Questions tagged [st.statistics]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
2
votes
1answer
74 views

Why Asymptotic Equipartition Property theorem proofs assume the source is memoryless?

I do not understand the assumption $X_1, X_2, \cdots$ are i.i.d. ~p(x) in the AEP proofs I have seen. I have read some different sources for understanding the Asymptotic Equipartition Property. Using ...
-1
votes
1answer
46 views

Notation in proof for Asymptotic Equipartition Property

In the following lecture notes chapter 3, page 12-13, they state the following We begin by introducting some important notation: - For a set $\mathcal{S},|\mathcal{S}|$ denotes its cardinality (...
0
votes
1answer
77 views

Volume of elements mapped to the same codeword is $2^{H(X|\hat{X})}$

In this paper by Tishby, Pereira and Bialek they mention on page 4 in the Relevant quantization chapter the setting is the following; Given some signal space $X \sim p(x)$ and a quantized codebook $\...
0
votes
0answers
17 views

ML classification with missing inputs using joint probability distribution

I am self-studying the deep learning book by Goodfellow, Bengio and Courville(https://www.deeplearningbook.org/contents/ml.html). On page 100, it has a brief introduction to classification with ...
2
votes
1answer
92 views

Why non-uniform learnability does not imply PAC learnability?

PAC guarantees provide us a a learning algorithm $A_n(\cdot)$ and sample complexity bound $n_{\mathcal{F}}(\epsilon,\sigma)$ that ensures $ P\left[L_P(A(\mathcal{D}^n))-L_P(f^*)\leq \epsilon\right]\...
0
votes
1answer
90 views

Notation of sequences in rate distortion theory

I have been reading whatever sources I could get my hands on today, regarding this problem. Most notes online about rate distortion theory come from the book Elements of Information Theory by Thomas ...
3
votes
1answer
126 views

Binary search on coin heads probability

Let $f:[0,1] \to [0,1]$ be a smooth, monotonically increasing function. I want to find the smallest $x$ such that $f(x) \ge 1/2$. If I had a way to compute $f(x)$ given $x$, I could simply use ...
4
votes
0answers
75 views

Strong data-processing inequality: bound $TV(T_{\#}P_0,T_{\#}P_1)$ if $\|T(x)-x\|_\infty \le \varepsilon;\forall x \in \mathbb R^p$

Disclaimer. I've moved this question from MO hoping that here is the right venue. Also, this is my first post on this channel, so please have some patience. So, Iet $X = (X,d)$ be a Polish space, ...
3
votes
0answers
166 views

Is this a known learning problem?

Let $(\mathcal{X},\rho)$ be a metric space (say, $\mathcal{X}=[0,1]$ with the Euclidean metric). Let $\alpha:\mathcal{X}\to[0,1]$ be unknown. Suppose that $\mathcal{X}$ is endowed with a distribution $...
2
votes
1answer
129 views

Is the Chi-square divergence a Bregman divergence?

Is the Chi-squared divergence $\sum_{i} \frac{(x(i)-y(i))^2}{x(i)}$ a Bregman divergence? I.e., can it be written as $\phi(x) - \phi(y) - \langle\phi'(y),x-y\rangle$? If so, what is the potential ...
4
votes
1answer
79 views

Terminology and references for a learning model

Let's say we're doing regression over $[0,1]^d$ -- either in the PAC sense with bounded-range agnostic noise or in the more classical-statistics sense with additive Gaussian noise. Suppose further ...
1
vote
0answers
46 views

Average smoothness learning rates

This question is somewhat related to this one. There are many results in statistics where convergence rates (including minimax ones) are given in terms of the smoothness properties of the underlying ...
0
votes
0answers
79 views

Expected value of a random experiment in a graph

I need to find the expected value of R in the random experiment below. $$ R = \frac{1}{K} \sum_{C \in \mathcal{H} } \ [\frac{1}{2} |V(C)| * (|V(C)| - 1) - |C|] $$ $\mathcal{H}$ is a partition on ...
2
votes
2answers
299 views

About learning a single Gaussian in total-variation distance

I am looking for the proof of this following result which I saw as being claimed as a "folklore" in a paper. It would be helpful if someone can share a reference where this has been shown! Let $G$ ...
2
votes
0answers
82 views

Lower bounds for SRM?

