Survey shows firms still underutilize collaboration in procurement relationships.
problem Limited collaboration between purchasing firms and suppliers despite benefits.
method Survey of procurement professionals, structural equation modeling.
result Firms are not fully utilizing collaborative relationships.
Collaborative filtering is a rapidly advancing research area. Every year several new techniques are proposed and yet it is not clear which of the techniques work best and under what conditions. In this paper we conduct a study comparing several collaborative filtering techniques -- both classic and recent state-of-the-…
Gradio simplifies sharing and testing ML models for non-experts.
problem Accessibility and collaboration challenges in machine learning.
method Developed an open-source Python package, Gradio, to create visual interfaces for ML models.
result Gradio makes it easy for non-technical users to test and provide feedback on ML models.
AI uses statistical PQRS workflow for human-machine collaboration.
problem Integrating AI with statistical principles for better results.
method PQRS workflow integrating statistical concepts with AI.
result Reproducibility and interpretability of AI algorithms and results.
Enhances financial analysis with multi-agent collaboration.
problem Limited use of AI-agent collaboration in financial research.
method Proposes a multi-agent system for financial investment research.
result Multi-agent system outperforms single-agent models.
Benchopt automates machine learning benchmarking across languages and hardware.
problem Limited transparency and tedious re-implementation work in machine learning validation.
method A collaborative framework for automating, reproducing, and publishing optimization benchmarks.
result Demonstrates practical findings that highlight the importance of details in machine learning validation.
Survey on recent work on area-minimizing currents.
problem Regularity of area-minimizing currents in higher codimensions.
method Collaborative research with Emanuele Spadaro.
result Recent findings on the regularity of area-minimizing currents.
Crowdsourced investigation shows differing results for technical analysis strategies.
problem Differing results in technical analysis strategies due to lack of method.
method Collaborative scientific computational framework using Monte Carlo simulations and historical back testing.
result Results are not repeatable by other researchers, highlighting the need for transparency and robustness.
This workshop about triangulations of manifolds in computational geometry and topology was held at the 2014 CG-Week in Kyoto, Japan. It focussed on computational and combinatorial questions regarding triangulations, with the goal of bringing together researchers working on various aspects of triangulations and of foste…
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
A recipe recommendation system suggests missing ingredients using collaborative filtering.
problem Encouraging healthy diets through personalized ingredient suggestions.
method Item-based collaborative filtering applied to a sparse dataset of recipes.
result Best method achieves a recall@10 of circa 40%.
R package connects to OpenML for machine learning tasks and data sharing.
problem Efficiency and reproducibility in machine learning research.
method R package interface with OpenML platform.
result Enables easy data and task sharing, reproducibility, and collaboration.
Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the pres…
Lingvo is a flexible framework for deep learning sequence models.
problem Sequence-to-sequence modeling challenges in deep learning.
method Modular, scalable, and customizable Tensorflow framework.
result Supports distributed training and quantized inference.
Surveying how to use unlabeled data in federated learning.
problem Costly labeling of data limits FL applications.
method Survey and analyze existing research.
result Potential for using unlabeled data in FL.
Study designs statistical inference for collaborative science teams.
problem Maintaining scientific rigor in distributed, collaborative research.
method Analyzes hypothesis testing with strategic agents and principals.
result Principal can design policies to control posterior probability of null.
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…
Machine learning applied to particle physics, focusing on future research and development.
problem Improving particle physics analysis and event identification.
method Roadmap for machine learning applications, resource requirements, and collaborative initiatives.
result Identification of resource needs and areas for external collaboration.
VAEs improve collaborative filtering for implicit feedback.
problem Limited modeling capacity of linear factor models in collaborative filtering.
method Introduced a generative model with multinomial likelihood and used Bayesian inference for parameter estimation.
result Significantly outperforms state-of-the-art baselines on real-world datasets.
PEHRT harmonizes EHR data for translational research.
problem Barriers in using EHR data for translational research.
method Common pipeline including open-source code, visualization tools, and detailed documentation.
result PEHRT harmonizes EHR data to standardized ontologies and generates robust embeddings.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
Paper presents a collaborative learning model to improve QoE models without sharing sensitive data.
problem Limited data volume and participant profiles lead to over-fitting and poor generalization of QoE models.
method Round-Robin based Collaborative Machine Learning training without sharing datasets.
result The proposed model outperforms conventional centralized and isolated learning methods.
Research tackles learning vertex representations for bipartite networks.
problem Lack of research on learning vertex representations for bipartite networks.
method Apply generic methods like node2vec and LINE, but ignore vertex type information.
result Generic methods are suboptimal for bipartite networks due to different properties and patterns.
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…
FL improves insurance claims loss prediction without sharing data.
problem Limited data volume and variety due to privacy concerns.
method Federated Learning (FL) to update a global model using local data insights.
result Improved claims loss forecasting compared to individual models.
This study optimizes model averaging for personalized collaborative learning.
problem Differences in data or objectives between nodes in federated learning.
method Weighted averaging between local and global models for scalar mean estimation.
result There is always some positive model averaging that reduces expected squared error.
Flexible priors improve VAE-based CF models for better user preference modeling.
problem Simplistic priors in VAEs limit user preference modeling and deeper representation learning.
method Incorporated flexible priors and gating mechanisms into VAEs for collaborative filtering.
result Flexible priors and gating mechanisms significantly improve recommendation performance.
