A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithm balances exploration cost between groups in multi-armed bandits.
AgABC improves ABC algorithm by balancing exploration and exploitation.
Enhances financial analysis with multi-agent collaboration.
Firms' collaboration networks can decline but remain resilient.
Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid.…
New algorithms reduce communication costs in collaborative learning.
PCL tackles collaborative learning for diverse agents, reducing sample complexity.
CoLoRA leverages task similarity to boost fine-tuning efficiency.
Study collaborative learning among multi-agents in multi-armed bandits.
This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.
Enhances student diversity in collaborative learning.
In conventional domain adaptation, a critical assumption is that there exists a fully labeled domain (source) that contains the same label space as another unlabeled or scarcely labeled domain (target). However, in the real world, there often exist application scenarios in which both domains are partially labeled and n…
Adaptive time decay functions improve financial product recommendation accuracy.
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
AdaDKRR tackles data silos by combining autonomy, privacy, and collaboration.
Study on collaborative vs. non-collaborative online and bandit convex optimization.
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…
Machine learning assesses group collaboration in classrooms.
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in practice: 1) only limited labeled samples are available due to expensive annotation costs over medical images; 2) labeled images may contain considerable label noises (e.g., mi…
Efficient CF approach using fast adaptive PCA for recommender systems.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
A new algorithm adapts to changing user behaviors in finance.
A new method for online personalized learning reduces gradient variance by dynamically selecting peers.
Optimum-statistical collaboration improves black-box optimization efficiency.
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…
In this paper we present a review of the existing typologies of Internet service users. We zoom in on social networking services including blogs and crowdsourcing websites. Based on the results of the analysis of the considered typologies obtained by means of FCA we developed a new user typology of a certain class of I…
Paper proposes CLAIR for efficient LLM fine-tuning across clients.
We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting. We study the problem of designing a learning algorithm for the first agent (A1) that facilitates a successful collaboration even in cases when the second agent (A2) is adapting its policy in an unknown way. The key ch…
Algorithm maximizes user rewards under per-item budget constraints.
Proposes TFCL to mitigate negative transfer in MTL by collaborating across features and tasks.
This study proposes a deep learning framework using ResNeXt for efficient financial data mining.
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…
Distributed adaptive networks achieve better estimation performance by exploiting temporal and as well spatial diversity while consuming few resources. Recent works have studied the single task distributed estimation problem, in which the nodes estimate a single optimum parameter vector collaboratively. However, there …
The paper defines a new Lie groupoid concept for infinite dimensions.
New algorithm reduces multi-agent bandit regret by sharing data.
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso model at the indivi…
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…
We introduce collaborative learning in which multiple classifier heads of the same network are simultaneously trained on the same training data to improve generalization and robustness to label noise with no extra inference cost. It acquires the strengths from auxiliary training, multi-task learning and knowledge disti…
The paper proposes a model reward scheme for collaborative ML based on Shapley value and information gain.
This paper has been withdrawn by arXiv administrators because of disputed claims of authorship among former collaborators
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not applicable. One of the main strategies is to use pure or hybrid content-based approaches, which usually yield to lower recommendation quality tha…
Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper…
FL-Sailer enables federated learning for scATAC-seq data, reducing dimensionality and noise.
Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of rati…
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with…
Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user dom…
Optimizes resource allocation for distributed parameter estimation in sensor networks.
A novel method for efficient CDRL over wireless networks.