arXiv research

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

The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper pre…

Meta clustering categorizes learners for collaborative learning.

A new privacy-preserving deep learning scheme for asymmetrically collaborative machine learning.

Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity proble…

In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner l…

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The prop…

FL improves insurance claims loss prediction without sharing data.

Collaborative filtering (CF) and content-based filtering (CBF) have widely been used in information filtering applications. Both approaches have their strengths and weaknesses which is why researchers have developed hybrid systems. This paper proposes a novel approach to unify CF and CBF in a probabilistic framework, n…

AdaDKRR tackles data silos by combining autonomy, privacy, and collaboration.

New algorithms for collaborative learning in uncertain, decentralized environments.

Novel method reduces radiomic data annotation needs.

Machine learning assesses group collaboration in classrooms.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose f…

Split learning improves deep learning in healthcare by sharing data.

NKI integrates obfuscated datasets using nonlinear kernels for improved data collaboration.

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

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations…

Paper proposes efficient privacy-preserving matrix encryption for secure collaborative learning against malicious adversaries.

Advances in collaborative filtering and ranking methods.

This paper analyzes privacy-preserving methods for collaborative forecasting.

Paper proposes FedAMP for improved federated learning with non-IID data.

Optimizes resource allocation for distributed parameter estimation in sensor networks.

New algorithms reduce communication costs in collaborative learning.

Proposes a new method to handle data heterogeneity in causal inference.

Game theory models incentivizes honesty in collaborative learning among competitors.

Framework allows organizations to collaborate on learning tasks securely.

Efficiently preserves privacy in logistic regression for IoT data.

In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models remain distributed over some institutions. Recently, data became large and distributed with decreasing costs of data collection. If we can cen…

Enhances student diversity in collaborative learning.

Develops NFCF to reduce gender bias in social media recommendation systems.

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

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algorithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the sta…

Asynchronous algorithms reduce privacy costs in distributed machine learning.

The paper proposes a model reward scheme for collaborative ML based on Shapley value and information gain.

Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…

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…

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

Improved product recommendations using deep learning.

Collab algorithm learns models from agents with missing data.

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…

We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine learning markets in terms of fair revenue distribution and potential threats, including…

Drone optimizes collaborative learning for neural networks.

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

A framework for collaborative learning reduces communication rounds.

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

A novel federated learning framework resolves structural misalignment in model fusion.

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning …