) , so that we assume all necessary class priors are also given. This is the unique type of supervision we will leverage throughout this paper, and hence this learning problem still belongs to weakly-supervised learning rather than unsupervised learning.

In this paper, we raise a fundamental question in weakly-supervised learning-how many sets of U data with different class priors are necessary for rewriting the risk? Our answer has two aspects:

• Risk rewrite is impossible given a single set of U data (see Theorem 2 in Sec. 3); • Risk rewrite becomes possible given two sets of U data (see Theorem 4 in Sec. 4). This suggests that three class priors1 are all you need to train deep models from only U data, while any two2 should not be enough. The impossibility is a proof by contradiction, and the possibility is a proof by construction, following which we explicitly design an unbiased risk estimator. Therefore, with the help of this risk estimator, we propose an ERM-based learning method from two sets of U data. Thanks to the unbiasedness of our risk estimator, we derive an estimation error bound which certainly guarantees the consistency of learning (Mohri et al., 2012; Shalev-Shwartz & Ben-David, 2014) . 3 Experiments demonstrate that the proposed method could train multilayer perceptron, All-ConvNet (Springenberg et al., 2015) and ResNet (He et al., 2016) from two sets of U data; it could outperform state-of-the-art methods for learning from two sets of U data. See Figure 1 for how the proposed method works on a Gaussian mixture of two components.

2. PROBLEM SETTING AND RELATED WORK:

Consider the binary classification problem. Let X and Y be the input and output random variables such that Data generation process Let θ and θ be two valid class priors such that θ = θ (here it does not matter if either θ or θ equals π p or neither of them equals π p ), and let

• p(x,

p tr (x) = θp p (x) + (1 -θ)p n (x), p tr (x) = θ p p (x) + (1 -θ )p n (x)

(1) be the marginal densities from which U training data are drawn. Eq. ( 1) implies there are p tr (x, y) and p tr (x, y), whose class-conditional densities are same and equal to those of p(x, y), and whose class priors are different, i.e.,

p tr (x | y) = p tr (x | y) = p(x | y), p tr (Y = +1) = θ = θ = p tr (Y = +1).

If we could sample L data from p tr (x, y) or p tr (x, y), it would reduce to supervised learning under class-prior change (Quiñonero-Candela et al., 2009) .

Nonetheless, the problem of interest belongs to weakly-supervised learning-U training (and validation) data are supposed to be drawn according to (1). More specifically, we have

EQUATION

where n and n are two natural numbers as the sample sizes of X tr and X tr . This is exactly same as du Plessis et al. ( 2013) and Menon et al. (2015) with some different names. In Menon et al. (2015) , θ and θ are called corruption parameters, and if we assume θ > θ , p tr (x) is called the corrupted P density and p tr (x) is called the corrupted N density. Despite the same data generation process in (2), a vital difference between the problem settings is performance measures to be optimized.

Performance measures Let g : R d → R be an arbitrary decision function, i.e., g may literally be any binary classifier. Let : R → R be the loss function, such that the value (z) means the loss by predicting g(x) when the ground truth is y where z = yg(x) is the margin. The risk of g is

EQUATION

where

E p [•] means E X∼pp [•] and E n [•] means E X∼pn [•]

respectively. If is the zero-one loss that is defined by 01 (z) = (1sign(z))/2, the risk is also known as the classification error and it is the standard performance measure in classification. A balanced version of Eq. ( 3) is

EQUATION

and if is 01 , (4) is named the balanced error (Brodersen et al., 2010) . The vital difference is that (3) is chosen in the current paper whereas (4) is chosen in du Plessis et al. ( 2013) and Menon et al. (2015) as the performance measure to be optimized.

We argue that (3) is more natural as the performance measure for binary classification than (4). By the phrase "binary classification", we mean π p is neither very large nor very small. Otherwise, due to extreme values of π p (i.e., either π p ≈ 0 or π p ≈ 1), the problem under consideration should be retrieval or detection rather than binary classification. Hence, it may be misleading to optimize (4), unless π p ≈ 1 2 which implies that Eqs. ( 3) and ( 4) are essentially equivalent.

Related work:

Learning from only U data is previously regarded as discriminative clustering (Xu et al., 2004; Valizadegan & Jin, 2006; Li et al., 2009; Gomes et al., 2010; Sugiyama et al., 2014; Hu et al., 2017) . Their goals are to maximize the margin or the mutual information between X and Y . Recall that clustering is suboptimal, since it requires the cluster assumption (Chapelle et al., 2002) and it is rarely satisfied in practice that one cluster exactly corresponds to one class.

