arXiv research

A framework isolates and learns approximately shared features for better domain adaptation.

PerPCA separates unique and shared features from heterogeneous data.

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…

Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sub-linearly with the number of possible classes. This implies that features …

Multitask learning (MTL) aims to learn multiple tasks simultaneously through the interdependence between different tasks. The way to measure the relatedness between tasks is always a popular issue. There are mainly two ways to measure relatedness between tasks: common parameters sharing and common features sharing acro…

Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned among multiple parties, and sharing of raw data or model parameters amo…

This paper considers the multi-task learning problem and in the setting where some relevant features could be shared across few related tasks. Most of the existing methods assume the extent to which the given tasks are related or share a common feature space to be known apriori. In real-world applications however, it i…

The creation of social ties is largely determined by the entangled effects of people's similarities in terms of individual characters and friends. However, feature and structural characters of people usually appear to be correlated, making it difficult to determine which has greater responsibility in the formation of t…

Improved neural population modeling using shared features and ensemble detection.

Study predicts customer data sharing in Open Banking and explains key factors.

Contrastive learning properties studied, including feature suppression and hierarchical learning.

Improved supervised EM learning for shared kernel models with feature space partitioning.

MT-HAL learns features and task associations for multiple tasks with a shared sparse structure.

New algorithms minimize regret in multi-task and lifelong linear bandits with shared representation.

Deep RL policies share adversarial features across different MDPs.

Hybrid model combines PCA and RNN for better aerospace stock price prediction.

Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties like uncertainty estimation, model robustness and regularisation. We want to discuss here the application of Bayesian models to knowledge sha…

Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP) as a prior over latent features, allow the number of features to be determined from the data. We present a generalization of the IBP, the …

New model allocates features sublinearly, improving model fit and performance.

Paper tackles robust decision-making from multiple sites with shared structure.

DFI maps covariates to latent representations for feature importance.

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the mod…

Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated learning (VFL) deals with the case where participants sharing the same sample ID space but having different feature spaces, while label informa…

Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domai…

DeepSeekMoE improves language model efficiency with shared experts and normalized gating.

A CNN-based model improves stock price prediction accuracy.

PIMA autoencoders discover shared features in multimodal scientific data.

Quaternion self-attention reduces computational cost and improves performance.

Proposes TFCL to mitigate negative transfer in MTL by collaborating across features and tasks.

PPG separates policy and value function training phases for better reinforcement learning efficiency.

The Hirzebruch $χ_y$-genus and Poincare polynomial share some similar features. In this article we investigate two of their similar features simultaneously. Through this process we shall derive several new results as well as reprove and improve some known results.

Paper improves bike-sharing demand prediction by adapting to changing patterns.

This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared features in a set of time series that exhibit significant individual variability. Our method builds on the framework of latent Diricihlet alloc…

In recent years, dock-less shared bikes have been widely spread across many cities in China and facilitate people's lives. However, at the same time, it also raises many problems about dock-less shared bike management due to the mismatching between demands and real distribution of bikes. Before deploying dock-less shar…

New risk measures for quantiles under ambiguity improve risk sharing.

New model captures complex relationships from experimental data.

Model predicts stock prices using Twitter sentiment data.

Hybrid framework prevents forgetting in continual learning.

New method identifies shared components from unpaired multimodal mixtures.

Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…

DAF uses attention sharing to adapt forecasts from abundant to scarce data.

The paper addresses risk sharing and variability measures among agents with general risk preferences.

Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorize…

A limaçon-like curve, allowing 2π-transition with monotone curvature between concentric curvature elements, is presented. The curve is 4th degree algebraic, 4th degree rational, and shares other common features with Pascal's limaçon.

New model for multiplex networks learns shared structure.

Surveying joint Gaussian graphical models to identify shared structures across domains.

Map matching of GPS trajectories from a sequence of noisy observations serves the purpose of recovering the original routes in a road network. In this work in progress, we attempt to share our experience of feature construction in a spatial database by reporting our ongoing experiment of feature extrac-tion in Conditio…

A novel approach tackles sparse linear bandits with reduced communication costs and minimal cumulative regret.