In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

[Q]: Este trabajo propone un nuevo enfoque para el apoyo de la priorización de requisitos a través de un proceso de transformación de los Casos de Uso en un grafo de dependencia a través de la aplicación de técnicas de IR, redes bayesianas y evaluación del experto en el dominio. El enfoque se presenta como un interesante aporte para la priorización de requisitos y muestra su aplicación a un caso real. El documento está bien escrito y muy bien soportado por referencias. Los resultados son robustos según el enfoque utilizado y se presentan trabajos futuros bien direccionados.  Algunos errores tipográficos o de redacción pueden ser encontrados en la página 5 (párrafo 1 y 2). Confusión por el uso de k y K en la página 5 (o error tipográfico). Puede que no quede claro la utilización del conocimiento de la priorización realizada en el caso base de prueba con respecto a la realizada semi-automáticamente por su enfoque, salvo que se utiliza el conocimiento del experto. Quizás una comparación objetiva entre las dos priorizaciones sería adecuado.
[A]: es


[Q]: The present submission discusses a "causal regularizer", which promotes the use of causal dependencies (X -> Y, where X is a feature of the learning problem, and Y is the target variable) in predictive models. Similarly, such causal regularizer penalizes the use of non-causal dependencies, which can arise due to reverse causation (Y -> X) or confounding (X <- Z -> Y, where Z is a hidden confounder).  + Overall, this submission tackles one of the most important problems in machine learning, which is to build causal models. The paper discusses and addresses this issue effectively when applied to a dataset in heart disease. In their experiments, the authors correctly identify some of the common causes of heart disease by virtue of their causal regularizer.  - The authors do not discuss the robustness of their approach with respect to choice of hyper-parameters (both describing the neural network architecture and the generative model that synthesizes artificial causal data). This seems like a crucial issue, in particular when dealing with medical data.  - The conclusions of the experimental evaluation should be discussed in greater length. On the one hand, Figure 4.a shows that there are no differences between L1 and causal regularization in terms of predictive performance, but it is difficult to conclude if this result is statistically significant without access to error-bars. On the other hand, Table 3 describes the qualitative differences between L1 and causal regularization. However, this table is hard to read: How were the 30 rows selected? What does the red highlighting mean? Are these red rows some true causal features that were missed? If so, this is related to precision. What about recall? Did the causal regularization pick up many non-causal features as causal?  - Regarding causal classifiers, this paper should do a much better job at reviewing previous work. For instance, the paper "Towards a Learning Theory of Cause-Effect Inference" from Lopez-Paz et al. is missing from the references. However, this prior work studies many of the aspects that are hinted as novel in this submission. In particular, the prior work of Lopez-Paz 1) introduces the concept of Mother distribution (referred as Nature hyper-prior in this submission) which explicitly factorizes the distribution over causes and mechanisms, 2) circumvented intractable likelihoods by synthesizing and training on causal data, 3) tackled the confounding case (compare Figure 1 of this submission and Appendix C of Lopez-Paz), and 4) dealt with discrete data seamlessly (such as the ChaLearn data from Section 5.3 in Lopez-Paz).  On a positive note, this is a well-written paper that addresses the important, under-appreciated problem of incorporating causal reasoning into machine learning. On a negative note, the novelty of the technical contributions is modest and the qualitative evaluation of the results could be greatly extended. In short, I am leaning slightly towards acceptance.
[A]: en


[Q]: This paper proposed to use the BPA criterion for classifier ensembles.  My major concern with the paper is that it attempts to mix quite a few concepts together, and as a result, some of the simple notions becomes a bit hard to understand. For example:  (1) "Distributed" in this paper basically means classifier ensembles, and has nothing to do with the distributed training or distributed computation mechanism. Granted, one can train these individual classifiers in a distributed fashion but this is not the point of the paper.  (2) The paper uses "Transfer learning" in its narrow sense: it basically means fine-tuning the last layer of a pre-trained classifier.  Aside from the concept mixture of the paper, other comments I have about the paper are:  (1) I am not sure how BPA address class inbalance better than simple re-weighting. Essentially, the BPA criteria is putting equal weights on different classes, regardless of the number of training data points each class has. This is a very easy thing to address in conventional training: adding a class-specific weight term to each data point with the value being the inverse of the number of data points will do.  (2) Algorithm 2 is not presented correctly as it implies that test data is used during training, which is not correct: only training and validation dataset should be used. I find the paper's use of "train/validation" and "test" quite confusing: why "train/validation" is always presented together? How to properly distinguish between them?  (3) If I understand correctly, the paper is proposing to compute the BPA in a batch fashion, i.e. BPA can only be computed when running the model over the full train/validation dataset. This contradicts with the stochastic gradient descent that are usually used in deep net training - how does BPA deal with that? I believe that an experimental report on the computation cost and timing is missing.  In general, I find the paper not presented in its clearest form and a number of key definitions ambiguous.
[A]:
en