As mentioned earlier, learning from two sets of U data is already studied in du Plessis et al. ( 2013) and Menon et al. (2015) . Both of them adopt (4) as the performance measure. In the former paper, g is learned by estimating sign(p tr (x)p tr (x)). In the latter paper, g is learned by taking noisy L data from p tr (x) and p tr (x) as clean L data from p p (x) and p n (x), and then its threshold is moved to the correct value by post-processing. In summary, instead of ERM, they evidence the possibility of empirical balanced risk minimization, and no impossibility is proven.

Our findings are compatible with learning from label proportions (Quadrianto et al., 2009; Yu et al., 2013) . Quadrianto et al. (2009) proves that the minimal number of U sets is equal to the number of classes. However, their finding only holds for the linear model, the logistic loss, and their proposed method based on mean operators. On the other hand, Yu et al. ( 2013) is not ERM-based; it is based on discriminative clustering together with expectation regularization (Mann & McCallum, 2007) .

At first glance, our data generation process, using the names from Menon et al. (2015) , looks quite similar to class-conditional noise (CCN, Angluin & Laird, 1988) in learning with noisy labels (cf. Natarajan et al., 2013) . 4 In fact, Menon et al. (2015) makes use of mutually contaminated distributions (MCD, Scott et al., 2013) that is more general than CCN. Denote by ỹ and p(•) the corrupted label and distributions. Then, CCN and MCD are defined by

p( Ỹ = +1 | x) p( Ỹ = -1 | x) = T CCN p(Y = +1 | x) p(Y = -1 | x) and p(x | Ỹ = +1) p(x | Ỹ = -1) = T MCD p p (x) p n (x) ,

where both of T CCN and T MCD are 2-by-2 matrices but T CCN is column normalized and T MCD is row normalized. It has been proven in Menon et al. (2015) that CCN is a strict special case of MCD. To be clear,

p(ỹ) is fixed in CCN once p(ỹ | x) is specified while p(ỹ) is free in MCD after p(x | ỹ) is specified. Furthermore, p(x) = p(x) in CCN but p(x) = p(x) in MCD.

Due to this covariate shift, CCN methods do not fit MCD problem setting, though MCD methods fit CCN problem setting. To the best of our knowledge, the proposed method is the first MCD method based on ERM.

3. LEARNING FROM ONE SET OF U DATA:

From now on, we prove that knowing π p and θ is insufficient for rewriting R(g).

3.1. A BRIEF REVIEW OF ERM:

To begin with, we review ERM (Vapnik, 1998) by imaging that we are given X p = {x 1 , . . . , x n } ∼ p p (x) and X n = {x 1 , . . . , x n } ∼ p n (x). Then, we would go through the following procedure:

1. Choose a surrogate loss (z), so that R(g) in Eq. ( 3) is defined.

2. Choose a model G, so that min g∈G R(g) is achievable by ERM.

3. Approximate R(g) by

EQUATION

4. Minimize R pn (g), with appropriate regularization, by favorite optimization algorithm.

Here, should be classification-calibrated (Bartlett et al., 2006) ,5 in order to guarantee that R(g; ) and R(g; 01 ) have the same minimizer over all measurable functions. This minimizer is the Bayes optimal classifier and denoted by g * * = arg min g R(g). The Bayes optimal risk R(g * * ) is usually unachievable by ERM as n, n → ∞. That is why by choosing a model G, g * = arg min g∈G R(g) became the target (i.e., g pn = arg min g∈G R pn (g) will converge to g * as n, n → ∞). In statistical learning, the approximation error is R(g * ) -R(g * * ), and the estimation error is R( g pn ) -R(g * ).

Learning is consistent if and only if the estimation error converges to zero as n, n → ∞.

3.2. IMPOSSIBILITY OF RISK REWRITE:

Recall that R(g) is approximated by (5) given X p and X n , which does not work given X tr and X tr . We might rewrite R(g) so that it could be approximated given X tr and/or X tr . This is known as the backward correction in learning with noisy/corrupted labels (Patrini et al., 2017 ; see also Natarajan et al., 2013; van Rooyen & Williamson, 2018) .

Definition 1. We say that R(g) in (3) is rewritable given p tr , if and only if6 there exist constants a and b, such that for any g it holds that

EQUATION

where

E ptr [•] means E X∼ptr [•] and ¯ (z) = a (z) + b (-z)

is the corrected loss function.

In Eq. ( 6), the expectation is with respect to p tr and θ is a free variable in it. The impossibility will be stronger, if θ is unspecified and allowed to be adjusted according to π p .

Theorem 2. Let be 01 , or any bounded surrogate loss satisfying that

EQUATION

)

Assume p p and p n are almost surely separable. Then, R(g) is not rewritable though θ is free.7 

This theorem shows that under the separability assumption of p p and p n , R(g) is not rewritable. As a consequence, we lack a learning objective, that is, the empirical training risk. It is even worse-we cannot access the empirical validation risk of g after it is trained by other learning methods such as discriminative clustering. In particular, 01 satisfies (7), which implies that the common practice of hyperparameter tuning is disabled by Theorem 2, since U validation data are also drawn from p tr .

