arXiv research

Kaggle competitions offer valuable insights for business forecasting.

Indirect competition emerged from the complex organization of human societies, and knowledge of the existing network topology may aid in developing effective strategies for success. Here, we propose an agent-based model of competition with systems co-existing in a `small-world' social network. We show that within the r…

New model improves deep learning robustness against adversarial attacks.

This work tackles representation learning by introducing stochastic competition-based activations.

While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions …

In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learnin…

The aim of this work is to enable inference of deep networks that retain high accuracy for the least possible model complexity, with the latter deduced from the data during inference. To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of…

Accelerates GPR with localized kernels for faster performance.

Algorithm for decentralized competition among adaptive agents.

Paper refutes EM convergence theory and introduces a new EM algorithm.

Develops a learning model predictive controller for competitive racing.

Estimates manifold dimension using local graph structure.

Hybridizes genetic algorithms with PROMOTHEE for better economic activity localization decisions.

Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation tim…

Proposes a continuous, differentiable model from local adaptive models.

Local SGD outperforms conventional methods in LLM training.

We present a first procedure that can estimate -- with statistical consistency guarantees -- any local-maxima of a density, under benign distributional conditions. The procedure estimates all such local maxima, or $\textit{modal-sets}$, of any bounded shape or dimension, including usual point-modes. In practice, modal-…

Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous node and edge attributes. Analogous to image-based convolutional networks that op…

CaLoNet integrates spatial and local correlations for multivariate time series classification.

We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible architectures, including those similar to ResNets. Inversion is achieved with a locally convergent iterative procedure that is parallelizable …

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components: (1) the vector representations of loc…

A new classifier encodes local neighborhoods for each class using Fly Bloom Filters.

Model shows partial compliance can lead to less fair outcomes than expected.

Study copyright's impact on creative industries using AI-generated fonts.

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a chec…

Diabetic Retinopathy is the leading cause of blindness in the world. At least 90\% of new cases can be reduced with proper treatment and monitoring of the eyes. However, scanning the entire population of patients is a difficult endeavor. Computer-aided diagnosis tools in retinal image analysis can make the process scal…

Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning…

For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past decade. In this paper, we introduce a new local SVM method, called L$^3$-SVMs, which clusters the input space, carries out dimensionality r…

Proposes LSGP for better graph signal representation.

Novel approach combines local and global brain changes for AD prediction.

We study Smoothed Online Convex Optimization, a version of online convex optimization where the learner incurs a penalty for changing her actions between rounds. Given a $Ω(\sqrt{d})$ lower bound on the competitive ratio of any online algorithm, where $d$ is the dimension of the action space, we ask under what conditio…

New estimator robust to adversarial noise and data heterogeneity.

L0Learn solves sparse learning problems with millions of features.

A new method for multiclass calibration using vector quantization.

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's p…

LLoCa makes any network Lorentz-equivariant, achieving high accuracy and efficiency.

Backpropagation-free RL method trains layers using local signals.

Efficient method classifies locally stationary time series based on second-order characteristics.

The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.

Introduces recency bias to improve time-series forecasting.

CLAPS improves conformal regression by adaptively scaling interval widths based on last-layer Laplace uncertainty.

Stochastic LWTA networks resist adversarial attacks while maintaining accuracy.

LESS combines local predictors for subsets to learn from heterogeneous input-output pairs.

We propose a flexible nonparametric regression method for ultrahigh-dimensional data. As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression. Then, an iterative procedure is developed to recover both the important covariates and the regress…

DSVNP uses global and local latent variables for improved neural process predictions.

Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive wi…

Proposes CoPO, a new policy optimization method for competitive games.

Estimates path-valued data using signature metrics and local kernels.