Questions tagged [sample-complexity]

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Is there a relation between packing number and disagreement coefficient in the active learning setting?

This is a question for active learning experts: Let $\mathcal{X}$ be the input space equipped with a distribution $\mathcal{D}$ and let $\mathcal{H}$ be a hypothesis class, $h \in \mathcal{H}$ our ...
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Is it possible to estimate the positive outcomes of a boolean function using an optimized version of Goldreich-Levin?

Let $\mathcal{X} = \{-1,1\}^n$ and $h: \mathcal{X} \to \{-1,1\}$, h can be expanded in the basis of monomials for the uniform distribution, or also can have a distribution free expansion (Gram-Schmidt ...
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definition of P-samplable distribution that allows non-binary fractions

Arora and Barak (in chapter 18, on average-case complexity) define a polynomial-time samplable (or P-samplable) distribution $D$ (actually a family $\{D_n\}$, for each output length $n$) as having an ...
Shivaram Lingamneni's user avatar
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what are some Lower bound for finding large fourier coefficients of boolean function (above a threshold)?

Is there some known lower bounds for estimating large fourier coefficients of boolean functions? And were there any comparison of tightness with the upper bound of Goldreich Levin algorithm?
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Does Goldreich-Levin algorithm for finding large Fourier coefficients have time complexity upper bound = sample complexity upper bound?

I'm currently working on finding better bounds for Goldreich-Levin algorithm for estimating large Fourier coefficients of a boolean function. I was surprised seeing that the upper bounds for time ...
rivana's user avatar
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1 answer
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Unable to understand the Sample complexity of PAC learning

I have been studying from the book "Understanding Machine Learning - From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David I am struck at corollary 3.2 which states that Every ...
Sathishkumar Thirumalai's user avatar
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49 views

Is there an efficient Goldreich-Levin algorithm that generalizes to agnostic PAC setting?

Goldreich Levin algorithm is an algorithm that based on some assumption (boundness on Fourier coefficients) outputs the indices for most significant Fourier coefficients of a boolean function, however ...
rivana's user avatar
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Faster algorithm for sampling unifromly at random

The goal is to come up the simple data structure for sampling a uniform point from a collection of sets, i.e., given a sub-collection $\mathcal{B}$, sample a point in $\cup \mathcal{B}$ uniformly at ...
Shi's user avatar
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Relationship between statistical query lower bounds and "traditional" iid sampling lower bounds

Coming from a more statistical background, it is not clear to me if or how lower bounds in the statistical query (SQ) model imply anything useful about traditional learning problems with iid samples (...
student3365's user avatar
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PAC guarantees for linear prediction under the squared loss

I am looking for generalisation bounds under the squared loss, specifically for the class $\mathcal{F}_{\text{lin}} = \{f(x) = \langle w, x \rangle : \|w\| \leq C\}$ of bounded linear predictors. I am ...
Nick Bishop's user avatar
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Sample complexity lower bound to learn the mode (the value with the highest probability) of a distribution with finite support

Say we have a black-box access to a distribution $\mathcal{D}$ with finite support $\{1,2,...,n\}$ with probability mass function $i \mapsto p_i$. How many samples of $\mathcal{D}$ are needed to learn ...
actcon's user avatar
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Lower bound for the OR problem

Let us have booleans $x_1, \cdots, x_n$. Any algorithm that determines $\bigvee_1^n x_i$ with probability at least $2/3$ requires $\Omega(n)$ time. It is not too difficult to prove this, but the proof ...
user2316602's user avatar
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understanding generalized coupon collector for distributions or learning mixture of distribution

Lets suppose we have a set $S=\{1,\ldots,n\}$ and $P$ is the uniform distribution over two subsets $T_1,T_2\subseteq S$, each of size $m\leq n/100$. Now, suppose somehow is given uniform samples from ...
Annonymous's user avatar
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Understanding Dudley Chaining Argument for Rademacher Bound

