arXiv research

A new method for virtual drug screening detects top treatments.

Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be…

Kriging is an efficient machine-learning tool, which allows to obtain an approximate response of an investigated phenomenon on the whole parametric space. Adaptive schemes provide a the ability to guide the experiment yielding new sample point positions to enrich the metamodel. Herein a novel adaptive scheme called Mon…

New representation for braid groups and surface braid groups, extending Lawrence-Krammer-Bigelow.

SCQRNN prevents quantile crossing and improves computational efficiency.

We make systematic developments on Lawson-Osserman constructions relating to the Dirichlet problem (over unit disks) for minimal surfaces of high codimension in their 1977 Acta paper. In particular, we show the existence of boundary functions for which infinitely many analytic solutions and at least one nonsmooth Lipsc…

We consider the problem of multi-task learning in the high dimensional setting. In particular, we introduce an estimator and investigate its statistical and computational properties for the problem of multiple connected linear regressions known as Data Enrichment/Sharing. The between-tasks connections are captured by a…

Paper develops a new method for distribution regression with indefinite kernels.

Sparse Gaussian process quantile regression tackles computational challenges in Bayesian quantile regression.

Paper introduces robust distribution regression using kernel methods.

Study reduces memory needs for active learning with enriched queries.

We define a symmetric monoidal (4,3)-category with duals whose objects are certain enriched multi-fusion categories. For every modular tensor category $\mathcal{C}$, there is a self enriched multi-fusion category $\mathfrak{C}$ giving rise to an object of this symmetric monoidal (4,3)-category. We conjecture that the e…

TSK-Streams learns fuzzy rules from data streams.

Book explores infinite translation surfaces, challenging traditional geometry.

DS2CF-Net learns hierarchical representations with deep coupled factorization and enriched prior.

In this paper we analyze supergeometric locally covariant quantum field theories. We develop suitable categories SLoc of super-Cartan supermanifolds, which generalize Lorentz manifolds in ordinary quantum field theory, and show that, starting from a few representation theoretic and geometric data, one can construct a f…

A new method combines federated learning and logistic regression for better credit scoring.

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods focus on enriching compared neighborhood relations in CF directly to address the sparsity problem. In this paper, we propose superhighway const…

This paper evaluates data enrichment techniques for rare event detection in manufacturing.

KANEL combines models for early hit enrichment in virtual screening.

This note compares two recently published machine learning methods for constructing flexible, but tractable families of variational hidden-variable posteriors. The first method, called "hierarchical variational models" enriches the inference model with an extra variable, while the other, called "auxiliary deep generati…

DeGAN enriches data from related domains for future learning tasks.

Fatgraphs are multigraphs enriched with a cyclic order of the edges incident to a vertex. This paper presents algorithms to: (1) generate the set of all fatgraphs having a given genus and number of boundary cycles; (2) compute automorphisms of any given fatgraph; (3) compute the homology of the fatgraph complex. The al…

We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning and point estimation problems without analytical solution (such as hyperparameter or entropy es…

Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent structure and variation that are enriched in a target dataset compared to some background---e.g. enriched in patients compared to the genera…

Dual-sPLS improves feature selection and prediction in high-dimensional data.

This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises. Our …

The Lax-Hopf formula simplifies the value function of an intertemporal optimization (infinite dimensional) problem associated with a convex transaction-cost function which depends only on the transactions (velocities) of a commodity evolution: it states that the value function is equal to the marginal fonction of a fin…

Improves neural network performance by enriching training dataset.

Geometric problems are usually formulated by means of (exterior) differential systems. In this theory, one enriches the system by adding algebraic and differential constraints, and then looks for regular solutions. Here we adopt a dual approach, which consists to enrich a plane field, as this is often practised in cont…

SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.

The identification of predictive biomarkers from a large scale of covariates for subgroup analysis has attracted fundamental attention in medical research. In this article, we propose a generalized penalized regression method with a novel penalty function, for enforcing the hierarchy structure between the prognostic an…

Deep learning model explains breast cancer subtypes using logistic regression.

Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in the prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simulta…

Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.

Proposes a deep tree-ensemble model for multi-output prediction.

Study improves retail demand forecasting by integrating macroeconomic data.

Fix a finite group $G$ and a conjugacy invariant subset $C\subseteq G$. Let $Σ$ be an oriented surface, possibly with punctures. We consider the question of when two homomorphisms $π_1(Σ) \to G$ taking punctures into $C$ are equivalent up to an orientation preserving diffeomorphism of $Σ$. We provide an answer to this …

Enhances graph neural networks with random walks to improve performance.

Paper reviews neurolinguistics and language technologies, emphasizing mutual enrichment.

Flowification enriches neural networks with an inverse pass and likelihood monitoring.

Algorithm recovers multiple low-rank matrices from unlabeled data.

Enhances LLMs for predicting stock movements by considering news dissemination and context.

The article proposes optimal learning strategies for machine learning-based reliability analysis.

Gaussian process regression loses locality in high dimensions, affecting molecular energy surface fitting.

Enhanced tree-based classifiers use derivatives and geometry for better function classification.

Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and subjectivity of music genres. Accounting for these has, though, many benefits for aligning knowledge sources, integrating data and enriching …

BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.