4. LEARNING FROM TWO SETS OF U DATA:

From now on, we prove that knowing π p , θ and θ is sufficient for rewriting R(g).

4.1. POSSIBILITY OF RISK REWRITE, AND UNBIASED RISK ESTIMATORS:

We have proven that R(g) is not rewritable given p tr , and Quadrianto et al. (2009) has proven that R(g) can be estimated from X tr and X tr , where g is a linear model and is the logistic loss. These facts motivate us to investigate the possibility of rewriting R(g), where g and are both arbitrary.8 

Definition 3. We say that R(g) is rewritable given p tr and p tr , if and only if9 there exist constants a, b, c and d, such that for any g it holds that

EQUATION

where z ) are the corrected loss functions.

¯ + (z) = a (z) + b (-z) and ¯ -(z) = c (z) + d (-

In Eq. ( 8), the expectations are with respect to p tr and p tr that are regarded as the corrupted p p and p n . There are two free variables θ and θ in p tr and p tr . The possibility will be stronger, if θ and θ are already specified and disallowed to be adjusted according to π p .

Theorem 4. Fix θ and θ . Assume θ > θ ; otherwise, swap p tr and p tr to make sure θ > θ . Then, R(g) is rewritable, by letting

EQUATION

Theorem (4) immediately leads to an unbiased risk estimator, namely,

EQUATION

Eq. ( 10) is useful for both training (by plugging U training data into it) and hyperparameter tuning (by plugging U validation data into it). We hereafter refer to the process of obtaining the empirical risk minimizer of (10), i.e., g uu = arg min g∈G R uu (g), as unlabeled-unlabeled (UU) learning. The proposed UU learning is by nature ERM-based, and consequently g uu can be obtained by powerful stochastic optimization algorithms (e.g., Duchi et al., 2011; Kingma & Ba, 2015) .

Simplification Note that (10) may require some efforts to implement. Fortunately, it can be simplified by employing that satisfies a symmetric condition:

EQUATION

Eq. ( 11) covers 01 , a ramp loss ramp (z) = max{0, min{1, (1z)/2}} in du Plessis et al. ( 2014) and a sigmoid loss sig (z) = 1/(1 + exp(z)) in Kiryo et al. (2017) . With the help of ( 11), ( 10) can be simplified as

EQUATION

where α = (θ + π p -2θ π p )/(θθ ) and α = (θ + π p -2θπ p )/(θθ ). Similarly, ( 12) is an unbiased risk estimator, and it is easy to implement with existing codes of cost-sensitive learning.

Special cases Consider some special cases of (10) by specifying θ and θ . It is obvious that (10) reduces to (5) for supervised learning, if θ = 1 and θ = 0. Next, (10) reduces to

R pu (g) = 1 n n i=1 π p (g(x i )) - 1 n n i=1 π p (-g(x i )) + 1 n n j=1 (-g(x j )),

if θ = 1 and θ = π p , and we recover the unbiased risk estimator in positive-unlabeled learning (du Plessis et al., 2015; Kiryo et al., 2017) . Additionally, (10) reduces to a fairly complicated unbiased risk estimator in similar-unlabeled learning (Bao et al., 2018) , if θ = π p , θ = π 2 p /(2π 2 p -2π p + 1) or vice versa. Therefore, UU learning is a very general framework in weakly-supervised learning.

4.2. CONSISTENCY AND CONVERGENCE RATE:

The consistency of UU learning is guaranteed due to the unbiasedness of (10) . In what follows, we analyze the estimation error R( g uu ) -R(g * ) (see Sec. 3.1 for the definition). To this end, assume there are C g > 0 and C > 0 such that sup g∈G g ∞ ≤ C g and sup |z|≤Cg (z) ≤ C , and assume (z) is Lipschitz continuous for all |z| ≤ C g with a Lipschitz constant L . Let R n (G) and R n (G) be the Rademacher complexity of G over p tr (x) and p tr (x) (Mohri et al., 2012; Shalev-Shwartz & Ben-David, 2014) . For convenience, denote by χ n,n = α/ √ n + α / √ n .

Theorem 5. For any δ > 0, let C δ = (ln 2/δ)/2, then we have with probability at least 1δ,

EQUATION

where the probability is over repeated sampling of X tr and X tr for training g uu .

Theorem 5 ensures that UU learning is consistent (and so are all the special cases): as n, n → ∞, R( g uu ) → R(g * ), since R n (G), R n (G) → 0 for all parametric models with a bounded norm such as deep networks trained with weight decay. Moreover, R( g uu ) → R(g * ) in O p (χ n,n ), where O p denotes the order in probability, for all linear-in-parameter models with a bounded norm, including non-parametric kernel models in reproducing kernel Hilbert spaces (Schölkopf & Smola, 2001) .

