Study on Vapnik-Chervonenkis dimension of product intervals in R^d.
problem Combinatorial complexity of product intervals in R^d.
method Vapnik-Chervonenkis geometry approach.
result Vapnik-Chervonenkis dimension of balls in ℓ∞^d equals (3d+1)/2.
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48 results for “Vapnik Chervonenkis Theory”
The paper provides bounds for regression schemes using nonstationary training samples.
problem Developing confidence intervals for nonparametric regression with nonstationary data.
method The approach involves Rademacher and Vapnik-Chervonenkis theories to analyze the cost and optimality of regression schemes.
result The paper establishes nonasymptotic bounds for regression schemes and optimality in -distance.
The Vapnik-Chervonenkis (VC) dimension of a collection of subsets of a set is an important combinatorial concept in settings such as discrete geometry and machine learning. In this paper we prove that the VC dimension of the family of -dimensional cubes in is .
For any family of measurable sets in a probability space, we show that either (i) the family has infinite Vapnik-Chervonenkis (VC) dimension or (ii) for every epsilon > 0 there is a finite partition pi such the pi-boundary of each set has measure at most epsilon. Immediate corollaries include the fact that a family wit…
Improves conformal prediction by combining multiple score functions and optimizing weights.
problem Limitations of single-score conformal predictors in multi-class classification.
method Combines multiple score functions and optimizes weights to minimize prediction set size.
result Consistently outperforms single-score conformal predictors while maintaining valid coverage.
In this dissertation, I derive a new method to estimate the Vapnik-Chervonenkis Dimension (VCD) for the class of linear functions. This method is inspired by the technique developed by Vapnik et al. Vapnik et al. (1994). My contribution rests on the approximation of the expected maximum difference between two empirical…
Paper extends nonparametric regression bounds for dependent -mixing samples.
problem Analyzing error in nonparametric regression with dependent data.
method Extends uniform deviation inequalities from independent to dependent -mixing samples.
result Derives generalization bounds for nonparametric regression with dependent data.
The paper tackles extrapolation in extreme regions of regression problems.
problem Extrapolation on the tails of covariates in continuous regression problems.
method Statistical regression on a subsample of furthest observations, focusing on their angular components, using multivariate regular variation theory.
result Quantifies predictive performance on tail regions in terms of excess risk, presenting it as a finite sample risk bound with a bias-variance decomposition.
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistencystat.ML
This paper provides statistical guarantees for WAE's latent space regeneration.
problem Lack of statistical analysis for Autoencoders, especially WAE.
method Utilizes Vapnik Chervonenkis (VC) theory and Optimal Transport of measures under the Wasserstein metric.
result WAE achieves the target distribution in the latent space and regenerates the input distribution.
Optimal sample complexity for contrastive learning of distances.
problem Minimum labeled tuples needed for high accuracy in learning distances.
method Analyzes sample complexity in various distance settings, proving tight bounds.
result Almost optimal bound on sample complexity for learning distances.
Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the generalization capacity of learning algorithms. However, apart from a few special cases, it is hard or impossible to calculate analytically. Vapnik et al. [10] proposed a technique for estimating the VC dimension empirically. While their approach behave…
The paper provides risk bounds for learning many response functions using linear regression.
problem Learning many response functions from a single dataset.
method Ordinary least squares regression in a high-dimensional feature space.
result Convergence guarantees on worst-case excess prediction risk for infinite response functions with finite VC dimension.
In this paper, we present a Longstaff-Schwartz-type algorithm for optimal stopping time problems based on the Brownian motion filtration. The algorithm is based on Leão, Ohashi and Russo and, in contrast to previous works, our methodology applies to optimal stopping problems for fully non-Markovian and non-semimartinga…
Linear classifiers in product space forms improve scRNA-seq data classification.
problem Linear classification in products of Euclidean, spherical, and hyperbolic spaces.
method Novel formulations of linear classifiers on Riemannian manifolds, proving expressive power, and formalizing perceptron and SVM classifiers.
result Linear classifiers in product space forms have the same expressive power as in Euclidean space of the same dimension.
