arXiv research

Improved SSD for faster and more accurate goodness-of-fit tests and model learning.

BOFiP optimizes high-dimensional functions by distributing them into sub-spaces and using game theory.

Motivated by the equation satisfied by the extremals of certain Hardy-Sobolev type inequalities, we show sharp $L^q$ regularity for finite energy solutions of p-laplace equations involving critical exponents and possible singularity on a sub-space of $\mathbb{R}^n$, which imply asymptotic behavior of the solutions at i…

The Martin boundary of a Cartan-Hadamard manifold describes a fine geometric structure at infinity, which is a sub-space of positive harmonic functions. We describe conditions which ensure that some points of the sphere at infinity belong to the Martin boundary as well. In the case of the universal cover of a compact m…

Proves transitivity of real Anosov diffeomorphisms with specific properties.

In this paper, we propose a design methodology for one-class classifiers using an ensemble-of-classifiers approach. The objective is to select the best structures created during the training phase using an ensemble of spanning trees. It takes the best classifier, partitioning the area near a pattern into $γ^{γ-2}$ sub-…

Analogously to the concept of a curvature of curve and surface, in the differential geometry, in the main part of this paper the concept of the curvature of the hyper-dimensional vector spaces of Riemannian metric is generally defined. The defined concept of the curvature of Riemannian spaces of higher dimensions M: M>…

We explain some interesting relations in the degree three bounded cohomology of surface groups. Specifically, we show that if two faithful Kleinian surface group representations are quasi-isometric, then their bounded fundamental classes are the same in bounded cohomology. This is novel in the setting that one end is d…

We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information of the observations regarding the label variable is preserved. We propose DVSDR, a deep variational approach for sufficient dimensionality r…

Subspace clustering assumes that the data is sepa-rable into separate subspaces. Such a simple as-sumption, does not always hold. We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspac…

The paper extends graph-based semi-supervised learning to infinite-dimensional Wasserstein space.

Introduces Conditional Action Trees to simplify RL action spaces.

Unified Bayesian Optimisation for mixed variables improves performance.

New optimal prior avoids bias in complex models with limited data.

New BO method efficiently optimizes high-dimensional functions by automatically selecting variables.

The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the t…

In the supervised high dimensional settings with a large number of variables and a low number of individuals, one objective is to select the relevant variables and thus to reduce the dimension. That subspace selection is often managed with supervised tools. However, some data can be missing, compromising the validity o…

The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certai…

Two Bayesian optimization methods tackle dynamic design spaces with mixed variables.

A new boosting method reduces overfitting and negative transfer in transfer learning.

HRF enhances tree diversity in random forests to improve performance.

Supervised dimensionality reduction has emerged as an important theme in the last decade. Despite the plethora of models and formulations, there is a lack of a simple model which aims to project the set of patterns into a space defined by the classes (or categories). To this end, we set up a model in which each class i…

Algorithm improves SVM classification in non-Euclidean spaces.

Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a large fraction of labels have very few positive instances in the data distribution. Most state-of-the-…

Many activation functions have been proposed in the past, but selecting an adequate one requires trial and error. We propose a new methodology of designing activation functions within a neural network at each layer. We call this technique an "activation ensemble" because it allows the use of multiple activation functio…

This paper studies activation sparsity in large language models, finding key trends and implications.

Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hi…

Many neural network architectures rely on the choice of the activation function for each hidden layer. Given the activation function, the neural network is trained over the bias and the weight parameters. The bias catches the center of the activation, and the weights capture the scale. Here we propose to train the netw…

Active learning method balances bias and variance under class imbalance.

LOTOS improves ensemble robustness by promoting orthogonal transformations.

Study active learning of PTFs with derivative access.

Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples giv…

Bayesian adaptive designs can be biased by active learning, especially with misspecified models.

Derives time-averaged active inference from control principles.

RAN model recognizes multiple activities from unlabeled sensor data.

A new indicator measures project risk from activity durations.

We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…

Hidden Markov Models detect hand gestures from wearable sEMG signals.

Active testing reduces label costs for efficient model evaluation.

The choice of activation function can have a large effect on the performance of a neural network. While there have been some attempts to hand-engineer novel activation functions, the Rectified Linear Unit (ReLU) remains the most commonly-used in practice. This paper shows that evolutionary algorithms can discover novel…

Survey of trainable activation functions in neural networks.

This paper analyzes and improves active learning techniques for real-world projects.

Study active nematic forces on curved surfaces, revealing new coupling mechanisms.

This paper provides an overview of activation functions in neural networks.

Bayesian active learning method improved for censored regression data.

DUNs improve active learning by dynamically adjusting model complexity.

Large deviation principle for deep neural networks with ReLU activation.

TransFall uses transfer learning to improve activity recognition from mobile sensors.