We thank all the reviewers for their careful reviews. None of these follow trivially from Xu et al. In molecule generation, validity is encouraged using the reward-based approach of MolGANs, not the SL. As a result diversity increases (91% to 98.98%) and so does uniqueness (2.4 to 2.5). We will clarify this in the discussion.

In collective decision making, members of a group need to coordinate their actions in order to achieve a desirable outcome. When there is no direct communication between group members, one must decide based on inferring others' intentions from their actions. The inference of others' intentions is called "theory of mind" and can involve different levels of reasoning, from a single inference of a hidden variable to considering others partially or fully optimal and reasoning about their actions conditioned on one's own actions (levels of "theory of mind"). In this paper, we present a new Bayesian theory of collective decision making based on a simple yet most commonly observed behavior: conformity. We show that such a Bayesian framework allows one to achieve any level of theory of mind in collective decision making. The viability of our framework is demonstrated on two different experiments, a consensus task with 120 subjects and a volunteer's dilemma task with 29 subjects, each with multiple conditions.

A.1 GAN Implementation For the training of the GANs we have used conditional generative network, with three hidden layers of size 100 and conditional discriminative network with two hidden layers of size 100. For all the hidden layers except the last one we have used tanh activation. For the last hidden layer leaky_relu was used. The conditioning is performed via concatenating the input noise z with input parameters ψ. The learning rate and batch size is set to 0.0008 and 512 correspondingly.

We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the simulator is stochastic. To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space. We demonstrate that these local surrogates can be used to approximate the gradient of the simulator, and thus enable gradient-based optimization of simulator parameters. In cases where the dependence of the simulator on the parameter space is constrained to a low dimensional submanifold, we observe that our method attains minima faster than baseline methods, including Bayesian optimization, numerical optimization, and approaches using score function gradient estimators.

We thank the reviewers for their thoughtful comments! We agree that we might have been under-selling scalability of our method (R3) w.r.t. We are glad the text is clearly written (R2, R4) with a broad literature review (R1, R2). We note this on lines [146-149]. Similarly, for visual clarity, error bands were not included for all baselines in Figure 1.

We analyze the global convergence of gradient descent for deep linear residual networks by proposing a new initialization: zero-asymmetric (ZAS) initialization. It is motivated by avoiding stable manifolds of saddle points.

We are grateful for all the reviewers' valuable suggestions and questions. The results are displayed in Figure 1. We can see that mZAS initialization always outperforms the Xavier initialization. ICLR2019), but with the top layer to be zero. We will clarify this in the revised version.

Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions.

We study the problem of precisely swapping objects in videos, with a focus on those interacted with by hands, given one user-provided reference object image. Despite the great advancements that diffusion models have made in video editing recently, these models often fall short in handling the intricacies of hand-object interactions (HOI), failing to produce realistic edits--especially when object swapping results in object shape or functionality changes. To bridge this gap, we present HOI-Swap, a novel diffusion-based video editing framework trained in a self-supervised manner. Designed in two stages, the first stage focuses on object swapping in a single frame with HOI awareness; the model learns to adjust the interaction patterns, such as the hand grasp, based on changes in the object's properties. The second stage extends the single-frame edit across the entire sequence; we achieve controllable motion alignment with the original video by: (1) warping a new sequence from the stage-I edited frame based on sampled motion points and (2) conditioning video generation on the warped sequence. Comprehensive qualitative and quantitative evaluations demonstrate that HOI-Swap significantly outperforms existing methods, delivering high-quality video edits with realistic HOIs.

Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving efficient communication among agents has always been an overarching problem in MARL. In this work, we propose Variance Based Control (VBC), a simple yet efficient technique to improve communication efficiency in MARL. By limiting the variance of the exchanged messages between agents during the training phase, the noisy component in the messages can be eliminated effectively, while the useful part can be preserved and utilized by the agents for better performance. Our evaluation using multiple MARL benchmarks indicates that our method achieves 2 10 lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to achieve better overall performance.