5. EXPERIMENTS:

In this section, we experimentally analyze the proposed method in training deep networks and subsequently experimentally compare it with state-of-the-art methods for learning from two sets of U data. The implementation in our experiments is based on Keras (see https://keras.io); it is available at https://github.com/lunanbit/UUlearning.

5.1. TRAINING DEEP NEURAL NETWORKS ON BENCHMARKS:

In order to analyze the proposed method, we compare it with three supervised baseline methods:

• small PN means supervised learning from 10% L data;

• PN oracle means supervised learning from 100% L data;

• small PN prior-shift means supervised learning from 10% L data under class-prior change.

Notice that the first two baselines have L data identically distributed as the test data, which is very advantageous and thus the experiments in this subsection are merely for a proof of concept.

Table 1 summarizes the benchmarks. They are converted into binary classification datasets; please see Appendix C.1 for details. X tr and X tr of the same sample size are drawn according to Eq. ( 1), where θ and θ are chosen as 0.9, 0.1 or 0.8, 0.2. The test data are just drawn from p(x, y).

Table 1 also describes the models and optimizers. In this table, FC refers to fully connected neural networks, AllConvNet refers to all convolutional net (Springenberg et al., 2015) and ResNet refers to residual networks (He et al., 2016) ; then, SGD is short for stochastic gradient descent (Robbins & Monro, 1951) and Adam is short for adaptive moment estimation (Kingma & Ba, 2015) .

Recall from Sec. 3.1 that after the model and optimizer are chosen, it remains to determine the loss (z). We have compared the sigmoid loss sig (z) and the logistic loss log (z) = ln(1 + exp(-z)), and found that the resulted classification errors are similar; please find the details in Appendix C.2. Since sig satisfies ( 11) and is compatible with ( 12), we shall adopt it as the surrogate loss.

The experimental results are reported in Figure 2 , where means and standard deviations of classification errors based on 10 random samplings are shown, and the table of final errors can be found in Appendix C.2. When θ = 0.9 and θ = 0.1 (cf. the left column), UU is comparable to PN oracle in most cases. When θ = 0.8 and θ = 0.2 (cf. the right column), UU performs slightly worse but it is still better than small PN baselines. This is because the task becomes harder when θ and θ become closer, which will be intensively investigated next.

On the closeness of θ and θ It is intuitive that if θ and θ move closer, X tr and X tr will be more similar and thus less informative. To investigate this, we test UU and CCN (Natarajan et al., 2013) on MNIST by fixing θ to 0.9 or 0.8 and gradually moving θ from 0.1 to 0.5, and the experimental results are reported in Figure 3 . We can see that when θ moves closer to θ, UU and CCN become worse, while UU is affected slightly and CCN is affected severely. The phenomenon of UU can be explained by Theorem 5, where the upper bound in ( 13) is linear in α and α which, as θ → θ, are inversely proportional to θθ . On the other hand, the phenomenon of CCN is caused by stronger covariate shift when θ moves closer to θ rather than the difficulty of the task. This illustrates CCN methods do not fit our problem setting, so that we called for some new learning method (i.e., UU). Note that there would be strong covariate shift not only by changing θ and θ but also by changing n and n . The investigation of this issue is deferred to Appendix C.2 due to limited space.

Robustness against inaccurate training class priors Hitherto, we have assumed that the values of θ and θ are accessible, which is rarely satisfied in practice. Fortunately, UU is a robust learning method against inaccurate training class priors. To show this, let and be real numbers around 1, ϑ = θ and ϑ = θ be perturbed θ and θ , and we test UU on MNIST and CIFAR-10 by drawing data using θ and θ but training models using ϑ and ϑ instead. The experimental results in Table 2 imply that UU is fairly robust to inaccurate ϑ and ϑ and can be safely applied in the wild. 

5.2. COMPARISON WITH STATE-OF-THE-ART METHODS:

Finally, we compare UU with two state-of-the-art methods for dealing with two sets of U data:10 

• proportion-SVM (pSVM, Yu et al., 2013) that is the best in learning from label proportions;

• balanced error minimization (BER, Menon et al., 2015) that is the most related work to UU.

The original codes of BER train single-hidden-layer neural networks by LBFGS (which belongs to second-order optimization) in MATLAB. For a fair comparison, we also implement BER by fixing π p to 0.5 in UU, so that UU and BER only differ in the performance measure. This new baseline is referred to as BER-FC. The information of datasets can be found in Table 3 . We work on small datasets following Menon et al. (2015) , because pSVM and BER are not reliant on stochastic optimization and cannot handle larger datasets. Furthermore, in order to try different π p , we first subsample the original datasets to match the desired π p and then calculate the sample sizes n and n according to how many P and N data there are in the subsampled datasets, where θ and θ are set as close to 0.9 and 0.1 as possible.

