Adversarial Inductive Transfer Learning with input and output space adaptation
Authors: Hossein Sharifi-Noghabi
Shuman Peng
Olga Zolotareva
Colin C. Collins
Martin Ester
Venue:  (2020)
Keywords: Inductive transfer learning
adversarial learning
multi-task learning
pharmacogenomics
precision oncology

Abstract: We propose Adversarial Inductive Transfer Learning (AITL), a method for addressing discrepancies in input and output spaces between source and target domains. AITL utilizes adversarial domain adaptation and multi-task learning to address these discrepancies. Our motivating application is pharmacogenomics where the goal is to predict drug response in patients using their genomic information. The challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines) and clinical datasets. Discrepancies exist between 1) the genomic data of pre-clinical and clinical datasets (the input space), and 2) the different measures of the drug response (the output space). To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.

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ADVERSARIAL INDUCTIVE TRANSFER LEARNING WITH INPUT AND OUTPUT SPACE ADAPTATION
Anonymous authors Paper under double-blind review
ABSTRACT
We propose Adversarial Inductive Transfer Learning (AITL), a method for ad- dressing discrepancies input and output spaces between source and target do- mains. AITI utilizes adversarial domain adaptation and multi-task learning to address these discrepancies. Our motivating application is pharmacogenomics where the goal is predict drug response in patients using their genomic informa- tion. The challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating need for transfer learning bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines) and clinical datasets. Discrepancies exist between the genomic data of pre-clinical and clin- ical datasets (the input space), and 2) the different measures of the drug response (the output space). To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITI outperforms state-of-the -h-tte-art pharmacoge- nomics and transfer learning baselines and may guide precision oncology more accurately.
INTRODUCTION
Deep neural networks (Goodfellow et al., 2016) have demonstrated the state-of-the-as art performance in different problems, ranging from computer vision and natural language processing to genomics (Eraslan et al., 2019) and medicine (Topol, 2019). However, these networks often require a large number of samples for training, which is challenging and sometimes impossible to obtain in the real world applications.
Transfer learning (Pan & Yang, 2009) attempts to solve this challenge by leveraging the knowledge in source domain, large data-rich dataset, to improve the generalization performance on a small targer domain. Training model on the source domain and testing it on the target domain violates the i.i.d assumption that the train and test data are from the same distribution. The discrepancy in the input space decreases the prediction accuracy on the test data, which leads to poor generalization (Zhang et al., 2019). Many methods have been proposed to minimize the discrepancy between the source and the target domains using different metrics such as Jensen Shannon Divergence (Ganin & Lempitsky, 2014), Maximum Mean Discrepancy (Gretton et al., 2012), and Margin Disparity Dis- crepancy (Zhang et al., 2019). While transductive transfer learning (e.g. domain adaptation) uses a labeled source domain to improve generalization learning (e.g. few-shot learning) uses a labeled source domain to improve the generalization on labeled target domain where label spaces different in the source and the target domains (Pan & Yang. 2009).
unlabeled target domain, inductive transfer
Adversarial domain adaptation has shown great performance in addressing the discrepancy in the input space for different applications (Schoenauer-Sebar et al., 2019; Hosseini-Ass et al., 2018; Pin- heiro, 2018: Zou al., 2018; Tsai et al., 2018: Long et al., 2018; Chen et al., 2017: Tzeng et al., 2017), however, adversarial adaptation to address the discrepancies in both the input and output spaces has not yet been explored, Our motivating application is pharmacogenomics (Smirnov al., 2017) where the goal is to predict response to cancer drug given the genomic data (e.g. gene ex- pression). Since clinical datasets in pharmacogenomics (patients) are small and hard to obtain, many studies have focused large pre-clinical pharmacogenomies datasets such cancer cell lines proxy to patients (Barretina et al., 2012: Iorio et al., 2016). A majority of the current methods are trained on cell line datasets and then tested on other cell line or patient datasets (Sharifi-Noghal

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et al., 2019b; Geeleher al., 2014). However, cell lines and patients data, even with the same set of genes, do not have identical distributions due to the lack of an immune system and the tumor mi- croenvironment in cell lines (Mourragui et al., 2019). Moreover, in cell lines, the response is often measured by the drug concentration that reduces viability by 50% (IC50), whereas in patients, is often based changes in the size of the tumor and measured by metrics such as response evaluation criteria in solid tumors (RECIST) (Schwartz et al., 2016). This means that drug response prediction regression problem in cell lines but classification problem in patients. Therefore, discrepan- cies exist in both the input and output spaces in pharmacogenomics datasets. Table A1 provides the definition of these biological terms.
In this paper, we propose Adversarial Inductive Transfer Learning (AITL), the first adversarial method of inductive transfer learning. Different from existing methods for transfer learning, AITI, adapts not only the input space but also the output space. Our motivating application is transfer learning for pharmacogenomics datasets. In our driving application, the source domain is the gene expression data obtained from the cell lines and the target domain is the gene expression data ob- tained from patients. Both domains have the same set of genes (i.e., raw feature representation). Discrepancies exist between the gene expression data in the input space, and the measure of the drug response in the output space. AITL learns features for the source and target samples and uses these features as input for multi-task subnetwork to predict drug response for both the source and the target samples. The output space discrepancy is addressed by the multi-task subnetwork, which has one shared layer and separate classification and regression towers, and assigns binary labels (called cross-domain labels) to the source samples. The multi-task subnetwork also alleviates the problem of small sample size in the target domain by sharing the first layer with the source domain. To address the discrepancy in the input space, AITI. performs adversarial domain adaptation. The goal is that features learned for the source samples should be domain-invann to the features learned for the target samples to fool global discriminator that receives samples from both domains. Moreover, with the cross-domain binary labels available for the source samples, AITL further regularizes the learned features by class-wist discriminators. A class wise discrimi- nator receives source and target samples from the same class label and should not be able to predict the domain accurately.
and similar enough
We evaluated the performance of AITI. and state-of-the-art inductive and adversarial transductive transfer learning baselines on pharmacogenimes datasets in terms of the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recal curve (AUPR). In our experiments, AITL achieved substantial improvement compared to the baselines, demon- strating the potential of transfer learning for drug response prediction, a crucial task of precision oncology.
