arXiv research

New algorithms reduce communication costs in collaborative learning.

Improved GAN sampling by collaborating discriminator and generator.

PCL tackles collaborative learning for diverse agents, reducing sample complexity.

We study a recent model of collaborative PAC learning where $k$ players with $k$ different tasks collaborate to learn a single classifier that works for all tasks. Previous work showed that when there is a classifier that has very small error on all tasks, there is a collaborative algorithm that finds a single classifi…

Optimizes resource allocation for distributed parameter estimation in sensor networks.

New method for collaborative learning with multiple distributions, minimizing sample size.

Study on collaboration vs. independent data collection in sensor networks.

Machine learning assesses group collaboration in classrooms.

Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.

Optimal algorithm found for collaborative learning in bandits with optimal regret bounds.

A graph-based method for two-sample testing across connected nodes.

New algorithm for collaborative bandit learning reduces sample complexity and regret.

The data torrent unleashed by current and upcoming astronomical surveys demands scalable analysis methods. Many machine learning approaches scale well, but separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is often an after-th…

We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously. The quality of the col…

Method transfers knowledge between partially labeled domains to classify all samples.

A framework for collaborative learning reduces communication rounds.

Paper tackles medical image diagnosis with unsupervised domain adaptation.

This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.

Backdoor attacks are possible in feature-partitioned collaborative learning, even without labels.

A federated learning framework improves RUL prognosis for aircraft engines without sharing data.

In this paper, we apply a mini-batch based negative sampling method to efficiently train a latent factor autoencoder model on large scale and sparse data for implicit feedback collaborative filtering. We compare our work against a state-of-the-art baseline model on different experimental datasets and show that this met…

FedLCB-Q learns optimal policies from federated offline data with linear speedup.

The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the o…

This paper improves collaborative filtering by integrating user and item embeddings with attention.

ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.

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

Split learning improves deep learning in healthcare by sharing data.

New collaborative algorithm improves personalized mean estimation in online settings.

New algorithm learns low-rank matrices with linear number of samples.

A new KD model for collaborative filtering improves top-N recommendation performance.

Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a us…

Algorithm estimates tensors from sparse observations with robust error bounds.

Supply Chain Management often requires independent organizations to work together to achieve shared objectives. This collaboration is necessary when coordinated actions benefit the group more than the uncoordinated efforts of individual firms. Despite the commonly reported benefits that can be gained in close relations…

This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies w…

Study addresses covariate mismatch in federated learning, improving model accuracy.

New algorithm reduces multi-agent bandit regret by sharing data.

Noisy Pooled PCR tests large groups more efficiently.

Drone optimizes collaborative learning for neural networks.

Proposes MC-AE for better unsupervised clustering of unlabeled data.

Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibili…

A model for collaborative learning with principal-agent interaction.

Paper tackles unobserved confounding in human-AI collaborations.

The paper identifies collaborations in codebases using commit activity and language usage.

This paper studies the problem of data-adaptive representations for big, distributed data. It is assumed that a number of geographically-distributed, interconnected sites have massive local data and they are interested in collaboratively learning a low-dimensional geometric structure underlying these data. In contrast …

In this paper we consider the collaborative ranking setting: a pool of users each provides a small number of pairwise preferences between $d$ possible items; from these we need to predict preferences of the users for items they have not yet seen. We do so by fitting a rank $r$ score matrix to the pairwise data, and pro…

Firms' collaboration networks can decline but remain resilient.

Data science teams collaborate extensively, using various tools and stakeholders.

Study collaborative learning among multi-agents in multi-armed bandits.