# Tag Info

12

BP and most of its variants are proved to converge on graphs without cycles. When you have cycles they show very strange behavior sometimes. For these cases people have tried different approximations schemes, for example Sherali-Adams, Lovasz-Schrijver, and Lasserre Hierarchies. See  for a comprehensive review of these approximations. Also (Wainwright ...

10

This fact is a corollary of a more general theorem. Let $\gamma_1,\dots, \gamma_{2n}$ be (jointly) Gaussian random variables; we don't assume that they are independent or identically distributed. Let $c_{ij} = {\mathbb E}[\gamma_i \gamma_j]$ be the covariance of $\gamma_i$ and $\gamma_j$. Consider the complete graph on $\{1, \dots, 2n\}$; assign weight $c_{... 9 I think it's a simple application of Hoeffding's inequality. Using your notation, let$Q_i = \frac1m C_i$, i.e.$Q$is the empirical distribution that approximates$P$. The total variation distance between$P$and$Q$, i.e. half the$\ell_1$distance, is $$\max_{S \subseteq [n]} \left| \sum_{i \in S}{P_i} - \sum_{i \in S}{Q_i}\right|.$$ Let$P(S):= \sum_{...

7

See Theorem 23 in Section 9.3 of Ryan O'Donnell's book Analysis of Boolean functions. Even though the theorem there is stated for $\pm 1$ variables, it holds for Gaussians as well (see Chapter 10 of the book for details on that). For the more general statement, see Exercise 7 in these lecture notes of Terry Tao.

5

Yuval Peres gave the answer in terms of the Kullback-Leibler divergence. Another way is to recall that the sample complexity will be captured by the inverse of the squared Hellinger distance between the two coins. Now, letting $D_p$ and $D_{p+\varepsilon}$ be the distributions of a Bernoulli random variable with parameter $p$ and $p+\varepsilon$ ...

5

Write $p=p_0=1-q$. We may assume that $\epsilon<\eta \le p\le 1/2$. Then the sample complexity is of order $\log(1/\delta)$ times the reciprocal of the relative entropy $D((p,q)||(p+\epsilon,q-\epsilon))$. This yields sample complexity $\Theta(p\epsilon^{-2}\log(1/\delta))$.

5

Using the relation between total variation and $L_1$/$\ell_1$ distance of the probability/distribution/mass functions, we have \begin{align} d_{\rm TV}(D_1, D_2) &= \frac{1}{2}\lVert D_1-D_2\rVert_1 = \frac{1}{2}\lVert \beta D_2 +(1-\beta)D_3 - D_2\rVert_1\\ &= \frac{1-\beta}{2}\lVert D_3 - D_2\rVert_1 = (1-\beta)d_{\rm TV}(D_2, D_3). \end{align}

5

Here is a different proof, adapted from the monograph The semicircle law, free random variables and entropy. Let $X_i$ be an infinite sequence of i.i.d. variables with distribution $\Pr[X_i = 1] = \Pr[X_i = -1] = 1/2$, so that $Y_n :=\frac{X_1+\cdots+X_n}{\sqrt{n}}$ converges in distribution to a standard Gaussian. We will show how to compute the limit of $... 5 What you are saying is that given$N$random samples one cannot simulate an algorithm that makes$T$queries to VSTAT$(N)$. If the$T$queries are chosen adaptively then one might need more samples (the best upper bound is$\sqrt{T} N$samples). This is true but not an issue for the planted clique paper you mentioned. That paper is concerned with proving a ... 5 Your construction does not work in general for the value of$k$given. Say$x = 0$and$y= (1, 0, \ldots, 0)$(or any other standard basis vector). Then$f(x) = 0$and$HDy$is a vector with$1 + \log_2 d$nonzero entries. We have $$Pr[f(y) = 0] = \left(1 - \frac{1 + \log_2 d}{d}\right)^k \approx 1-O\left(\frac{k\log d}{d}\right),$$ for$k \ll d$. Of ... 5 Here's a paper where the authors used BP to obtain a fully polynomial-time randomized approximation scheme for the the capacitated minimum-cost network ﬂow problem. 5 For distributions with finite support of size$d$, when the error metric is the$\ell_1$distance, the analogue of VC dimension is exactly$d$. (In fact, it's pretty much the VC dimension -- since to estimate a distribution over$d$in$\ell_1$is equivalent to agnostically PAC-learning the concept class$2^{[d]}$). For discrete distributions with infinite ... 4 Essentially, this follows from three facts: learning a Gaussian in total variation distance$\delta$is equivalent to learning its two parameters,$\mu,\Sigma$, to (respectively)$\ell_2$and relative Frobenius norms$O(\delta)$. (Since then the "empirical Gaussian" with the mean and covariance you estimated will be$\delta$-close to the true Gaussian). ... 4 If your function is$f:\mathbb{R}\to\mathbb{R}$, you can "learn"$f'$as a standard regression problem (linear, polynomial, etc.) and then recover$f$up to an additive constant by integrating$f'$. Obviously, you will only able to recover$f$up to an additive constant from the derivative. Update: As for error estimates, here's a simple one. Suppose you've ... 4$\chi^2$-divergence is not a Bregman divergence. I'll show it for sample size$n=1$. We would have $$(x-y)^2/x=f(x)-f(y)-f'(y)(x-y)$$ If$y=0$and$x>0$this says $$x=f(x)-f(0)-xf'(0),$$ $$1=\frac{f(x)-f(0)}x-f'(0).$$ Taking$x\to 0^+$this gives the contradiction$1=0$. 4 This is addressed in the following paper of Karp and Kleinberg: Karp, Richard M.; Kleinberg, Robert. Noisy binary search and its applications. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 881--890, ACM, New York, 2007. You can find a copy on Kleinberg's website. 4 This seems to be addressed in the following paper by Joseph P. Romano, Section 3  (specifically, Example 1): Example 1 (Finite versus not finite mean). Let$X$be$X_1, \dots, X_n$,$n$i.i.d. observations on the real line. As remarked by Bahadur & Savage (1956), "it would be interesting to know whether, in comparable non-parametric situations, ... 3 Well, we wrote a paper on it, so now it's definitely known: https://arxiv.org/abs/2010.09886 3 @odea, one can see that$\chi^2(P||Q) \leq c D(P||Q)$cannot hold in general by taking a two point space with$P = \{ 1 , 0\}$and$Q = \{ q, 1-q \}$. Then$\chi^2(P ; Q) = \frac 1 q -1$while$D(P||Q) = \log \frac 1 q$. Such a$c$would need to satisfy$c \geq \frac{x-1}{\log x}$for$x \to \infty$. However, if one assumes that$c=\| \frac{dP}{dQ} \|_\...