2 RELATED WORK
2.1 TRANSFER LEARNING
Following the notation of (Pan & Yang. 2009), domain like DM is defined by raw in- put feature space! X and a probability distribution p(X), where X {ai, 22, ..., In and x; is the i-th raw feature vector of X. A task T is associated with DM {X, p(X), where T = (Y, F()) is defined by a label space Y and a predictive function F()) which is learned from training data of the form (X, Y),,, where X E X and Y Y. A source domain is defined as DMg = {(T,, Mo,) (Tag; Msg), (IS..g Msngg) and a target domain is defined as DMT = ( Mt,), ), (Teg, Vt2).... (Trapy Mtag }}. where &, E X5. E Xx, ys E Ys, and E Yr. Since 77 << ny and is challenging to train a model only on the target domain, transfer learning aims improve the generalization
target task Ty using the knowledge in DMs and DMr and their corresponding tasks Ts and Ty. Transfer learning can be categorized into three categories: 1) unsupervised transfer learning, 2) transductive transfer learning, and 3) inductive transfer learning. In unsupervised transfer learning, there is no label in the source and target domains. In transductive transfer learning, source domain is labeled but target domain is unlabeled, domains can be either the same or different (domain adaptation), but source and target tasks are the same. In inductive transfer learning, target domain labeled and source domain can be either labeled or unlabeled, and
This is different from learned features by the network
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domains be the same different, but in this category tasks are always different (Pan & Yang, 2009).
2.2 INDUCTIVE TRANSFER LEARNING
There are three approaches to inductive transfer learning: 1) deep metric learning, 2) few-shot learn- ing, and 3) weight transfer (Scott et al., 2018). Deep metric learning methods are independent of the number of samples in each class of the target domain, denoted by k, meaning that they work for small and large values. Few-shot learning methods focus on small (k I 20). Finally, weight transfer methods require a large k (k Z 100 k z 1000) (Scott et al., 2018). Figure Al (in Ap- pendix) presents this taxonomy.
In drug response prediction, the target domain small, which means a limited number of samples for each class is available, therefore, few-shot learning is more suitable for such a problem. Few- shot learning involves training classifier to recogmize new classes, provided only a small number of examples from each of these new classes in the training data (Snell et al., 2017). Various methods have been proposed for few-shot learning (Chen et al., 2019; Scott et al., 2018; Snell et al., 2017; Vinyals et al., 2016). For example, Prototypical Networks (ProtoNet) (Snell et al., 2017) constructs prototypical representatives (class means) from source domain learned features and compares the Euclidean distance between the target domain learned features and these class representatives to assign labels to the target samples.
2.3 ADVERSARIAL TRANSFER LEARNING
Recent advances in adversarial learning leverage deep neural networks to learn transferable repre- sentation that disentangles domain-invarian and class-invariant features from different domains and matches them properly (Peng et al., 2019; Zhang al., 2019; Long et al., 2018). In this section, we first introduce the Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), and then introduce some of the existing works on adversarial transfer learning.
2.3.1 GENERATIVE ADVERSARIAL NETWORKS
GANs (Goodfellow et al.. 2014) attempt to learn the distribution of the input data via a minimax framework where two networks are competing: discriminator D and a generator G. The generator tries to create fake samples from a randomly sampled latent variable that fool the discriminator, while the discriminator tries to catch these fake samples and discriminate them from the real ones. Therefore, the generator wants to minimize its error, while the discriminator wants to maximize its accuracy:
(G.MaVV(G.D)
= Σ log[D(x)]+ +
Σ D(G())
-
(1)
G D
datto
zownnss
majority of literature on adversarial transfer learning are for transductive transfer learning where the source domain is labeled while the target domain is unlabeled.
2.3.2 ADVERSARIAL. TRANSDUCTIVE TRANSFER LEARNING
Transductive transfer learning, often referred to as domain adaptation, is the most common scenario in transfer learning. Various methods have been proposed for adversarial transductive transfer learn- ing different applications such as image segmentation (Chen et al., 2017: Tsai et al., 2018), image classification (Tzeng et al., 2017; Long et al., 2018), speech recognition (Hosseini-Asl et al., 2018), domain adaptation under label-shift (Azizzadeneshelii et al., 2019), partial domain adaptation (You al., 2019), and multiple domain adaptation (Schoenauer-Selee Sebag et al., 2019). The idea of these methods is that features extracted from source and target samples should be similar enough to fool global discriminator (Tzeng al., 2017) and/or class-wise discriminators (Chen al., 2017).
2.4 DRUG RESPONSE PREDICTION
The goal of precision oncology is to tailor a treatment for a cancer patient using genomic informa- tion of that patient. However, currently, only about 5% of the patients can benefit from precision
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oncology because response to drug is a highly complex phenotype and it depends diverse ge- netic and/or non-gen con-genetic factors (Marquart et al., 2018).
Pre-clinical pharmacogenomies datasets such cancer cell lines (lorio et al., 2016) and patient- derived xenografts (PDX) (Gao et al., 2015) are reliable proxies to study the associations between cancer treatieett The advantage of these resources is
the genomic landscape and the response that they can be screened with hundreds of drugs - chemotherapy agents and targeted therapeutics which is impossible for patients. For example, in the Genomics of Drug Sensitivity in Cancer (GDSC) dataset (Iorio et al., 2016) over 1000 pan-cancer cell lines screened with 265 chemotherapy and targeted drugs are available. Another advantage of the pre-clinical datasets that they are often significantly larger than patient datasets with known drug response (labels).
These advantages of pre-clinical datasets make them a suitable resource to develop computational methods for drug response prediction (Smirnov et al., 2017). Various methods have been developed to predict drug response from single or multiple types of genomic data. For example, Geelcher et al. (2014) proposed a ridgeeregression method to predict drug response based gene expression data. Sharifi-Noghabe et al. (2019b) showed that integrating multiple data types with deep neural networks and transfer learning via sample transfer improves the accuracy of drug response prediction.
