To Reviewer 1: [Intuition of benefits of advanced data augmentation] In line 198, we explained the theoretical3 connection between advanced data augmentation and better semi-supervised learning performance. We stated that4 "Importantly, the number of components is actually decided by the quality of the augmentation operation: an ideal5 augmentation should be able to reach all other examples of the same category given a starting instance.

Based on their suggestions, we benchmark our2 method against additional non-deep-learning, state-of-the-art baselines.

Our explanations help users more accurately reject AI'swrong decisions than all other tested methods.

WheneverMSTandδ-MBST19 have throughput close to RING, they achieve faster training, as they have better spectral properties. Comparison with MATCHA (Review #2) The reviewer is right that MATCHA [99] selects more frequently the25 important links.

Search issound, because all nodes are eventually expanded, and path costs updated. While all the evaluated39 domains were deterministic, our framework also supports Probabilistic PDDL [5], and can be readily extended to40 stochastic domains, e.g. Our use ofareplanning model also means that itisnot41 as strongly tied to deterministic environments as R3 contends: ifthe agent encounters astate that itdid not plan for42 (e.g.

This is the state-of-the-art graph contrastive learning based recommendation method, which proposes randomly node dropout, edge dropout, and random walk for augmentation onthebipartite graph.

The out-side lab doesn't7 have access to in-hospital's private labels, but it could still initiate and provide assistance to the in-hospital lab by8 fitting the received residuals instead of public labels. An example case is where each participant holds a disjoint subset of the features and uses linear regression. Wealso did extensiveexperimentsin the last few days to provide empirical evidence (summarized in18 Table1). Each participant needs to hold and distribute identifiers for data23 items, so that the data from different participants can be conceptually combined. Theprobabilityofchoosing34 the optimum could be theoretically derived from large-deviation bounds (under certain assumptions).

DCBO is useful in scenarios where all causal effects in a graph are changing over time.

Wefirst implement global augmentation approaches including random rotation and random scaling on two LiDAR scans separately and thenconcatenate themfortraining. The more copies the better segmentation performance as shown in ' 1, 2, 3' in the table, which indicates the effectiveness of the approach in enriching data distribution. In this section, we conducted experiments to analyze how PolarMix benefits LiDAR point cloud learning. As a comparison, PolarMix is more robust to the instance spatial location without much performance drop. PolarMix improves the robustness of the baseline clearly with respect to the angular variations of instances (i.e.

We randomly selected action sequences from test episodes collected with action noise alongside the training episodes. Next, we analyze the IDM framework based on Janner's work [1]. Denote pθ(z |z,a) as the state transition probability predicted by model.