Did you specify all the training details (e.g., data splits, hyperparameters, how they

Do the main claims made in the abstract and introduction accurately reflect the paper's

Re: Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators (ID=1064). We thank the reviewers for reviewing our work. We will update the paper based on the suggestions. On what occasion would the diffeomorphic universality results be useful other than distribution approximation? Thank you for pointing out the missing references.

Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread useof neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce. To bridge this gap, we study theoretical properties of deep neural networks on modeling non-linear time series data. Specifically, non-asymptotic bounds for prediction error of (sparse) feed-forward neural network with ReLU activation function is established under mixing-type assumptions. These assumptions are mild such that they include a wide range of time series models including auto-regressive models. Compared to independent observations, established convergence rates have additional logarithmic factors to compensate for additional complexity due to dependence among data points. The theoretical results are supported via various numerical simulation settings as well as an application to a macroeconomic data set.

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces.

Do the main claims made in the abstract and introduction accurately reflect the paper's If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? Did you include the total amount of compute and the type of resources used (e.g., type Did you include any new assets either in the supplemental material or as a URL? [N/A] Did you discuss whether and how consent was obtained from people whose data you're If you used crowdsourcing or conducted research with human subjects... (a) We assume data are generated by Equation 12 which is the process adopted by Arjovsky et al. B.1 Implementation Resources Our implementations of EDNIL are in the repository All experiments were run on a GeForce RTX 3090 machine. The training time and GPU memory consumption of EDNIL are specified in Table 7. In Adult-Confounded and CMNIST, full-batch training is implemented due to enough memory space.

We prove that, for a linear parametrization, gradient descent converges to global optima despite non-linearity and non-convexity introduced by the implicit representation.

Thus, rank( P) = 1 followed by the definition of the tensor rank. The following proposition is related with the second paragraph in Section 3.4. Next, we show the opposite direction. The following proposition is related to the second paragraph in Section 3.4. A.5 Proof of Proposition 5 The following discussion is related to the third paragraph in Section 3.4.

We study the problem of off-policy evaluation (OPE) for episodic Partially Observable Markov Decision Processes (POMDPs) with continuous states.