3
ADVERSARIAL INDUCTIVE TRANSFER LEARNING
3.1 PROBLEM DEFINITION
Given a labeled source domain DMs with a learning task T's and a labeled target domain DMT with learning task Tr, where Tx F Ts, and p(X²) * p(Xs), where Xs. Xy X, we assume that the source and the target domains are not the same due to different probability distributions. The goal of Adversarial Inductive Transfer Learning (AITL) is utilize the source and target domains and their tasks in order to improve the learning of (-))) on DMT.
In the area of pharmacogenomics, the source domain is the gene expression data obtained from the cell lines, and the source task is to predict the drug response in the form of IC50 values. The target domain consists of gene expression data obtained from patients, and the target task is to predict drug response in a different form often change in the size of tumor after receiving the drug. In this setting, p(Xr) * p(Xs) because cell lines are different from patients even with the same set of genes. Additionally, YT jf Ys because for the target task Yr E {0, 13. drug response in patients is a binary outcome, but for the source task Ys E R+, drug response cell lines is continuous outcome. As result, AITL needs to address these discrepancies in the input and output spaces.
3.2 THE AITL METHOО
Our proposed AITL method takes input data from the source and target domains, and achieves the following three objectives: first, it makes predictions for the target domain using both of the input domains and their corresponding tasks, second, it addresses the discrepancy in the output space between the source and target tasks, and third, it addresses the discrepancy in the input space. AITL is a neural network consisting of four components:
The feature extractor receives the input data from the source and target domains and extracts salient features, which are then sent to the multi-task subnetwork component.
The multi-task subnetwork takes the extracted features of source and target samples and maps them to their corresponding labels and makes predictions for them. This component has a shared layer and two task-speciie towers for regression (source task) and classifica- tion (target task). Therefore, by training the multi-task subnetwork the source and target samples, it addresses the small sample size challenge in the target domain. In addition, also addresses the discrepancy in the output space by assigning cross-domain labels (binary labels in this case) to the source samples (for which only continuous labels are available) using its classification tower.
The global discriminator receives extracted features of source and target samples and pre- dicts if an input sample is from the source or the target domain. To address the discrepancy in the input space, these features should be domain-invarien nator cannot predict their domain labels accurately. This goal is achieved by adversarial learning.
that the global discrimi-
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The class wise discriminators further reduce the discrepancy in the imput space by adversar- ial learning at the level of the different classes, i.c., extracted features of source and target samples from the same class go the discriminator for that class and this discriminator should not be able to predict if an input sample from a given class is from the source or the target domain.
The AITL cost function consists of a classification loss, regression loss, and global and class-wite discriminator adversarial losses and is optimized end-to-end. An overview of the proposed method presented in figure
Global
discriminator
Source data
Sumples
Regression
Adversarial
Feature
Multi-task
loss
0.0
loss
subnetwork
Q.O 00000OM
o ODD
Class-wise
discriminstors
Sumples
Target data
1OO 0. 0000000
GOODER
Adversarial Joss for class
Clasification loss
Adversarial
Cross-domsin bintry labels assigned to source domsoin
class
Overview of AITL: First, the feature extractor receives source and target samples and learns feature for them. Then, the multi-task subnetwork uses these features make predictions for the source and target samples and also assigns cross-domain labels to the source samples. The multi-task subnetwork addresses the discrepancy in the output space. Finally, to address the input space discrepancy, global and class- wise discriminators receive the extracted features and regularize the feature extractor learn domain-invariant features.
Figure 1:
3.2. FEATURE EXTRACTOR
To learn salient features in lower dimensions for the input data, we design feature extractor compo- The feature extractor is one-layer fully-connected subnetwork with batch normalization and the ReLUU activation function that receives both the source and target samples as input. We denote the feature extractor f(.):
f(x)( E {S,T}
(2)
where Z denotes the extracted features for input X which is from either the source (S) or the target (T) domain. In our driving application, the feature extractor learns features for the cell line and patient data.
3.2.2 MULTI-TASK SUBNETWORK
After extracting features of the input samples, we want to use these learned features to 1) make predictions for target samples, and 2) address the discrepancy between the source and the target domains in the output space. To achieve these goals, multi-task subnetwork with a shared layer g(-) and two task-specific towers Ms(-) and Mr(-) is designed, where Mg is for regression (the source task) and My is for classification (the target task):
Y, (,(g(Z)),, {S,T)
The performance of the multi-task subnetwork component is evaluated based on a binary-cross en- tropy loss for the classification task on the target samples and mean squared loss for the regression task on the source samples:
LBCE(XT, Yr. S. Mr) =
Σ
[y log + (1 21) log(1 - -we)
(4)
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(Mse(Xs, J.,
E
(Ts y,)
(5)
Where Ys and Yr are the true labels of the source and the target samples, respectively, and LBCE and LASE are the corresponding losses for the target and the source domains, respectively. The multi-task subnetwork component outputs 1) the predicted labels for the target samples, and 2) the assigned cross-domain labels for the source samples. The classification tower in the multi- task subnetwork makes predictions for the source samples and assigns binary labels (responder or non-responder) because such labels do not exist for the source samples. Therefore, the multi-task subnetwork adapts the output space of the source and the target domains by assigning cross- cross-domain labels to the source domain. The multi-task subnetwork has a shared fully-connected layer with batch normalization and the ReLU activation function. The regression tower has layers with batch normalization and the ReLU activation function. The classification tower also has two fully connected layer with batch normalization and the ReLU activation function in the first layer and the Sigmoid activation function in the second layer. In our driving application the multi task subnetwork predicts IC50 values for the cell lines and the binary response outcome for the patients. Moreover, it also assigns binary labels to the cell lines which is similar to those of the patients.