I follow the proof of the Dudley chaining/metric entropy bound of the (empirical) Rademacher complexity, but I don't have any intuition for why this bound should be true. In particular, I don't know ...
user27182's user avatar
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Qubit gates in google supremacy

The gates in quantum supremacy experiment are nearest-neighbor and have spatial locality. Would this additional information help bolster IBM's argument to perhaps simulate quantum supremacy experiment ...
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representation of concept classes and pac learning

I was reading the book of Kearns and Vazirani and I didn't completely understand the following: Let C be a concept class and suppose we want to PAC learn C, they say first consider a larger ...
Annonymous's user avatar
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How many samples are needed to reconstruct a path?

Consider an input set of vertices $V$ and vertices $s,t\in V$. The goal is to learn some unknown shortest path from $s$ to $t$; the set of edges of the graph is hidden at first and there may be ...
R B's user avatar
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7 votes
1 answer
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Sampling monotone Boolean functions

I'm interested in sampling monotone increasing Boolean functions on $n$ input bits uniformly at random. I understand that this is equivalent to approximating the Dedekind numbers ($D_n = $ the number ...
Samuel Schlesinger's user avatar
10 votes
3 answers
668 views

Is uniform convergence faster for low-entropy distributions?

Let $\mathcal D$ be a probability distribution on $\{0,1\}^d$. Let $X_1, \cdots, X_n \in \{0,1\}^d$ be i.i.d. samples from $\mathcal D$. Let $\mu \in [0,1]^d$ be the mean of $\mathcal D$ and let $\...
Thomas's user avatar
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1 answer
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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 $...
Lemke's user avatar
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2 votes
1 answer
140 views

Sample complexity for learning Boltzmann Distribution parameters

I am trying to think through the number of samples that I would need to estimate the parameters of a Boltzmann partition function to a desirable precision. Suppose that there are $N$ possible states ...
Asterix's user avatar
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8 votes
0 answers
276 views

How to sample a lot of independent uniform spanning trees?

There are a bunch of good algorithms for sampling a uniform spanning tree from a graph $G$. For example, Aldous/Broder and Wilson's algorithm are pretty efficient. However, each of these graphs only ...
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1 vote
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Complexity of Proportional Sampling

Let $p_1,...,p_n$ be a list of numbers, each specified by $n^{O(1)}$ bits. Let $\mu = \sum_{i} p_i$ be the sum of all numbers in the list. I want to sample from the set $\{1,...,n\}$ where each $j$ is ...
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4 votes
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Sampling Functions Efficiently vs Pseudorandom Generators

Let $X$ be a set of $n$-bit Boolean functions of the form $f:\{0,1\}^n\rightarrow \{0,1\}$. For instance, $X$ could be the set of $n$-bit monotone Boolean functions, or the set of $n$-bit functions ...
verifying's user avatar
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4 votes
1 answer
200 views

How well do subspaces hit sets

Let $S\subset F_2^n$ be a subset of size $\epsilon\cdot 2^n$. Say I choose a random subspace $V$ of dimension $k$ in $F_2^n$. I want to know what is the smallest $k$ such that $V$ `hits' $S$, i.e., $V\...
relG's user avatar
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2 votes
0 answers
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About lower bounding the sample complexity of a distribution

Given a joint probability distribution over a finite number of random variables (each with a finite range space) of which only a certain subset is observable, is there a notion of "sample complexity" ...
Student's user avatar
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1 vote
0 answers
310 views

How does one sample uniformly at random from an uncountably infinite set?

I want to know if there are any examples of polynomial time algorithms which can sample uniformly at random from a given uncountably infinite set? (assuming it is possible) Does it help if the sample ...
Student's user avatar
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2 votes
2 answers
383 views

Coreset and VC dimension

I am trying to understand the notion of $\epsilon$-coreset and its relation with sampling bounds of a range space having a finite VC-dimension. Although both of them give an $\epsilon$-approximation ...
Ram's user avatar
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2 votes
1 answer
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Evaluating the expected value of negatively correlated random variables

A polynomial random process satisfying the following properties converts a fractional point $(x_1, x_2, \ldots, x_n) \in \mathcal{P}$, $(x_i \in [0,1])$ to a random integer point $(X_1, X_2, \ldots, ...
salmAn's user avatar
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5 votes
1 answer
931 views

Sample complexity of distinguishing two Gaussian distributions?

