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Tag: multi

  • Blessings of Multiple Good Arms in Multi-Objective Linear Bandits

    Blessings of Multiple Good Arms in Multi-Objective Linear Bandits arXiv:2602.12901v1 Announce Type: new Abstract: The multi objective bandit setting has traditionally been regarded as more complex than the single objective case, as multiple objectives must be optimized simultaneously. In contrast to this prevailing view, we demonstrate that when multiple good arms exist for multiple objectives,…

  • Learning Multi-type heterogeneous interacting particle systems

    Learning Multi-type heterogeneous interacting particle systems arXiv:2602.03954v1 Announce Type: new Abstract: We propose a framework for the joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems from multi-trajectory data. This learning task is a challenging non-convex mixed-integer optimization problem, which we address through a novel three-stage approach.…

  • Multi-Attribute Decision Matrices, Done Right

    Multi-Attribute Decision Matrices, Done Right How to structure decisions, identify efficient options, and avoid misleading value metrics The post Multi-Attribute Decision Matrices, Done Right appeared first on Towards Data Science. Josiah DeValois Go to original source

  • Multi-task Modeling for Engineering Applications with Sparse Data

    Multi-task Modeling for Engineering Applications with Sparse Data arXiv:2601.05910v1 Announce Type: new Abstract: Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized…

  • Microeconomic Foundations of Multi-Agent Learning

    Microeconomic Foundations of Multi-Agent Learning arXiv:2601.03451v1 Announce Type: new Abstract: Modern AI systems increasingly operate inside markets and institutions where data, behavior, and incentives are endogenous. This paper develops an economic foundation for multi-agent learning by studying a principal-agent interaction in a Markov decision process with strategic externalities, where both the principal and the agent…

  • Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options

    Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options Human-guided AI collaboration The post Probabilistic Multi-Variant Reasoning: Turning Fluent LLM Answers Into Weighted Options appeared first on Towards Data Science. alan nekhom Go to original source

  • How Agent Handoffs Work in Multi-Agent Systems

    How Agent Handoffs Work in Multi-Agent Systems Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph The post How Agent Handoffs Work in Multi-Agent Systems appeared first on Towards Data Science. Kenneth Leung Go to original source

  • Multi-Agent Arena: Insights from London Great Agent Hack 2025

    Multi-Agent Arena: Insights from London Great Agent Hack 2025 What mattered: robust agents, glass-box reasoning, and red-team resilience The post Multi-Agent Arena: Insights from London Great Agent Hack 2025 appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • Multi-Agent SQL Assistant, Part 2: Building a RAG Manager

    Multi-Agent SQL Assistant, Part 2: Building a RAG Manager A hands-on guide to comparing multiple RAG strategies — Keyword, FAISS, and Chroma The post Multi-Agent SQL Assistant, Part 2: Building a RAG Manager appeared first on Towards Data Science. Alle Sravani Go to original source

  • Learning Multi-Index Models with Hyper-Kernel Ridge Regression

    Learning Multi-Index Models with Hyper-Kernel Ridge Regression arXiv:2510.02532v1 Announce Type: new Abstract: Deep neural networks excel in high-dimensional problems, outperforming models such as kernel methods, which suffer from the curse of dimensionality. However, the theoretical foundations of this success remain poorly understood. We follow the idea that the compositional structure of the learning task is…

  • Risk-averse Fair Multi-class Classification

    Risk-averse Fair Multi-class Classification arXiv:2509.05771v1 Announce Type: new Abstract: We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable for multi-class problems when the data is noisy, scarce (relative to the dimension of the problem), and the labeling might be unreliable. In the…

  • Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning

    Deep Intrinsic Coregionalization Multi-Output Gaussian Process Surrogate with Active Learning arXiv:2508.16434v1 Announce Type: new Abstract: Deep Gaussian Processes (DGPs) are powerful surrogate models known for their flexibility and ability to capture complex functions. However, extending them to multi-output settings remains challenging due to the need for efficient dependency modeling. We propose the Deep Intrinsic Coregionalization…