3.2.3 GLOBAL DISCRIMINATOR
The goal of this component is to address the discrepancy in the input space by adversarial learning of features. To achieve this goal, a discriminator receives source and target extracted features from the feature extractor and classifies them into their corresponding domain. The feature extractor should learn comain-invariinn features to fool the global discriminator. In our driving application the global discriminator should not be able to recognize if the extracted features of sample are from a cell line or a patient. This discriminator is a one-layer subnetwork with the Sigmoid activation function denoted by Dc(.). The adversarie loss for Dc(.) is follows:
Lado Da (Xs, XT, DG) =- Σ
flog Do(((E)))
[log(1 Log(1 Do(f( (6)
2snXs
xe~X
3.2.4 CLASS-WISE DISCRIMINATORS
With cross-domain binary labels available for the source domain, AITL further reduces the discrep- ancy between the input domains via class-wise discriminators. The goal is to learn domain-invariant features with respect to specific class labels such that they fool corresponding class-wise discrimi- nators. Therefore, extracted features of the target samples in class and those of the source domain which the multi-task subnetwork assigned to class i, will go to the discriminator for class We denote such class-wise discriminator as DC,, The adversarial loss for DC2 is follows:
LadvDo, Ys, XT, YT, DC,) =
Σ
Dlg (f,(((
Σ
[og(1-DDC(((x))))
(X..
(#L))~(XX.))))
(7)
In our driving application the class-wise discriminator for the responder samples should not be able to recognize if the extracted features of responder sample are from cell line or a patient (similarly for non-responder sample). Similar to the global discriminator, class-wise discriminators are also one-layer fully-connected subnetworks with the Sigmoid activation function.
3.2.5 COST FUNCTION
To optimize the entire network in an end-to-end fashion, we design the cost function as follows:
J LBCE LMSE + AgLadvDa + Apc LadeDC.
(8)
Where, Ag and ApG are adversarial regularization coefficients for the global and Blssswiie dis- criminators, respectively.

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4 EXPERIMENTAL RESULTS
4.1 DATASETS
In our experiments, we used the following datasets (See Table A2 in the Appendix for more detail);
The Genomics of Drug Sensitivity in Cancer (GDSC) cell lines dataset, consisting of thousand cell lines from different cancer types, screened with 265 targeted and chemother- apy drugs. (lorio et al., 2016)
The Patient-Derived Xenograft (PDX) Encyclopedia dataset, consisting of more than 300 PDX samples for different cancer types, screened with 34 targeted and chemotherapy drugs. (Gao et al., 2015)
The Cancer Genome Atlas (TCGA) (Weinstein et al., 2013) containing total number of 117 patients with diverse cancer types, treated with Cisplatin, Docetaxel, or Paclitaxel.
Patient datasets from nine clinical trial cohorts containing a total number of 491 patients with diverse cancer types, treated with Bortezomib (Amin et al., 2014; Mulligan et al., 2007), Cisplatin (Silver et al., 2016; Marchion et al., 2011), Docetaxel (Hatzis et al., 2011; Lehmann al., 2011: Chang et al., 2005), or Paclitaxel (Hatzis et al., 2011; Bauer al., 2010; Ahmed et al., 2007).
The GDSC dataset was used as the source domain, and all the other datasets were used as the tar- get domain. For the GDSC dataset, raw gene expression data were downloaded from ArrayExpress (E-MTAB-3610) and release 7.0 of the dataset was used obtain the response outcome. Gene ex- pression data of TCGA patients were downloaded from the Firchose Broad GDAC and the response outcome was obtained from (Ding et al., 2016). Patient datasets from clinical trials were obtained from the Gene Expression Omnibus (GEO), and the PDX dataset was obtained from the supplemen- tary material of (Gao et al., 2015). For each drug, we selected those patient datasets that applied a comparable measure of the drug response. For preprocessing, the same procedure was adopted as described in the supplementary material of "Sharifi-Noghaiee et al., 2019b) for the raw gene expres- sion data (normalized and z-score transformed) and the drug response data. After the preprocessing. source and target domains had the same number of genes.
4.2 EXPERIMENTAL DESIGN
We designed our experiments answer the following three questions:
1. Does AITL outperform baselines that are trained only cell lines and then evaluated on patients (without transfer learning)? To answer this question, we compared AITL against (Geeleher et al., 2014) and (Sharifi-No) -ooghabi et al., 2019b) (MOLI) which are state-of-the-an art methods of drug response prediction that do not perform domain adaptation.
Does AITL outperform baselines that adopt adversarial transductive transfer learning (without adaptation of the output space)? To answer this question, we compared AITL against (Tzeng et al., 2017) (ADDA) and (Chen al., 2017), sfate-of-the-arr methods of adversarial transductive transfer learning with global and class-wise discriminators, respectively.
3. Does AITL outperform baseline for inductive transfer learning? To answer this last question, compared AITL against (Snell al., 2017) (ProtoNet) which is the state-of-the-art inductive transfer learning method for small numbers of examples per class.
Based on the availability of patient/PDX datasets for drug, we experimented with four different drugs: Bortezomib, Cisplatin, Docetaxel, and Paclitaxel. It is important to note that these drugs have different mechanisms and are being prescribed for different cancers. For example, Docetaxel is a chemotherapy drug mostly known for treating breast cancer patients (Chang et al., 2005), while Bortezomib a targeted drug mostly used for multiple myeloma patients (Amin et al., 2014). There- fore, the datasets we have selected cover different types of anti-cancer drugs.
In addition to the experimental comparison against published methods, we also performed an ab- lation study investigate the impact of the different AITL components separately. AITL-AD denotes a version of AITL without the adversarial adaptation components, which means the net- work only contains the multi-task subnetwork. AITI- De denotes version of AITL without the
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Table I: Performance of AITL and the baselines in terms of prediction AUROC
Method/Drug
Bortezomib
Cisplatin
Docetaxel
Paclitaxel
(Geeleher et al., 2014)
0.48
0.58
0,55
0.53
MOLI (SharifiNoogaahi et al., 2019b)
0.57
0.54
0.54
0.53
(Chen et al., 2017)
0.54
0.60=+0.14
0.52+0.002
0.58++0.04
ADDA (Tzeng et al., 2017)
0.51
0.56:10.06
0.48:10.06
did not converge
ProtoNet (Snell et al., 2017)
0.49
0.40+0.003
0,40+10.01
did not converge
AITL-AD AD
0.69
0.57
0.57
0.58
AITL- DG
0.69
0.62:10.1
0.48
0.62
AITL- DC
0.69=0.03
0.54
0.59=00007
0.59-10.03
AITL
0.74
0.66
0.64
0.61++0.04
global discriminator, which means the network only employs the multi-task subnetwork and class- wise discriminators. AITL-DCC denotes version of AITI. without the class discriminators, which means the network only contains the multi-task subnetwork and the global discriminator.