This question is about structural risk minimization and model selection. Let $H_n$ be the collection of all binary classifiers on some fixed set with an $n$-bit description length in some fixed ...
2
votes
1answer
105 views

Sample Complexity for Order Statistics

I have a sample complexity question which seems fairly basic, but for which I'm having trouble finding a reference. Let $F$ be an unknown distribution over $[0,1]$. Denote by $X_{k:n}$ the $k$th of $...
1
vote
0answers
77 views

Average margin bounds for separable SVM

Suppose we're training a linear separator in the realizable PAC setting. Given $m$ labeled examples $(x_i,y_i)$ in $\mathbb R^d\times\{-1,1\}$, a (consistent) linear separator is a vector $w\in\mathbb ...
0
votes
0answers
45 views

Function that maps non-linear distribution to normal distribution while maintaining distance

I have a collection $X$ of 10 million $(x,y,z)$ 3-tuples, where $x$, $y$, and $z$ are all numbers between 0 and 1. The distribution of $x$, $y$, and $z$ values are complex, and the distributions of $...
3
votes
2answers
205 views

How can AIC converge in the limit when even 2 parameter models can have infinite VC dimension?

AIC-based model-selection converges to zero error in the limit, and also has finite-sample convergence that is rate-optimal with respect to worst case minimax error [1]. (Note that AIC refers to ...
4
votes
1answer
92 views

Design a sampling process to select an element with probability proportional to its appear probability in a simulation

We are given a black box $A$ that can do a simulation. Each time running box A gives a sample $S \in 2^X$ where $X$ is a finite ground set. Let $\Pr[x]$ be the probability that $x \in X$ appears in ...
1
vote
0answers
66 views

To what extent supervised learning ERM learn first-order knowledge

Suppose I have a collection of (hidden) first-order rules: $$ \mathcal{R}: \{ Q_i(x) => P_i(x) \}_{i=1}^{k} $$ all defined over $x \in \mathcal{X}$. I can use these rules and (automatically) ...
-1
votes
1answer
150 views

Application of the inequality with expectations

Let $\Vert\cdot\Vert$ is a norm in $R^n$. Let $x_1,\dots,x_N$ non-independent Rademacher random variables random variables (variables which are uniform on $\{-1, 1\}$). . By $E$ we denote an ...
0
votes
1answer
74 views

Learning a discrete distribution in $\ell_r$ norm

Let $P=(p_1,\ldots,p_d)$ be a distribution on $[d]$. Given $n$ iid draws from $P$, we construct some empirical estimate $\hat P_n=(\hat p_{n,1},\ldots,\hat p_{n,d})$. Let us define the $r$-risk by $$ ...
13
votes
0answers
251 views

Differential privacy and data poisoning

A differentially private algorithm takes datasets containing inputs and produces randomized outputs, such that no small change in the dataset can shift the distribution of outputs by too much. This ...
4
votes
2answers
429 views

Learning a coin's bias (localized)

It's well known that the minimax sample complexity for estimating the bias $p$ of a coin to additive error $\epsilon$ with confidence $\delta$ is $\Theta(\epsilon^{-2}\log(1/\delta))$. What if we ...
-1
votes
1answer
154 views

L1 / Variational Distance between distributions [closed]

My statistics knowledge is somewhat poor, so I have to ask one (dumb) question. Let $\beta$ be a real number in the interval $\big[0, \frac{1}{2}\big)$ and $\mathcal{D}_1, \mathcal{D}_2, \mathcal{D}...
7
votes
2answers
268 views

What is the connection between moments of Gaussians and perfect matchings of graphs?

Today, I heard the following statement in a talk: The 4th moment of a $1$-dimensional Gaussian distribution with mean $0$ and variance $1$ is the same as the number of perfect matchings of a ...
2
votes
1answer
156 views

Learning from derivative data

In many machine learning algorithm, it is often assumed that outputs of unknown function and their corresponding inputs are given to estimate the unknown function. However, I wonder whether there ...
2
votes
0answers
68 views

Impossibility result on metric learning?

Are there any fundamental limitations (impossibility results) known for metric learning? Are there any direct connection reduction from/to that I can use results in clustering? (e.g. this: 2 ) 2 ...
1
vote
0answers
138 views

Maximal correlation vs correlation coefficient when one RV is Gaussian

Last week I asked a question on MOF (see here), but I got no reply. So I am asking my question here. Let a pair of random variables $(X,Y)$ be continuous random variables (i.e., they both have ...
8
votes
1answer
646 views

Exponential Concentration Inequality for Higher-order moments of Gaussian Random Variables

Let $X_1,\ldots, X_n$ be $n$ i.i.d. copies of Gaussian random variable $X \sim N(0, \sigma^2)$. It is known that \begin{align} \mathbb{P}\Bigl( \Bigl|\frac{1}{n}\sum_{j=1}^n X_j \Bigl| >t\Bigr) &...
2
votes
1answer
141 views

Tolerance parameter of statistical query model and adaptivity

It seems that the reasonable assumption for the tolerance parameter of statistical query model is roughly $1/\sqrt{n}$, which is obtained from concentration inequalities (see, e.g., Definition 2.3 of ...
4
votes
3answers
210 views

Approximating distributions from samples

One claim I find in many papers about identity testing, and closeness testing is that any distribution over $[n]$ can be approximated to within $\ell_1$ distance $\epsilon$ in $O\left(\frac{n}{\...
0
votes
0answers
46 views