For UU and BER-FC, the model is FC with ReLU of depth 5 and the optimizer is SGD. We repeat this sampling-and-training process 10 times for all learning methods on all datasets.

The experimental results are reported in Table 3 , and we can see that UU is always the best method (7 out of 10 cases) or comparable to the best method (3 out of 10 cases). Moreover, the closer π p is to 0.5, the better BER and BER-FC are; however, the closer π p is to 0 or 1, the worse they are, and sometimes they are much worse than pSVM. This is because their goal is to minimize the balanced error instead of the classification error. In our experiments, pSVM falls behind, because it is based on discriminative clustering and is also not designed to minimize the classification error.

6. CONCLUSIONS:

We focused on training arbitrary binary classifier, ranging from linear to deep models, from only U data by ERM. We proved that risk rewrite as the core of ERM is impossible given a single set of U data, but it becomes possible given two sets of U data with different class priors, after we assumed that all necessary class priors are also given. This possibility led to an unbiased risk estimator, and with the help of this risk estimator we proposed UU learning, the first ERM-based learning method from two sets of U data. Experiments demonstrated that UU learning could successfully train fully connected, all convolutional and residual networks, and it compared favorably with state-of-the-art methods for learning from two sets of U data. 2 -regularization with a regularization parameter 1e-5 were applied. The optimizer was Adam with the default momentum parameters (β 1 = 0.9 and β 2 = 0.999), an initial learning rate 1e-3, and a batch size 500.

CIFAR-10 This dataset consists of 60,000 32*32 color images in 10 classes, and there are 5,000 training images and 1,000 test images per class. For CIFAR-10 dataset,

• the P class is composed of 'bird', 'cat', 'deer', 'dog', 'frog' and 'horse';

• the N class is composed of 'airplane', 'automobile', 'ship' and 'truck'.

The model was ResNet-32 (He et al., 2016) as follows. where [ •, • ] means a building block (He et al., 2016) . The optimization setup was the same as for SVHN, except that the regularization parameter was set to be 5e-3 and the initial learning rate was set to be 1e-5.

Remark:

In the experiments on the closeness of θ and θ and on the robustness against inaccurate training class priors, we sampled 40,000 training data from all the training data of MNIST in order to make it feasible to perform class-prior changes.

C.2 RESULTS:

Final classification errors Please find in Table 4 .

Comparison of different losses:

We have compared the sigmoid loss sig (z) and the logistic loss log (z) on MNIST. The experimental results are reported in Figure 4 . We can see that the resulted classification errors are similar-in fact, sig (z) is a little better.

On the variation of n and n We have further investigated the issue of covariate shift by varying n and n . Likewise, we test UU and CCN on MNIST by fixing n to 20,000 and gradually moving n from 20,000 to 4,000, where θ is fixed to 0.4 and θ is chosen from 0.9 or 0.8. The experimental results in Figure 5 indicate that when n moves farther from n , UU and CCN become worse, while UU is affected slightly and CCN is affected severely. Figure 5 is consistent with Figure 3 , showing that CCN methods do not fit our problem setting.

:

Two class-prior probabilities are of the training distributions and one is of the test distribution.

One of the training distribution and one of the test distribution, or two of the training distributions.

Learning is consistent (more specifically the learned classifier is asymptotically consistent), if and only if as the amount of training data approaches infinity, the risk of the learned classifier converges to the risk of the optimal classifier, where the optimality is defined over a given hypothesis class.

There are quite few instance-dependent noise models(Menon et al., 2016;Cheng et al., 2017), and others explore instance-independent noise models(Natarajan et al., 2013;Sukhbaatar et al., 2015;Menon et al., 2015;Liu & Tao, 2016;Goldberger & Ben-Reuven, 2017;Patrini et al., 2017; Han et al., 2018a) or assume no noise model at all(Reed et al., 2015;Jiang et al., 2018;Ren et al., 2018; Han et al., 2018b).

is classification-calibrated if and only if there is a convex, invertible, and nondecreasing transformation ψ with ψ (0) = 0, such that ψ (R(g; 01) -infg R(g; 01)) ≤ R(g; ) -infg R(g; ). If is a convex loss, it is classification-calibrated if and only if it is differentiable at the origin and (0) < 0.

This is because the backward correction in (6), if exists, would be unique.

Please find in Appendix A the proofs of theorems.

The technique that underlies Theorem 4 is totally different fromQuadrianto et al. (2009). We shall obtain (9) by solving a linear system resulted from Definition 3. As previously mentioned,Quadrianto et al. (2009) is based on mean operators, and it cannot be further generalized to handle nonlinear g or arbitrary .