All of the baselines were trained on the same data, tested on patients/PDX for these drugs, and eventually compared to AITL in terms of prediction AUROC and AUPR. Since the majority of the studied baselines cannot use the continuous IC50 values in the source domain, binarized IC50 labels provided by (Iorio et al., 2016) using the Waterfall approach (Barretina et al., 2012) were used to train them. Finally, for the minimax optimization, gradient reversal layer was employed by AITL and the adversarial baselines (Ganin et al., 2016).
We performed 3-fold cross validation in the experiments to tune the hyper-parameters of AITL, and the baselines based on the AUROC. Two folds of the source samples were used for train- ing and the third fold for validation, similarly, two folds of the target samples were used for training and validation, and the third for the test. The hyper-parameters tuned for AITL were the number of nodes in the hidden layers, learning rates, mini-batch size, weight decay coefficient, the dropout rate, number of epochs, and the regularization coefficients. We con- sidered different ranges for each hyper-parameter and the final selected hyper-parameter settings for each drug and each method are provided in Section A.2 in the Appendix. Finally, each network was re-trained on the selected settings using the train and validation data together for each drug. We used Adagrad for optimizing the parameters of AITL and the baselines (Duchi et al., 2011) implemented in the PyTorch framework, except for the method of (Geeleher et al., 2014) which was implemented in We used the author's implementations for the method of (Geeleher al., 2014), MOLI, and ProtoNet. For ADDA, we used an existing implementation from https: /tt//ggttttu trub.uu.... com/ tranvugt/pytore plemented the method of (Chen et al., 2017) from scratch.
domain -adintaaapatttt. ion, and we im-
4.3 RESULTS
Tables and A3 (Appendix) and Figure 2 report the performance of AITL and the baselines in terms of AUROC and AUPR, respectively. To answer the first experimental question, AITL was compared to the baselines which do not any adaptation (neither the input the output space), i.e. the method of (Geeleher et al., 2014) and MOLI (Sharifi-Noghabi et al., 2019b), and AITL demonstrated better performance in both AUROC and AUPR for all of the studied drugs. This indicates that addressing the discrepancies in the input and output spaces leads to better performance compared to training model on the source domain and testing it on the target domain. To answer the second experimental question, AITL was compared to state-of-the-art methods of adversarial transductive transfer learning, i.e. ADDA (Tzeng et al., 2017) and the method of (Chen al., 2017), which address the discrepancy only in the input space. AITL achieved significantly better performance in AUROC for all of the drugs and for three out of four drugs in AUPR (the results of (Chen al., 2017) for Cisplatin were very competitive with AITL). This indicates that addressing the discrepancies in the both spaces outperforms addressing only the input space discrepancy. Finally, to answer the last experimental question, AITL was compared to ProtoNet (Snell et al., 2017) representative of inductive transfer learning with input space adaptation via shot learning. AITL outperformed this method in all of the metrics for all of the drugs.
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Bortezomib
Cisplatin
0.8
0.6
0.8
AUPR
0.5
AUPR
0.4
0..4
0.3
0.2
0.2
0.1
0.0
Goeleher MDLI
Chénet
ADDA ProtoNet
AFT.
Geolcher MOLI
Cher 2017
ADDA ProtoNet ATTL.
2014
2014
Docetaxel
Paclitaxel
0.8
0.7
0.7
0.6
0.5
AUPR
0.5
AUPR
0.4
0.4
0.3
0.3
0.2
0.2
0.1
ADDA
ProteNer AITL
0.0
MDLI
Chèn
ADDA ProtoNer
ATTL
2014
2014
Random performance
Figure 2: Performance of AITL and the baselines in terms of prediction AUPR
We note that the methods of drug response prediction without adaptation, namely the method of (Geeleher et al., 2014) and MOLI, outperformed the method of inductive transfer learning based on few-shot learning (ProtoNet). Moreover, these two methods also showed very competitive performance compared to the methods of adversarial transductive transfer learning (ADDA and the method of (Chen al., 2017)). For Paclitaxel, ADDA did not converge in the first step (training classifier on the source domain). which was also observed in another study (Sharifi- 2019b). ProtoNet also did not converge for this drug.
et al.,
We observed that AITL, using all of its components together, outperforms all the additional baselines omitting some of the components. This indicates the importance of both input and output space adaptation. The only exception was for the drug Paclitaxel, where AITL- De outperforms AITL. We believe the reason for this that this drug has the most heterogeneous target domain (see Table A1 in the appendix), and therefore the global discriminator component of AITL causes minor decrease in the performance.
All these results indicate that addressing the discrepancies in the input and output spaces between the source and target domains, via the AITL method, leads to a better prediction performance.
4.4 DISCUSSION
To our surprise, ProtoNet and ADDA could not outperform the method of (Geeleher et al., 2014) and MOLI baselines. For ProtoNet, this may be due to the depth of the backbone network. A re- cent study has shown that deeper backbone improves ProtoNet performance drastically image classification Chen et al. (2019). However, in pharmacogenomics, employing a deep backbone is not realistic because of the much smaller sample size compared to an image classification applica- tion. Another limitation for ProtoNet is the imbalanced number of training examples in different classes pharmacogenomics datasets. Specifically, the number of examples per class in the train- ing episodes is limited to the number of samples of the minority class ProtoNet requires the same number examples from each class. For ADDA, this lower performance may be due to the lack of end-to-end training of the classifier along with the global discriminator of this method. The reason

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is that end-to-end training of the classifier along with the discriminators improved the performance of the second adversarial baseline (Chen et al., 2017) in AUROC and AUPR compared to ADDA. Moreover, the method of (Chen et al., 2017) also showed relatively better performance in AUPR compared to the method of (Geeleher et al., 2014) and MOLI.
