combining
- Europe > Denmark > Capital Region > Kongens Lyngby (0.14)
- North America > United States > Virginia (0.04)
- North America > Canada > Ontario > Toronto (0.04)
- Europe > Switzerland (0.04)
- North America > United States > Texas > Travis County > Austin (0.14)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Michigan > Washtenaw County > Ann Arbor (0.04)
- Asia > Middle East > Jordan (0.04)
Supplement to " Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections "
For the moment, it is worth noting that such sets of functions (e.g., Haar wavelets, Daubechies wavelets) are readily We are now in a position to present the main theorem of this subsection. To avoid repetition, we defer further discussions on the rates observed in Theorem A.1 to Remark 2.7 where a holistic In fact, by Proposition 1.1, there exists an optimal transport map Based on (B.2), the natural plug-in estimator of ρ Suppose that the same assumptions from Theorem 2.2 hold. B.2 Nonparametric independence testing: Optimal transport based Hilbert-Schmidt independence criterion Proposition B.2 shows that the test based on Further, when the sampling distribution is fixed, Proposition B.2 shows that In the following result (see Appendix C.2 for a proof), we show that if This section is devoted to proving our main results and is organized as follows: In Appendix C.1, we Further by Lemma D.2, we also have: ϕ Note that (C.10) immediately yields the following conclusions: S By (1.5) and some simple algebra, the following holds: null null null S Combining the above display with (C.9), we further have: null null null null 1 2 W Combining the above observation with Theorem 2.1, we have: lim sup For the next part, to simplify notation, let us begin with some notation. By using the exponential Markov's inequality coupled with the standard union Now by using [7, Theorem 2.10], we have P (B We are now in a position to complete the proof of Theorem 2.2 using steps I-III. Therefore, it is now enough to bound the right hand side of (C.17).
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Washington > King County > Seattle (0.04)
- North America > United States > Massachusetts > Suffolk County > Boston (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
Appendix of " Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent " A Preliminaries
In this section, we first summarize frequently used notations in the following table. Table 4: Frequently used notations.Notation Description C Lemma 7. Let d = 1 . Combining the cases above completes the proof. Subsection B.2 proves several convergence rate lemmas. Subsection B.3 gives some technical We are now ready to prove Theorem 1. Proof of Theorem 1.