This is similar because the backward correction in (8), if exists, would be unique.

We downloaded the codes by the original authors; see https://github.com/felixyu/pSVM and https://akmenon.github.io/papers/corrupted-labels/index.html.

Figures and Tables:

Figure 1: Illustrative example of classification from a Gaussian mixture dataset.

y) is the underlying joint density, • p p (x) = p(x | Y = +1) and p n (x) = p(x | Y = -1) are the class-conditional densities, • p(x) is the marginal density, and • π p = p(Y = +1) is the class-prior probability.

Figure 2: Experimental results of training deep neural networks.

Figure 3: Experimental results of moving θ closer to θ (black dashed lines are PN oracle).

0th (input) layer: (32*32*3)-1st to 11th layers: C(3*3, 16)-[C(3*3, 16), C(3*3, 16)]*5-12th to 21st layers: [C(3*3, 32), C(3*3, 32)]*5-22nd to 31st layers: [C(3*3, 64), C(3*3, 64)]*5-32nd layer: Global Average Pooling-1

Figure 4: Experimental results of comparing sig and log .

Figure 5: Experimental results of moving n farther from n (black dashed lines are PN oracle).

Specification of benchmark datasets, models, and optimization algorithms.

Table content: <table><tr><td>Dataset</td><td># Train</td><td colspan="2"># Test # Feature</td><td>πp Model g(x; θ)</td><td>Optimizer</td></tr><tr><td>MNIST</td><td colspan="2">60,000 10,000</td><td colspan="3">784 0.49 FC with ReLU (depth 5) SGD</td></tr><tr><td>Fashion-MNIST</td><td colspan="2">60,000 10,000</td><td colspan="3">784 0.50 FC with ReLU (depth 5) SGD</td></tr><tr><td>SVHN</td><td colspan="2">100,000 26,032</td><td colspan="2">3,072 0.27 AllConvNet (depth 12)</td><td>Adam</td></tr><tr><td>CIFAR-10</td><td colspan="2">50,000 10,000</td><td colspan="2">3,072 0.60 ResNet (depth 32)</td><td>Adam</td></tr></table>

Mean errors (standard deviations) in percentage given inaccurate training class priors.

Table content: <table><tr><td>Dataset</td><td>θ, θ</td><td>, = 0.8, 0.8</td><td>, = 0.9, 0.9</td><td>= = 1.0</td><td>, = 1.1, 1.1</td><td>, = 1.2, 1.2</td></tr><tr><td/><td>0.9, 0.1</td><td>2.31 (0.16)</td><td>2.31 (0.14)</td><td>2.31 (0.14)</td><td>2.32 (0.14)</td><td>2.35 (0.14)</td></tr><tr><td>MNIST</td><td>0.8, 0.2</td><td>3.00 (0.12)</td><td>3.00 (0.11)</td><td>3.01 (0.10)</td><td>3.02 (0.10)</td><td>3.01 (0.10)</td></tr><tr><td/><td>0.7, 0.3</td><td>4.24 (0.23)</td><td>4.24 (0.24)</td><td>4.24 (0.26)</td><td>4.25 (0.24)</td><td>4.25 (0.25)</td></tr><tr><td/><td>0.9, 0.1</td><td>10.19 (0.37)</td><td colspan="2">10.14 (0.29) 10.14 (0.30)</td><td>10.11 (0.34)</td><td>10.09 (0.35)</td></tr><tr><td>CIFAR-10</td><td>0.8, 0.2</td><td>10.84 (0.38)</td><td colspan="2">10.84 (0.40) 10.77 (0.40)</td><td>10.73 (0.40)</td><td>10.73 (0.40)</td></tr><tr><td/><td>0.7, 0.3</td><td>12.04 (0.61)</td><td colspan="2">12.00 (0.54) 11.92 (0.54)</td><td>11.91 (0.53)</td><td>11.88 (0.53)</td></tr><tr><td>Dataset</td><td>θ, θ</td><td>, = 0.8, 1.2</td><td>, = 0.9, 1.1</td><td>= = 1.0</td><td>, = 1.1, 0.9</td><td>, = 1.2, 0.8</td></tr><tr><td/><td>0.9, 0.1</td><td>2.30 (0.15)</td><td>2.31 (0.16)</td><td>2.31 (0.14)</td><td>2.30 (0.13)</td><td>2.30 (0.14)</td></tr><tr><td>MNIST</td><td>0.8, 0.2</td><td>3.00 (0.10)</td><td>3.00 (0.12)</td><td>3.01 (0.10)</td><td>3.02 (0.12)</td><td>3.01 (0.11)</td></tr><tr><td/><td>0.7, 0.3</td><td>4.19 (0.22)</td><td>4.22 (0.23)</td><td>4.24 (0.26)</td><td>4.24 (0.25)</td><td>4.25 (0.23)</td></tr><tr><td/><td>0.9, 0.1</td><td>10.20 (0.33)</td><td colspan="2">10.15 (0.34) 10.14 (0.30)</td><td>10.12 (0.35)</td><td>10.08 (0.37)</td></tr><tr><td>CIFAR-10</td><td>0.8, 0.2</td><td>10.94 (0.46)</td><td colspan="2">10.83 (0.39) 10.77 (0.40)</td><td>10.75 (0.37)</td><td>10.71 (0.43)</td></tr><tr><td/><td>0.7, 0.3</td><td>12.24 (0.71)</td><td colspan="2">12.05 (0.59) 11.92 (0.54)</td><td>11.88 (0.53)</td><td>11.95 (0.49)</td></tr></table>

Mean errors (standard deviations) in percentage of UU and state-of-the-art methods. Best and comparable methods (paired t-test at significance level 1%) are highlighted in boldface.The first five datasets come with the original codes of BER and USPS is from https://cs.nyu.edu/ ~roweis/data.html. The rows are arranged according to πp. In this table, # Sub means the amount of subsampled L training data, # Train means the amount of generated U training data, and ∆θ means θ -θ . The cell N/A (in MNIST row and pSVM column) is since pSVM is based on maximum margin clustering and is too slow on MNIST. The task would be harder, if πp is closer to 0.5, or # Train or ∆θ is smaller.

Table content: <table><tr><td>Dataset</td><td>πp</td><td colspan="2"># Sub # Train</td><td>∆θ</td><td>pSVM</td><td>BER</td><td>BER-FC</td><td>UU</td></tr><tr><td>pendigits</td><td>0.1</td><td>4,000</td><td colspan="2">971 0.57</td><td>4.03 (0.27)</td><td>5.51 (1.35)</td><td>5.46 (1.23)</td><td>1.97 (0.78)</td></tr><tr><td colspan="2">covertype 0.3</td><td>7,400</td><td colspan="4">3,863 0.80 14.63 (1.00) 11.33 (0.26)</td><td>5.17 (0.57)</td><td>4.97 (0.48)</td></tr><tr><td>MNIST</td><td colspan="2">0.5 11,800</td><td colspan="2">7,139 0.77</td><td>N/A</td><td>3.66 (0.20)</td><td>3.03 (0.25)</td><td>2.87 (0.28)</td></tr><tr><td colspan="2">spambase 0.7</td><td>3,570</td><td colspan="6">1,139 0.80 29.18 (1.29) 11.28 (1.73) 13.98 (1.63) 12.53 (1.00)</td></tr><tr><td>letter</td><td>0.9</td><td>5,555</td><td colspan="4">532 0.60 15.65 (4.18) 15.45 (6.99)</td><td>8.45 (2.92)</td><td>3.15 (0.84)</td></tr><tr><td/><td>0.1</td><td>4,000</td><td colspan="2">971 0.57</td><td colspan="2">5.91 (1.52) 12.69 (4.09)</td><td>8.57 (2.40)</td><td>3.74 (1.24)</td></tr><tr><td/><td>0.3</td><td>5,000</td><td colspan="2">2,605 0.80</td><td>5.55 (0.46)</td><td>5.36 (0.41)</td><td>2.75 (0.28)</td><td>2.63 (0.18)</td></tr><tr><td>USPS</td><td>0.5</td><td>4,000</td><td colspan="2">1,695 0.60</td><td>9.27 (0.61)</td><td>7.27 (1.09)</td><td>5.48 (1.33)</td><td>5.52 (1.02)</td></tr><tr><td/><td>0.7</td><td>5,720</td><td colspan="2">1,853 0.80</td><td>8.20 (0.73)</td><td>7.48 (0.65)</td><td>4.23 (0.50)</td><td>4.43 (0.94)</td></tr><tr><td/><td>0.9</td><td>4,445</td><td colspan="2">424 0.44</td><td colspan="3">9.80 (2.07) 14.13 (2.02) 18.27 (5.17)</td><td>6.20 (1.33)</td></tr></table>

Means (standard deviations) of the final classification errors in percentage corresponding to Figure2. Best and comparable methods (excluding PN oracle) based on the paired t-test at the significance level 1% are highlighted in boldface.