In pharnacogenomies, patient datasets are small or not publicly available due to privacy and/or data sharing issues. We believe including more patient samples and more drugs will increase general- ization capability. In addition, recent studies in pharmacogenomies have shown that using multiple genomic data types (known as multi-omics in genomics) works better than using only gene expres- sion (Sharifi-Ne) -aggibb patient samples with multi-omics and drug response data publicly available; however, in principle, AITL also works with such data. Last but not least, we used pharmacogenomies as our motivating application for this new problem of transfer learning, but we believe that AITI. can also be employed in other applications. For example, in slow progressing cancers such as prostate cancer, large patient datasets with gene expression and short-term clinical data (source domain) are available, however, patient datasets with long-term clinical data (target domain) are small. AITL may be beneficial to learn model predict these long-term clinical labels using the source domain and its short-term clinical labels (Sharifi-oohhabi et al., 2019a). Moreover, AITL can also be applied to the diagnosis of rare cancers with small sample size. Gene expression data of prevalent cancers with a large sample size, such as breast cancer, may be beneficial to learn model to diagnose these rare cancers.
al., 2019b). In this work, we did not consider such data due the lack of
5 CONCLUSION
In this paper, we introduced a new problem in transfer learning motivated by applications in phar- macogenomics. Unlike domain adaptation that only requires adaptation in the input space, this new problem requires adaptation in both the input and output spaces. To address this problem, proposed AITL, an Adversarial Inductive Transfer Learning method which, to the best of our knowledge, is the first method that addresses the discrepancies in both the input and output spaces. AITL uses feature extractor to learn features for target and source samples. Then, to address the discrepancy in the output space, AITI utilizes these features as input of a multi-task subnetwork that makes predictions for the target samples and assign dross-domain labels to the source samples. Finally, to address the input space discrepancy, AITL employs global and class-wise discriminators for learning domain-invariant features. In our motivating application, pharmacogenomies, AITL adapts the gene expression data obtained from cell lines and patients in the input space, and also adapts different measures of the drug response between cell lines and patients in the output space. In addition, AITL can also be applied to other applications such as rare cancer diagnosis or pre- dicting long-term clinical labels for slow progressing cancers. We evaluated AITI, on four different drugs and compared it against state-of-the-art baselines from three categories in terms of AUROC and AUPR. The empirical results indicated that AITL achieved a significantly better performance compared to the baselines showing the benefits of addressing the discrepancies in both the input and output spaces. We conclude that AITL may be beneficial in pharmacogenomics, a crucial task in precision oncology.
For future research directions, we believe that the TCGA dataset consisting of gene expression data of more than 12,000 patients (without drug response outcome) can be incorporated in an unsuper- vised transfer learning setting to learn better domain-invariant features between cell lines and cancer patients. In addition, we did not explore the impact of the chemical structures of the studied drugs in the prediction performance. We believe incorporating this input with transfer learning in the ge- nomic level can lead to a better performance. Currently, AITL borrows information between the input domains indirectly via its multi-task subnetwork and assignment of cross-domain labels. An interesting future direction can be to exchange this information between domains in a more explicit way. Moreover, we also did not perform theoretical analysis on this new problem of transfer learn- ing and we leave it for future work. Finally, we did not distinguish between different losses in the multi-task subnetwork, however, in reality patients more important than cell lines, and consid- ering a higher weight for the corresponding loss in the cost function can improve the prediction performance.
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A APPENDIX
A.1 SUPPLEMENTARY TABLES
Table Al: Definition of biological terminologies
Term
Definition
Cell lines
Human cells that have been immortalized grow continu- ously in the laboratory.
Patient-Derived Xenografts (PDX)
Tumor tissue taken from a patient and implanted into mice to mimic the microenvironment around the tumor.
Chemotherapy drugs
A type of treatment that stops cancer cells' growth by killing them stopping them from dividing.
Targeted drugs
A type of treatment that is designed for a specific type(s) of cancer cells with minor effect on the other cell types.
Table A2: Characteristics the datasets
Dataset
Resource
Drug
Usage
Sample Size
GSE55145 (Amin et al., 2014)
clinical trial
Bortezomib
target
67
GPE9782-GPL96 (Mulligan et al., 2007)
clinical trial
Bortezomib
target
169
GDSC (Iorio et al., 2016)
cell line
Bortezomib
source
391
GSE18864 (Silver et al., 2016)
clinical trial
Cisplatin
target
24
GSE23554 (Marchion et al., 2011)
clinical trial
Cisplatin
target
28
TCGA (Ding et al., 2016)
patient
Cisplatin
target
66
GDSC (lorio et al., 2016)
cell line
Cisplatin
source
829
GSE25065 (Hatzis et al., 2011)
clinical trial
Docetaxel
target
49
GSE28796 (Lehmann al., 2011)
clinical trial
Docetaxel
target
12
GSE6434 (Chang et al., 2005)
clinical trial
Docetaxel
target
24
TCGA (Ding al., 2016)
patient
Docetaxel
target
16
GDSC (lorio et al., 2016)
cell line
Docetaxel
source
829
GSE15622 (Ahmed et al., 2007)
clinical trial
Paclitaxel
target
20
GSE22513 (Bauer et al., 2010)
clinical trial
Paclitaxel
target
14
GSE25065 (Hatzis al., 2011)
clinical trial
Paclitaxel
target
84
PDX (Gao et al., 2015)
animal (mouse)
Paclitaxel
target
43
TCGA (Ding al., 2016)
patient
Paclitaxel
target
35
GDSC (lorio et al., 2016)
cell line
Paclitaxel
source
389
Table A3: Performance of AITL and its variants terms of prediction AUPR
Method/Drug
Bortezomib
Cisplatin
Docetaxel
Paclitaxel
AITI--A A7)
0.72-+0.04
0.85=0.06
0.74+F0.02
0.63+F0.02
AITI- DG
0.70++0.07
0.83+0.06
0.76++0.009
0.65++0.33
AITL-DC
0.70+10.05
0.81
0.74
0.63100.02
AITL
0.76:10.02
0.89
0.78-10.007
0.63+:0.04
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A.2
SELECTED HYPER- HYPER-PARAMETERS
Selected hyper-parameters for MOLI (Sharifi-Noghabi et al., 2019b):
Drug
Selected hypar-parameters
Bortezomib
64 (number of nodes in the hidden layer), 1.5 (margin for the triplet loss), 0.0001 (encoder subnetwork learning rate), 40 (epochs), 0.7 and 0.3 (encoder and classi- fier dropout rates), 0.01 (weight decay), 0.5 (classifier learning rate), 0.2 (regular- ization coefficient), 36 (batch size).