Establishing causality under conditions of certainty

I'm currently reading "Causality: Models, Reasoning, and Inference" by Judea Pearl. Early on, he states that the development assumes that there are no certain entailments, no 1 or 0 probabilities -- ...
3
votes
2answers
986 views

An upper bound for chi-square divergence in terms of KL divergence for general alphabets

In my research I need an upper bound for chi-square divergence in terms KL divergence which works for general alphabets. To make this precise, note that for two probability measures $P$ and $Q$ ...
1
vote
0answers
117 views

Orlicz Norm and a result on expectation

I am reading a paper which is mainly about Dobrushin's contraction coefficient and its generalization. In page 27, the following is defined: Consider an arbitrary, non-negative, convex function $\psi:\...
3
votes
0answers
73 views

One kind of dependence relation between a pair of random variables

I have been working on privacy and come across a neat problem. Suppose two random variables $X$ and $Y$, over finite alphabets $\mathcal{X}$ and $\mathcal{Y}$, are given with joint distribution $P_{...
2
votes
0answers
126 views

Strong Dependence

I asked this question on MO, but no answer. I don't know if this definition has been already given. Suppose $X$ and $Y$ are two random variables over finite alphabets $\mathcal{X}$ and $\mathcal{Y}$...
7
votes
2answers
264 views

Maximizing the number of heads in $N$ tosses by choosing which coin to toss

Assume you have two coins $A,B$ with biases $P_A,P_B$ respectively. We would like to make $N$ coin tosses and get the maximal number of heads possible. Unfortunately, we know $P_B$, but $P_A$ is ...
1
vote
0answers
276 views

What's the meaning of the class indicator matrix when transforming the class label matrix into it in canonical correlation analysis?

When using canonical correlation analysis (CCA), we can integrate the dataset and label information via transforming the class label matrix Y into the class indicator matrix T. Such as: $T = (YY^T)^½Y$...
7
votes
1answer
310 views

Proof Haar matrices satisfy JL lemma

The Johnson-Lindenstrauss lemma says roughly that for any collection $S$ of $n$ points in $\mathbb{R}^d$, there exists a linear map $f:\mathbb{R}^d \rightarrow \mathbb{R}^k$ where $k = O(\log n/\...
4
votes
0answers
532 views

Practical Implications of Kolmogorov's Result on the Universal Approximation Theorem with Neural Networks

After having read matus's beautiful answer in this thread explaining (among other things) Kolmogorov's result regarding the Universal Approximation Theorem with Neural Networks, I wonder: if just $\...
2
votes
0answers
197 views

Expected probability of error in Vapnik's book

In Vapnik's book "Statistical Learning Theory", Theorem 10.5 states that - for a Support Vector Machine - the expected probability of error (of the optimal hyperplane) is upper bounded by $1/(l+1)$ ...
14
votes
2answers
337 views

Theoretical guarantees for running times of belief propagation methods?

Belief propagation has been shown to be a very powerful method through research in probabilistic graphical models. However, I don't know anything about BP that's comparable to MCMC methods where we ...
11
votes
4answers
575 views

Lower bound for testing closeness in $L_2$ norm?

I was wondering if there was any lower bound (in terms of sample complexity) known for the following problem: Given sample oracle access to two unknown distributions $D_1$, $D_2$ on $\{1,\dots,n\}$, ...
5
votes
1answer
1k views

What happens if you minimize $D_{KL}(P_{parameters} || P_{data})$ under the Kullback-Leibler divergence?

If $D_{KL}$ is the Kullback-Leibler divergence, minimizing $D_{KL}(P_{data}||P_{parameters})$ performs maximum likelihood estimation of the parameters. What happens if you minimize $D_{KL}(P_{...
1
vote
1answer
199 views

How to choose a correct prior

Consider a Bernoulli experiment, such as flipping a not necessarily fair coin, which results in a positive outcome (heads) with probability $p$ and with a negative outcome (tails) with probability $(1-...
0
votes
3answers
673 views

Algorithm Suggestion for Scoring System - weighted solution

I'm trying to validate a series of words that are provided by users. I'm trying to come up with a scoring system that will determine the likelihood that the series of words are indeed valid words. ...
7
votes
1answer
859 views

Streaming Algorithms: Motivations for estimating frequency moments

The celebrated AMS paper "The space complexity of approximating the frequency moments" defines the problem as following: Let $a_1, a_2,\dotsc, a_m$ be a sequence of integers where each $a_j \in \{1,2,...
2
votes
0answers
38 views

Lower bounds for minimum variance estimators in limited space

Cramer-Rao, Rao-Blackwell and Lehmann-Scheffé, all give you ways to prove that a statistical estimator has the lowest variance possible. Is there any CS related work on the minimum variance ...