A.1 Proof of Proposition 1 We note that 1 is restated and was proved in [25, Appendix A.1] Proof of 2: Non-negativity directly follows by non-negativity of mutual information. Proof of 5: The proof relies on the independence of functions of independent random variables. This concludes the proof. 1 A.2 Proof of Proposition 2 By translation invariance of mutual information, we may assume w.l.o.g. that the means are Next, we show that we may equivalently optimize with the added unit variance constraint. Example 3.4]), we have I (A B) null, where the last equality uses the unit variance property and Schur's determinant formula. Armed with Lemma 1, we are in place to prove Proposition 2. Since the CCA solutions Theorem 2.2], which is restated next for completeness.

This work proposes a middle ground in the form of a scalable information-theoretic generalization of CCA, termed max-sliced mutual information (mSMI).

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks.

DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs.

As such, it is often desired to select a small number of informative features to consider in subsequent analyses.

Data coming from different sensors often capture information related to common latent factors.