Cisplatin
64 (number of nodes in the hidden layer), 0.5 (margin for the triplet loss), 0.005 (encoder subnetwork learning rate), 40 (epochs), 0.5 and 0.5 (encoder and clas- sifier dropout rates), 0.001 (weight decay), 0.001 (classifier learning rate), 0.2 (regularization coefficient), 64 (batch size).
Docetaxel
128 (number of nodes in the hidden layer), (margin for the triplet loss), 0.05 (en- coder subnetwork learning rate), 25 (epochs), 0.6 and 0.5 (encoder and classifier dropout rates), 0.001 (weight decay), 0.001 (classifier learning rate), 0.1 (regular- ization coefficient), 36 (batch size).
Paclitaxel
64 (number of nodes in the hidden layer), (margin for the triplet loss), 0.0001 (encoder subnetwork learning rate), 15 (epochs), 0.5 and 0.5 (encoder and clas- sifier dropout rates), 0.0001 (weight decay). 0.001 (classifier learning rate), 0.3 (regularization coefficient), 14 (batch size).
Selected hyper-parameter parameters for ADDA (Tzeng et al., 2017):
Drug
Selected hyper-parameters
Bortezomib
256 (number of nodes in the hidden layer of the feature extractor trained on the source samples, feature extractor of the target samples, and also the input layer of the classifier trained on the source samples), 64 (number of nodes in the hidden layer of the discriminator). 0.01 (learning rate), 20 (epochs). 0.3 and 0.7 (dropout rates for target samples feature extractor and the discriminator, respectively), no weight decay, 16 and 16 (batch size for source and target domains, respectively).
Cisplatin
256 (number of nodes the hidden layer of the feature extractor trained on the source samples, feature extractor of the target samples, and also the input layer of the classifier trained on the source samples), 64 (number of nodes the hidden layer of the discríminator), 0.005 (learning rate), 20 (epochs), 0.3 and 0.6 (dropout rates for target samples feature extractor and the discriminator, respectively), no weight decay, 8 and 16 (batch size for source and target domains, respectively).
Docetaxel
1024 (number of nodes in the hidden layer of the feature extractor trained on the source samples, feature extractor of the target samples, and also the input layer of the classifier trained on the source samples), 512 (number of nodes in the hidden layer of the discriminator), 5e-5 (learning rate), 15 (epochs), 0.3 and 0.5 (dropout rates for target samples feature extractor and the discriminator, respectively), 0.005 (weight decay), 16 and 32 (batch size for source and target domains, respectively).
Paclitaxel
NA.
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Selected parame-s for ProtoNet (Snell al., 2017):
Drug
Selected hyper-parameeers
Bortezomib
16 (number of nodes in the hidden layer), 5e-5 and 0.5 (learning rates for training on source and target domains), 15 (number of epochs for the source and target domains), 0.7 (dropout rate for the souree and target domains), and 8 (number of support and query), 100 (number of episodes).
Cisplatin
256 (number of nodes in the hidden layer). 0.0005 and 0.5 (learning rates for training source and target domains), 15 and 10 (number of epochs for the source and target domains), 0.3 and 0.4 (dropout rate for the source and target domains), 2 and 4 (number of support and query), 100 (number of episodes).
Docetaxel
16 (number of nodes in the hidden layer), 0.0005 and 0.1 (learning rates for train- ing on source and target domains), 10 and 30 (number of epochs for the source and target domains), and 0.6 (dropout rate for the source and target domains), 4 and 8 (number of support and query), 100 (number of episodes).
Paclitaxel
NA.
Selected
for AITL:
Drug
Selected hyper-parameters
Bortezomib
1024 and 1024 (number of nodes in the hidden layers of the feature extractor), 0.0005 (learning rate), 0.2 and 0.4 (regularization for global and class-wise dis- criminators), 16 and 16 (mini-batch size for the source and target domains), 0.4 (dropout rate), 10 (epoch).
Cisplatin
512 and 16 (number of nodes in the hidden layers of the feature extractor), 0.05 (learning rate), 0.3 and 0.3 (regularization for global and class wiss discrimina- tors), 32 and (mini-batch size for the source and target domains), 0.15 (dropout rate), (epoch).
Docetaxel
512 and 256 (number nodes in the hidden layers of the feature extractor), 5e-5 (learning rate), 0.1 and 0.8 (regularization for global and class-wise discrimina- tors), 16 and 32 (mini-batch size for the source and target domains), 0.4 (dropout rate), 20 (epoch).
Paclitaxel
1024 and 1024 (number of nodes in the hidden layers of the feature extractor), 0.0001 (learnngg rate), 0.9 and 0.3 (regularization for global and class-wiss dis- criminators), 32 and 32 (mini-batch size for the source and target domains). 0.5 (dropout rate), 20 (epoch).
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Selected pyyer-parameee for (Chen et al., 2017):
Drug
Selected hyper-parameters
Bortezomib
128 (number of nodes in the hidden layer of the feature extractor), 32 (num- ber of nodes in the hidden layer of discriminators). 0.0001 (learning rate), 20 (epochs), 0.0001 (weight decay), 0.8, 0.3, 0.2, 0.6, 0.2 (dropout rates in featuer extractor, global discriminator, responder class discriminator, non-responder class discriminator, and classifier, respectively), 0.9 and 0.6 (regularization coefficients for class-wise and global discriminators, respectively), 16 and 64 (batch size for source and target domains, respectively).