Shai-am simplifies ML for finance, solving code structure and scalability issues.
problem Challenges in integrating ML for investment strategies, including code structure and scalability.
method Integrates a Python framework with modern open-source technologies to manage containerized pipelines and unified interfaces.
result Facilitates collaborative work in quantitative finance by enhancing reusability and readability.
Survey on federated learning systems for privacy protection.
problem Privacy-preserving collaborative training of machine learning models.
method Comprehensive review and categorization of federated learning systems.
result Systematic summary of existing federated learning systems and design factors.
This study proposes a deep learning framework using ResNeXt for efficient financial data mining.
problem Complex financial data with high dimensionality, nonlinearity, and task correlations.
method Introduces ResNeXt into multi-task learning framework for efficient feature extraction and task collaboration.
result Significantly improved performance in classification and regression tasks on S&P 500 data.
Secure sum outperforms homomorphic encryption in collaborative deep learning.
problem Training deep learning models on private data from multiple parties without revealing the data.
method Used a secure sum protocol in conjunction with default secure channels.
result Secure sum protocol provides superior properties in terms of collusion-resistance and runtime.
Federated learning collaborates clients to train models without sharing data.
problem Privacy and data sharing in machine learning.
method Central server orchestrates collaborative training of models on decentralized data.
result Recent advances and open problems in FL.
Develops user-controlled privacy collaboration between users and data providers.
problem Ensuring data privacy while maintaining utility for users and providers.
method Collaborative learning of a sensitization function to control data sharing and privacy.
result Maintains data utility while fully protecting private information through user-controlled privacy settings.
YRC-Bench benchmarks AI agents learning to collaborate with experts.
problem Learning to recognize when to consult an expert in novel situations.
method Validation strategy and proposer-validator decomposition.
result Insights for future AI safety research.
Personalized Transformer improves temporal collaborative ranking performance.
problem Temporal collaborative ranking in recommendation systems.
method Personalized Transformer model using attention mechanisms.
result Personalized Transformer outperforms SASRec by almost 5% in NDCG@10.
New method optimizes collaborative filtering for better ranking metrics.
problem Improving recommendation quality metrics like top-N ranking.
method Actor-critic reinforcement learning to directly optimize ranking metrics.
result The method outperforms state-of-the-art baselines on real-world datasets.
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on perfor…
FRACTI framework supports large-scale collaboration and transparent investigation in finance.
problem Complex, multidisciplinary research in finance requires robust support systems.
method Defines scientific support systems, shares contributions, classifies facets, and outlines a meta-model.
result FRACTI enables provenance tracking and large-scale investigation in computational finance.
A new platform helps detect and manage pressure ulcers.
problem Detecting and managing pressure ulcers efficiently.
method Convolutional neural networks and transfer learning.
result Automated skin damage and pressure ulcer assessment tool.
Study compares federated and centralized learning for patient data privacy.
problem Ensuring privacy in machine learning models trained on electronic health records.
method Examined private and non-private federated learning for clinical prediction tasks.
result Differentially private stochastic gradient descent is effective in centralized learning but challenging in federated learning.
Podcast recommendations improved by analyzing user listening paths.
problem Challenges in recommending podcasts effectively.
method Analyzes user listening paths as sequential trajectories for recommendations.
result 450% increase in effectiveness over baseline.
This paper improves collaborative filtering by integrating user and item embeddings with attention.
problem Sparse ratings and limited robustness of Bayesian methods in collaborative filtering.
method Proposes a VAE-based Bayesian MF framework that leverages both data and embedding information.
result The proposed method enhances robustness and accuracy of collaborative filtering models.
Proposes MCCF to distinguish latent purchasing motivations in user-item interactions.
problem Difficulty in capturing fine-grained user preferences due to complex latent motivations.
method Introduces MCCF with decomposer and combiner modules to identify and recombine latent components.
result Significant performance gains and necessity of considering multiple components demonstrated.
Coding collaborations link crypto returns, revealing systemic transparency.
problem Cryptocurrencies' market behavior overlooked due to isolated code approach.
method Analyzed 4% of developers contributing to multiple cryptocurrencies.
result First coding event linking two cryptocurrencies synchronizes their returns.
A new linear GCN model improves recommendation performance for large graphs.
problem Training difficulties and over-smoothing in GCN-based CF models.
method Proposes a linear residual graph convolutional network (LRGCCF) to address training difficulties and over-smoothing issues.
result The proposed model yields better efficiency and effectiveness on real datasets.
Develops a deep learning framework to predict future tech directions for high-tech companies.
problem Difficult task in predicting future R&D trends for high-tech companies due to complexity and variety of factors.
method Deep Technology Forecasting (DTF) framework with three components: PCR, CTR, and DTT neural network.
result DTF framework precisely predicts future tech emphasis of companies using hybrid factors.
TradingAgents uses LLM-powered multi-agent framework for financial trading.
problem Lack of collaborative dynamics in multi-agent financial trading systems.
method Inspired by real-world trading firms, TradingAgents features specialized LLM-powered agents and a risk management team.
result Framework outperforms baseline models in trading performance metrics.
PrivacyFL simulates privacy-preserving federated learning.
problem Ensuring privacy in federated learning environments.
method Extensible, configurable simulator for federated learning.
result PrivacyFL checks feasibility and improves model accuracy.