Previousworksignore the distribution of sample difficulty and we argue that this potentially degrade their agent performance.

Moreover, we show more visualization results in experiments. To ensure a fair comparison, we used the fusion and optimization method as same as Latefusion. When k=1, it means that the object's physical properties are only related to itself, while As described in Section 3.1 in our paper, we represent audio Table 2: Performance comparison between our proposed DSE-audio and existing baseline methods. As shown in Table 2, we compare our method with other baseline methods. In Figure 6, we show a few additional examples of clustering using dynamic factors.

The task aims to infer objects'

However,inmanyreal-worldtasks,the learned function is intended to satisfy domain-specific constraints. We focus on monotonicity constraints, which are common and require that the function's output increases with increasing values of specific input features.

If different options were available to each group offour values, then more advantageous choices canbemade, asillustrated inFigure 1.

Importantly, E2 is bounded independently ofS.