Cisplatin
512 (number of nodes in the hidden layer of the feature extractor), 128 (num- ber of nodes in the hidden layer of discriminators), 0,0001 (learning rate), 15 (epochs), 0.0001 (weight decay). 0.3, 0.3, 0.5, 0.8, 0.5 (dropout rates in featuer extractor, global discriminator, responder class discriminator, non-responder class discriminator, and classifier, respectively), 0.4 and 0.7 (regularization coefficients for class-wise and global discriminators, respectively), 8 and 32 (batch size for source and target domains, respectively).
Docetaxel
128 (number nodes in the hidden layer of the feature extractor), (number of nodes in the hidden layer of discriminators), 0.0005 (learning rate), 5 (epochs), 0.0001 (weight decay), 0.6, 0.4, 0.3, 0.7, 0.4 (dropout rates in featuer extractor, global discriminator, responder class discriminator, non-responder class discrimi- nator, and classifier, respectively), 1 and 0.4 (regularization coefficients for class- wise and global discriminators, respectively), 8 and 32 (batch size for source and target domains, respectively).
Paclitaxel
512 (number of nodes in the hidden layer of the feature extractor), 16 (number of nodes in the hidden layer of discriminators), 0.0005 (learning rate), 10 (epochs), 0.1 (weight decay), 0.6, 0.8, 0.8, 0.7, 0.3 (dropout rates in featuer extractor, global discriminator, responder class discriminator, non-responder class discriminator, and classifier, respectively). and 0.8 (regularization coefficients for class dise and global discriminators, respectively). 64 and 16 (batch size for source and target domains, respectively).
Transductive
Metric learning
Agnostic
Transfer learning
Inductive
Few-shot Icarning
k<=20
Weight transfer
hー 100 or
10=1000
Unsupervised
Figure Al: three approaches inductive transfer learning with respect to the number of samples required for each class the target domain, denoted as k
17


Reviews:
[{"review_id": "S1x9U-W2tS", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes a novel Transfer Learning Method, namely Adversarial Inductive Transfer Learning (AITL), which adapts not only the input space but also the output space. The idea is interesting. \n\nHowever, the proposed method is not convincing from either theorical analysis or experimental results. No theorical analysis is given on the output space adaption, which is a nontrivial adaption between a discrete probability measure and a continuous probability measure. Furthermore, the method is verified on just one pharmacogenomics case. It is not convincing that the method can be used in another application.\n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "BJlfxmLTKS", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposes an adversarial transfer learning network that can handle the adaptation of both the input space and the output space. The paper is motivated by the application of drug response prediction where the source domain is cell line data and the target domain is patient data. Patient data are usually scarce, hence motivating transferring the knowledge learned from the more widely available cell line data to improve the predictive performance based on the patient data.  The idea of making use of adversarial networks is to learn a representation of the data points that is invariant to whether the data points come from the source domain or the target domain. Experiments on real-world data over four drugs demonstrate the effectiveness of the proposed methods compared to other methods that are not specifically designed for this scenario.\n\nThe major utility of the proposed method demonstrated by the paper is its empirical effectiveness on real-world data with four drugs. The reviewer has the following concerns:\n\n1. While the proposed method is capable of handling adaption of the output space between the source domain and the target domain, it makes use of a multi-task subnetwork component, which is a sensible modeling choice but it seems that the idea of leveraging a global discriminator and a class-wise discriminator has been exploited in previous works, as pointed out by the authors in Section 4.2. Therefore, the reviewer is concerned about the modeling contribution made in this paper is somewhat incremental given existing literature.\n\n2. The multi-task subnetwork component itself also seems to be a straightforward and naive way to deal with the different tasks addressed in the source domain and the target domain. For one thing, only the data of the source domain are used to optimize the loss in the source domain and only the data of the target domain are used to optimize the loss in the target domain. While it is understandable that such a decision is due to the lack of ground truth of binary label in the source domain and numeric response in the target domain, ideally, the discriminative process might gain further benefits from borrowing information across the two domains.\n\n3. It is also unclear from the paper how each component of the architecture contributes to the final performance. An ablation study that gets rid of the multi-task subnetwork, global discriminator, and class-wise discriminators (and potential combinations of these components) could help to provide better insights in the importance of each component and determine whether empirically these components do play a role coinciding with the description of the paper.\n\nOther issues/questions:\n1. Intuitively, what is the purpose of the shared layer g() in section 3.2.2?\n2. In the spirit of the class-wise discriminators, will it be helpful to also add a discriminator based on the value of IC50?\n3. Concepts and terminology are not well explained. e.g. what is a cell line? The authors should also provide further descriptions of what do the cell line and patient datasets look like early on in the paper. It was not until section 3 it is clear to the readers that in the dataseets consider in this paper, the source domain and the target domain have the same raw feature representation. Furthermore, in section 2, the authors could consider providing a taxonomy in table format to better explain different types of transfer learning and the three approaches to inductive transfer learning.  \n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A"}, "extra_text": {}}, {"review_id": "B1loCbGk5S", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "the paper studies transfer learning, which addresses the inconsistencies of the source and target domains in both input and output spaces. usually, we only worry about the inconsistencies in the input domain but here we worry about input and output. the paper proposes adversarial inductive transfer learning which uses adversarial domain adaptation for the input space and multi-task learning for the output space.\n\nthe main contribution of the paper is in identifying a new type of problem that may be worth studying. the proposal of the paper is sensible and its potential application to pharmacogenomics seems appealing. the paper shows promising performance of the proposal across datasets.", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes an adversarial inductive transfer learning method that handles distribution changes in both input and output spaces.

While the studied problem is interesting, reviewers have major concerns about the incremental modeling contribution, the lack of comparative study to existing methods and ablation study to disentangling different modules. Overall, the current study is less convincing from either theoretical analysis or experimental results.

Hence I recommend rejection.
Decision: Reject