# Questions tagged [st.statistics]

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### Upper bound for VCdim of $H$ in terms of subgraph$(F)$, where $H := \{S(f) | f \in F\}$, with $S(f) := \{(x,y) \in X \times \{\pm 1\} | yf(x) \le 1\}$

$\DeclareMathOperator\sg{sg}\DeclareMathOperator\VCdim{VCdim}$ Let $X$ be a measurable space and given a measurable function $f:X \to \mathbb R$, recall that the subgraph of $f$, denoted $\sg(f)$ is ...
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### Differential privacy definition: subset of range of values vs. equals a value in the range

Consider only $\epsilon$-differential privacy. The textbook definition for this is: Definition 1: "A randomized algorithm $\mathcal{M}$ with domain $\mathbb{N}^{|\chi|}$ is $\epsilon$-...
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### An (unusual?) risk bound

I am told that that a bound on the generalization error of the following form exists in terms of something called the shattering coefficient" - but I am not able to reference this quantity in ...
1 vote
206 views

### Generalization bound for parameters rather than loss functions

I was wondering if it is possible to obtain high probability bounds (provided finite sample size of the training data) for the distance (say in the l-1 or l-2 norm) between the best parameter set and ...
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### 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 ...
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### 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
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### 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 ...
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### 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 ...
568 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 $G$ ...
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### 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 ...
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### 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 ...
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### 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 ...
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### 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
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### 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 ...
903 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) &...
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 ...