Deep Heaviside networks are limited but can be improved with connections or linear neurons.
problem Limited expressivity of deep Heaviside networks.
method Including skip connections or linear activation neurons improves expressivity.
result Lower and upper bounds for VC dimensions and approximation rates are derived.
Rademacher complexity and spin glasses: A link between the replica and statistical theories of learningcond-mat.dis-nn
Statistical learning theory connects to spin glass models via Rademacher complexity and replica theory.
problem Bounding generalization gap in statistical learning theory.
method Linking Rademacher complexity in statistical learning to synthetic models in statistical physics.
result Rademacher complexity is closely related to ground state energy in spin glass models.
This research sets limits on how complex multi-class learning problems can be.
problem Understanding the complexity of multi-class classification problems.
method Established upper bounds on Natarajan dimensions for specific function classes.
result Upper bounds on Natarajan dimensions for multi-class decision trees, random forests, and neural networks.
Study tests whether trade-off functions are above or below benchmarks using finite samples.
problem Testing trade-off functions between unknown distributions.
method Identifies a condition for nontrivial testing, constructs a test with error guarantees, and inverts the test for confidence bands.
result Finite-sample testing is possible under specific structural assumptions about rejection regions.
Paper shows SVM can achieve super fast convergence rates.
problem Understanding fast convergence rates for SVM.
method Presented a simple mechanism to obtain fast convergence rates for SVM.
result SVM can exhibit exponential convergence rates without hard Tsybakov margin condition.
We introduce a variant of the -nearest neighbor classifier in which is chosen adaptively for each query, rather than supplied as a parameter. The choice of depends on properties of each neighborhood, and therefore may significantly vary between different points. (For example, the algorithm will use larger $k…
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology. Convolutional Neural Networks that perform various vision tasks without memory overhaul is the need of the hour. This paper focuses on qualitative and quantitative analysis of reducing the network complexit…
New neural network class reduces VC dimension, leading to better generalization.
problem VC theory struggles with explaining small generalization errors in overparametrized neural networks.
method Developed hyperplane arrangement neural networks (HANNs) and used sample compression analysis.
result HANNs can have significantly smaller VC dimension than the number of weights, yet remain highly expressive.
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theorystat.ML
In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that the Vapnik-Chervonenkis (VC-) dimension of the set of half-spaces in generated by -sparse vectors is bounded below by and above by…
Study on VC dimension of GCNNs with input resolution effects.
problem Understanding the generalization capabilities of GCNNs.
method Derived upper and lower bounds for VC dimension, analyzed factors affecting it.
result Extended previous results on VC dimension of GCNNs, providing insights into input resolution dependence.
Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe. Various authors have described multiple metrics to evaluate the capacity of deep architectures. In this paper, we allude to the radius margin bounds described for a support vector machine (SVM) with hinge loss, apply the…
In Statistical Learning, the Vapnik-Chervonenkis (VC) dimension is an important combinatorial property of classifiers. To our knowledge, no theoretical results yet exist for the VC dimension of edited nearest-neighbour (1NN) classifiers with reference set of fixed size. Related theoretical results are scattered in the …
In many applications of relational learning, the available data can be seen as a sample from a larger relational structure (e.g. we may be given a small fragment from some social network). In this paper we are particularly concerned with scenarios in which we can assume that (i) the domain elements appearing in the giv…
Paper combines RL with policy regularization for inventory policies.
problem Optimizing inventory policies using RL and dynamic programming.
method Hybrid approach combining RL with policy regularization.
result Generalization guarantees for inventory policies using VC theory.
Leveraging algorithmic stability to derive sharp generalization bounds is a classic and powerful approach in learning theory. Since Vapnik and Chervonenkis [1974] first formalized the idea for analyzing SVMs, it has been utilized to study many fundamental learning algorithms (e.g., -nearest neighbors [Rogers and Wag…
Introduces greedy feature selection for classifier-dependent feature ranking.
problem Feature selection for classification tasks.
method Greedy feature selection, identifying the most important feature at each step based on the selected classifier.
result Theoretical and numerical benefits of greedy feature selection.