Table content: <table><tr><td>Dataset</td><td>θ, θ</td><td colspan="2">small PN small PN prior-shift</td><td>UU</td><td>PN oracle</td></tr><tr><td>MNIST</td><td>0.9, 0.1 0.8, 0.2</td><td>3.56 (0.13) 3.56 (0.13)</td><td>6.69 (0.23) 4.56 (0.16)</td><td colspan="2">2.37 (0.17) 1.44 (0.08) 2.55 (0.11)</td></tr><tr><td>Fashion-MNIST</td><td>0.9, 0.1 0.8, 0.2</td><td>4.76 (0.17) 4.76 (0.17)</td><td>4.93 (0.16) 4.86 (0.16)</td><td colspan="2">2.94 (0.07) 2.90 (0.10) 3.35 (0.13)</td></tr><tr><td>SVHN</td><td>0.9, 0.1 0.8, 0.2</td><td>4.28 (1.07) 4.28 (1.07)</td><td>9.26 (2.41) 6.16 (0.66)</td><td colspan="2">2.69 (0.20) 2.08 (0.43) 3.99 (0.51)</td></tr><tr><td>CIFAR-10</td><td colspan="2">0.9, 0.1 12.53 (0.69) 0.8, 0.2 12.53 (0.69)</td><td colspan="3">18.58 (1.30) 10.97 (0.91) 9.26 (0.41) 14.59 (1.05) 11.64 (0.54)</td></tr><tr><td colspan="4">4th to 6th layers: [C(3*3, 192)]*2-C(3*3, 192, 2)-</td><td/></tr><tr><td colspan="4">7th to 9th layers: C(3*3, 192)-C(1*1, 192)-C(1*1, 10)-</td><td/></tr><tr><td colspan="3">10th to 12th layers: 1000-1000-1</td><td/><td/></tr><tr><td colspan="6">where C(3*3, 96) means 96 channels of 3*3 convolutions followed by ReLU, [ • ]*2 means 2 such layers, C(3*3, 96, 2) means a similar layer but with stride 2, etc. Again, batch normalization and</td></tr></table>

Peer review (iclr2019):
 This paper studies the weak supervision setting of learning a general binary classifier from two unlabeled (U) datasets with known class balances. The authors establish that this is possible by constructing an unbiased estimator, analyze its convergence theoretically, and then run experiments using modern image classification models. Pros: - This work demonstrates, theoretically and empirically, a simple way to train generic models using only the known class balances of several sets of unlabeled data (having the same conditional distributions p(x|y))---a very interesting configuration of weak supervision, an increasingly popular and important area - The treatment is thorough, proceeding from establishing the minimum number of U datasets, constructing the estimator, analyzing convergence, and implementing thorough experiments Cons: - This is a crowded area (as covered in their related work section). As they cite, (Quadrianto et al., 2009) proposed this setting and considered linear models for k-wise classification. Moreover, the two U datasets with known class balances can equivalently be viewed as two weak / noisy label sources with known accuracies. Thus this work connects to many areas- both in noisy learning, as they cite heavily, but also in methods (in e.g. crowdsourcing and multi-source weak supervision) where several sources label unlabeled datasets with unknown accuracies (which are often estimated in an unsupervised fashion). - The overall clarity of the paper*s writing could be improved. For example, the introduction and related work sections take up a large portion of the paper, but are very dense and heavy with jargon that is not internally defined upfront; for example "risk rewrite" is introduced in paragraph 2 with no internal definition and then used subsequently throughout the paper (this defn would be simple enough to give: in the context of this paper, "risk rewrite" means a linear combination of the class-conditional losses; or more generally, the expected loss w.r.t. distribution over classes...). Also intuition could be briefly given about the theorem proof strategies. - The difference between the two class distributions over the U datasets seems like an important quantity (akin, in e.g. weak supervision / crowd source modeling papers, to quantity of how bounded away from random noise the labelers are). This is treated empirically, but would be stronger to have this show up in the theory somewhere. - Other prior work here has handled k classes with k U sets; could have extended to cover this setting too, since seems natural Overall take: This learning from label proportions setting has been covered before, but this paper presents it in an overall clean and general way, testing it empirically on modern models and datasets, which is an interesting contribution. Other minor points: - The argument for / distinction between using eqns. (3) and (4) seems a bit ad hoc / informal ("we argue that..."). This is an important point... - Theorem 1 proof seems fine, but some intuition in the main body would be nice. - What does "classification calibrated" mean? - Saying that three U sets are needed, where this includes the test set, seems a bit non-standard? Also I*m confused- isn*t a labeled test set used? So what is this third U set for? - The labels l_+ and l_- in Defn. 3 seem to imply that the two U sets are positive vs. negative; but this is not the case, correct…? - Stating both Lemma 5 and Thm 6 seems unnecessary - In Fig. 2, seems like could have trained for longer and perhaps some of the losses would have continued decreasing? In particular, small PN? Also, a table of the final test set accuracies would have been very helpful. - More detail on experimental protocol would be helpful: what kind of hyperparameter tuning was done? repeated runs averaging? It seems odd, for example in Fig. 3, that the green lines are so different in (a) vs. (c), and not in the way that one would expect given the decrease in theta