Below is a description of the problem: Suppose I have two $p$-dimensional Gaussian distributions with the same covariance matrix $\Sigma$ and means $\mu_1$, $\mu_0$. And I can get $n$ samples $X_1^{(...
Tianyang Li's user avatar
3 votes
1 answer
206 views

Almost uniform sampling implies approximate counting

I began studying papers about approximate counting and I keep seeing the above being quoted, without further explanation. I suppose the procedure that yields the result is very well known and that is ...
Paramar's user avatar
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2 votes
1 answer
228 views

Determining the number of clusters using property testing algorithm

We say a set of $n$ points in $R^d$ are $k$-clusterable, if all points are covered by k unit balls. We have a property testing algorithm (see section 5 of paper) which consider a promise version of ...
Ram's user avatar
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2 votes
1 answer
103 views

Approximating the value of k in $k$-mean clustering problem

Consider a set of $n$ points in $R^d$ which are covered by some finitely many (say $k$) unit balls. Can we approximate the value of $k$ by querying only sublinear many points. More precisely, by ...
Ram's user avatar
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11 votes
1 answer
286 views

Given $f:\{0,1\}^n \rightarrow \{-1,1\}$, find a subcube with large volume and large average value

Here is a problem with a similar flavor to learning juntas: Input: A function $f: \{0,1\}^n \rightarrow \{-1,1\}$, represented by a membership oracle, i.e. an oracle that given $x$, returns $f(x)$. ...
greg's user avatar
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24 votes
1 answer
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Sampling satisfiable 3-SAT formulas

Consider the following computational task: We want to sample a 3-SAT formula of $n$ variables (a variant: $n$ variables $m$ clauses) with respect to the uniform probability distribution, conditioned ...
Gil Kalai's user avatar
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1 vote
2 answers
419 views

algorithms to split data into roughly equal sized quantiles

What is the state-of-the art on algorithms that calculate/estimate approximate quantiles? I don't even worry about errors in terms of the value of quantiles (here meaning the cutoff) but having ...
László's user avatar
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17 votes
1 answer
1k views

The complexity of sampling (approximately) the Fourier transform of a Boolean function

One thing that quantum computers can do (possibly even with just BPP + log-depth quantum circuits) is to approximate-sample the Fourier transform of a Boolean $\pm 1$-valued function in P. Here and ...
Gil Kalai's user avatar
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5 votes
1 answer
320 views

VC-Dimension and sample complexity dependent on size of subsets

I have a range space $(X,R)$, were $R$ is a collection of subsets of $R$ and I have an upper bound $d$ to the VC-dimension of $(X,R)$. Suppose for simplicity that $X$ is finite. Given $\delta\in(0,1)$ ...
Matteo's user avatar
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9 votes
1 answer
416 views

What is the proper role of verification in quantum sampling, simulation, and extended-Church-Turing (E-C-T) testing?

Since no answer was given, a flag has been set requesting that this question be converted to a community wiki. The comments by Aaron Sterling, Sasho Nikolov, and Vor have been synthesized into the ...
6 votes
1 answer
674 views

Efficiently Samplable Distributions

What does it mean for a distribution to be efficiently samplable? This came up in the discussions about the distributions used in the recent attempted P!=NP proof. The context was that a ...
Jeff's user avatar
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12 votes
2 answers
399 views

Computational query complexity of SQ-learning

It is known that for PAC learning, there are natural concept classes (e.g. subsets of decision lists) for which there are polynomial gaps between the sample complexity needed for information theoretic ...
Aaron Roth's user avatar
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