3

Your definition of $\chi^2$ divergence is missing a term; namely, $$\chi^2(P\|Q) = \int_{\mathcal{X}} dQ\left(\frac{dP}{dQ} - 1\right)^2 = \int_{\mathcal{X}} dQ\left(\frac{dP}{dQ}\right)^2 - 1$$ (see e.g. this Wikipedia article on $f$-divergences). With this in hand, recall that by concavity of the logarithm, we have \log x \leq x-1, \qquad \forall x &... 2 I seem to have resolved this question. The claim (on page 5 of this http://www.eccc.hpi-web.de/report/2015/063/ survey by Cannone) should have been that one can approximate a distribution to within \ell_2 distance \epsilon in O(\frac{n}{\epsilon^2}) samples (He does not mention approximate in what sense). This seems to follow directly from an ... 2 I agree with D.W. that this should just be a dynamic programming question. Assume that P_B is known and that we have a prior on P_A and that N is known. (Without a prior on P_A or a known N, I do not see how your objective or "optimal" are well-defined.) Let optimal mean "maximizes expected number of heads" with the expectation over both the coin ... 2 This can be done efficiently if the size of the samples S is not too large. Let m denote the maximum possible size of S. Then the following procedure outputs exactly the correct distribution: Draw a sample S (using the black box A). With probability |S|/m, keep S and go to step 2. Otherwise, go back to step 1 and draw a new sample. Choose ... 2 I tried to find a simple and accessible analysis of AIC. A definitive work seems to be Barron, Birgé, Massart, "Risk bounds for model selection via penalization" https://link.springer.com/article/10.1007/s004400050210 though I can't vouch for accessibility. Instead, let's analyze your sinusoids example. Suppose I have a parametric class of densities over [... 2 The related work I know of is experimental, but a similar setup has been called Learning from Label Proportions: https://link.springer.com/article/10.1007/s13278-017-0478-6 2 In Appendix B of [Ashtiani et al., Neurips 2018]. https://arxiv.org/pdf/1710.05209.pdf 2 Clément Canonne and I worked this out at some point. Let X_j be the number of realizations of j \in [d]. So \mathbb{E} X_j = np_j. \begin{align*} \mathbb{E} J_n^r &= \mathbb{E} \|\hat{P}_n - P \|_r^r \\ &= \frac{1}{n^r} \sum_{j=1}^d \mathbb{E} |X_j - \mathbb{E}X_j|^r \\ &\leq \frac{1}{n^r} \sum_{j=1}^d 3\mathbb{E} X_j & (*) ... 2 Before we try to get into ergodic or whatever else, let's try to understand what phenomenon a mathematician or scientist is trying to (or could be trying to) model with AEP. Well Asymptotic for very large n, a lot of coin flips, after a long time, etc ... Equipartition Equally distributed amongst some boxes or bins, Uniformly random, Equilibrium ... 2 I believe a common name for what you describe as \mathcal{N}(\mathcal{F},2n) is the "growth function". For a concept class \mathcal{F} = \{ h : X \to \{0,1\} \} and S = (x_1,\dots,x_n) \subseteq X^n we define \mathcal{F} \Big|_S = \{(h(x_1,\dots,h(x_n)) ~|~ h \in \mathcal{F}\}. Then, the "growth function" for $\mathcal{F}$ is: $... 1 In information theory notation, capital letters such as$X$denote random variables, and lowercase letters such as$x$mean their possible outcomes (i.e., fixed values). For example you can write the definition of expectation as$E[X] = \sum p(x) \cdot x$. Moreover, vector values are denoted using length as superscripts, for example$X^n$means$(X_1, \ldots,...

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