The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC) dimension. The VC dimension measures the capacity of a learning machine, and a smaller VC dimension leads to improved generalization. On many benchmark datasets, the MCM gene…
WL meet VCcs.LG
The paper connects GNNs to VC dimension theory to study their generalization performance.
problem Understanding GNNs' ability to make meaningful predictions beyond the training set.
method Using Vapnik-Chervonenkis (VC) dimension theory in two settings: no upper bound on graph order and known upper bound.
result Tight connections between GNNs' bitlength, number of colors, and VC dimension in different settings.
Deep ReLU networks generalize well with few parameters.
problem Generalization of overparametrized deep neural networks.
method Explicit bounds on test error independent of overparametrization and VC dimension.
result Generalization error is independent of network architecture and overparametrization.
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset an…
PEA improves PCA and k-means for non-linear data and complex clusters.
problem Non-linear dimensionality reduction and clustering challenges.
method Principal Elliptical Analysis (PEA) for efficient non-linear approximation.
result PEA outperforms k-means in complex data clustering.
Deriving generalization bounds for stable algorithms is a classical question in learning theory taking its roots in the early works by Vapnik and Chervonenkis (1974) and Rogers and Wagner (1978). In a series of recent breakthrough papers by Feldman and Vondrak (2018, 2019), it was shown that the best known high probabi…
Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometric literature. Consis- tency of Lasso has been established under vario…
New algorithm reduces sample complexity for multi-distribution learning.
problem Achieving data-efficient multi-distribution learning with robustness and fairness.
method Proposes a novel algorithm with sample complexity (d+k)/varepsilon^2 for Vapnik-Chervonenkis (VC) dimension d, matching lower bounds.
result Algorithm matches best-known lower bound and resolves open problems in COLT 2023.
This paper analyzes the limits of neural networks under adversarial attacks.
problem Understanding how neural networks perform under adversarial noise.
method Defined the adversarial VC-dimension and combined it with the VC-dimension of halfspace classifiers.
result Concluded the adversarial VC-dimension of neural networks with sign activation functions.
The paper studies empirical processes from nearest neighbors in regression.
problem Estimating conditional cumulative distribution functions and local linear regression.
method Uniform central limit theorem and non-asymptotic bound under local bracketing entropy and uniform entropy numbers.
result Gaussian limit of empirical process with simple covariance.
This paper addresses the complexity of labeled datasets using topological methods.
problem Estimating the necessary training sample size for supervised learning.
method Employing equivalence relations from Topology, data separability results, and combinatorics to compute the Shattering coefficient.
result Estimation of required number of hyperplanes and training sample sizes for binary and multi-class datasets.
Chemical networks outperform spiking neural networks in classification tasks.
problem Learning tasks with spiking neural networks require hidden layers, which are computationally expensive.
method Used deterministic mass-action kinetics to prove chemical reaction networks without hidden layers can solve tasks previously solved by spiking neural networks.
result A chemical reaction network without hidden layers outperforms a spiking neural network with hidden layers in a handwritten digit classification task.
Deep networks can efficiently approximate functions on curved manifolds.
problem Approximating functions and their derivatives on complex, curved domains.
method Proved constant-depth ReLU networks can approximate functions in Sobolev spaces on manifolds.
result Deep networks with bounded weights can approximate functions in to an error of using parameters.
One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm. These constraints are usually expressed as some simplicity assumptions on the target. For instance, in the Vapnik-Chervonenkis (VC) theory the space of possible hypotheses is…
To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version space. However, the effectiveness of volume-splitting critically depends on the initial hypothesis and this problem is also known as target…
Effect of Various Regularizers on Model Complexities of Neural Networks in Presence of Input Noisecs.LG
Deep neural networks are over-parameterized, which implies that the number of parameters are much larger than the number of samples used to train the network. Even in such a regime deep architectures do not overfit. This phenomenon is an active area of research and many theories have been proposed trying to understand …
PAC learning, dating back to Valiant'84 and Vapnik and Chervonenkis'64,'74, is a classic model for studying supervised learning. In the agnostic setting, we have access to a hypothesis set and a training set of labeled samples drawn i.i.d. from a…
New Sauer inequality improves multiclass hypothesis class bounds.
problem Bounding the size of multiclass hypothesis classes.
method Polynomial method and combinatorial parameters (DS, list-DS dimensions).
result Sharp Sauer inequality with optimal polynomial dependence on list size and alphabet size.