Do the main claims made in the abstract and introduction accurately reflect the paper's If you ran experiments (e.g. for benchmarks)... (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] See A.2 (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? [Y es] 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) For a detailed description and intended uses, please refer to 1. A.2 Dataset Accessibility We plan to host and maintain this dataset on HuggingFace. A.4 Dataset Examples Example question-answer pairs are provided in Tables 9 10 11, . Example Question "What does the symbol mean in Equation 1?" Answer "The symbol in Equation 1 represents "follows this distribution". "Can you provide more information about what is meant by'generative process in "The generative process refers to Eq. (2), which is a conceptual equation representing Question "How does the DeepMoD method differ from what is written in/after Eq 3?" Answer "We add noise only to Question "How to do the adaptive attack based on Eq.(16)? "By Maximizing the loss in Eq (16) using an iterative method such as PGD on the end-to-end model we attempt to maximize the loss to cause misclassification while Question "How does the proposed method handle the imputed reward?" "The proposed method uses the imputed reward in the second part of Equation 1, "Table 2 is used to provide a comparison of the computational complexity of the "Optimal number of clusters affected by the number of classes or similarity between "The authors have addressed this concern by including a new experiment in Table 4 of Question "Can you clarify the values represented in Table 1?" Answer "The values in Table 1 represent the number of evasions, which shows the attack "The experiments in table 1 do not seem to favor the proposed method much; softmax Can the authors explain why this might be the case?" Answer "The proposed method reduces to empirical risk minimization with a proper loss, and However, the authors hope that addressing concerns about the method's theoretical Question "Does the first row of Table 2 correspond to the offline method?"

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? [Y es] 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) Hyper-parameter V alues learning rate 0.0005, 0.0001 batch size 16, 32 " annealing period 20000, 10000 RNN hidden dimension 64, 32, 16 Table 2: Hyper-parameters of QMIX in the Tiger-Trampoline Experiment In Section 5.1, we show the results of MAPPO and QMIX on the Tiger-Trampoline game. In the Hanabi experiments, we implement IMPROVISED as follows (better viewed together with the pseudocode). Player 1 and player 2 do not share the random seed beforehand. We do not anticipate any immediate negative impact from this work.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you discuss any potential negative societal impacts of your work? 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] Available at 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 of GPUs, internal cluster, or cloud provider)? Appendix 4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators? Did you include any new assets either in the supplemental material or as a URL? [No] 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) Our method proposes to learn efficient data structure for accurate prediction in large-output space.

Do the main claims made in the abstract and introduction accurately reflect the paper's If you ran experiments (e.g. for benchmarks)... (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? [Y es] 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) E5-2698 v4 @ 2.20GHz) and each experiment is run for 5 seeds. 's if there are too few. A.4 Features The features of the ego object are: - The speed of the object. Road object features are: - The speed of the object.

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] See appendix B.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you discuss any potential negative societal impacts of your work? Did you state the full set of assumptions of all theoretical results? Did you include complete proofs of all theoretical results? 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] See the 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 believe policy reuse serves as a promising way to transfer knowledge among AI agents.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you describe the limitations of your work? 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) Therefore, an additional tuning process is needed to find an appropriate λ for different tasks. For better visualization, the scores are smoothed by a window with length 20. For better visualization, the scores are smoothed by a window with length 20.

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] See the 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 If your work uses existing assets, did you cite the creators? Did you include any new assets either in the supplemental material or as a URL? [No] 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) Thus Lemma 2.4 implies that ψ Lemma 2.2 implies that ψ That is nearly the same as the proof of Proposition 4.1, but replacing By lemma C.3 we know that with probability at least 1 α T, λ Inequality (2) is due to the mathematical induction using the same technique in the equality (1). To prove the problem-dependent bound, we need only combine Lemma C.1 and Lemma C.2 together Given Lemma D.2, we need only show that for both the private OLS estimator and the private SGD estimator, we can find the corresponding s Then Theorem 4.1 follows from combining Lemma D.2, D.3 and D.4 .Remark. Notice that in the statement of Lemma D.3 and Lemma D.4, there exists a term The proof of Lemma D.3 and Lemma D.4 needs the following result: For a fixed On the other hand, we have by Markov's inequality λ Now we can claim our first result about the private OLS-estimator in the warm up stage: Lemma D.9.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you discuss any potential negative societal impacts of your work? 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] Code can be 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? [No] 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) Code was written in PyTorch (Paszke et al., 2019), and hyperparameters for regular (non SMC) methods were selected through grid search over a validation fold of the training data where appropriate. In Thomson et al. (2009), there was no BMI information, so we The simulations used for evaluating the methods were provided to us by Uster et al. (2021), who However, the simulation setup they use is not based on the underlying assumption that we make. These methods rely on some level of experience in the environment (mostly from expert demonstrations), and the challenge is clearly harder if given just suggestions from an expert policy.

We first consider process samples by logistic regression with cluster centers as categorical variables . Intuitively, non-orthonormal centers correlate with each other, which means there is an overlap among categorical variables and makes it hard to identify the decision boundary that leads to a failed classification.