  • Objective Soups: Multilingual Multi-Task Modeling for Speech Processing

    Objective Soups: Multilingual Multi-Task Modeling for Speech Processing arXiv:2508.09228v1 Announce Type: cross Abstract: Training a single model for multilingual, multi-task speech processing (MSP) is severely hampered by conflicting objectives between tasks like speech recognition and translation. While multi-objective optimization (MOO) aims to align gradient updates, its effectiveness diminishes as the number of tasks grows, making…

  • Build Multi-Agent Apps with OpenAI’s Agent SDK

    Build Multi-Agent Apps with OpenAI’s Agent SDK Creating multi-agent apps is simple with this open-source SDK, and it can be used with any OpenAI-compatible LLM The post Build Multi-Agent Apps with OpenAI’s Agent SDK appeared first on Towards Data Science. Alan Jones Go to original source

  • A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control

    A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control Your very own SQL assistant built with Streamlit, SQLite, & CrewAI The post A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control appeared first on Towards Data Science. Alle Sravani Go to original source

  • Momentum Multi-Marginal Schr”odinger Bridge Matching

    Momentum Multi-Marginal Schr”odinger Bridge Matching arXiv:2506.10168v1 Announce Type: new Abstract: Understanding complex systems by inferring trajectories from sparse sample snapshots is a fundamental challenge in a wide range of domains, e.g., single-cell biology, meteorology, and economics. Despite advancements in Bridge and Flow matching frameworks, current methodologies rely on pairwise interpolation between adjacent snapshots. This hinders…

  • Agentic AI 103: Building Multi-Agent Teams

    Agentic AI 103: Building Multi-Agent Teams Build multi-agent teams that can automate tasks and enhance productivity. The post Agentic AI 103: Building Multi-Agent Teams appeared first on Towards Data Science. Gustavo Santos Go to original source

  • MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements

    MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements arXiv:2506.02260v1 Announce Type: new Abstract: The growing prevalence of digital health technologies has led to the generation of complex multi-modal data, such as physical activity measurements simultaneously collected from various sensors of mobile and wearable devices. These data hold immense potential for advancing health studies, but current…

  • Multi-Agent Communication with the A2A Python SDK

    Multi-Agent Communication with the A2A Python SDK The Agent Card helps discover agents, but how does communication between agents actually work in practice? The post Multi-Agent Communication with the A2A Python SDK appeared first on Towards Data Science. Deborah Mesquita Go to original source

  • Optimizing Multi-Objective Problems with Desirability Functions

    Optimizing Multi-Objective Problems with Desirability Functions When working in Data Science, it is not uncommon to encounter problems with competing objectives. Whether designing products, tuning algorithms or optimizing portfolios, we often need to balance several metrics to get the best possible outcome. Sometimes, maximizing one metrics comes at the expense of another, making it hard…

  • Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties

    Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties arXiv:2505.11984v1 Announce Type: new Abstract: We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models,…

  • Learning Multi-Attribute Differential Graphs with Non-Convex Penalties

    Learning Multi-Attribute Differential Graphs with Non-Convex Penalties arXiv:2505.09748v1 Announce Type: new Abstract: We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with non-convex penalties. The GGM structure is encoded in its precision (inverse covariance) matrix. Existing methods…

  • Causal rule ensemble approach for multi-arm data

    Causal rule ensemble approach for multi-arm data arXiv:2504.17166v1 Announce Type: new Abstract: Heterogeneous treatment effect (HTE) estimation is critical in medical research. It provides insights into how treatment effects vary among individuals, which can provide statistical evidence for precision medicine. While most existing methods focus on binary treatment situations, real-world applications often involve multiple interventions.…

  • Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

    Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations arXiv:2504.11610v1 Announce Type: new Abstract: Background: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only…

  • Improving the evaluation of samplers on multi-modal targets

    Improving the evaluation of samplers on multi-modal targets arXiv:2504.08916v1 Announce Type: new Abstract: Addressing multi-modality constitutes one of the major challenges of sampling. In this reflection paper, we advocate for a more systematic evaluation of samplers towards two sources of difficulty that are mode separation and dimension. For this, we propose a synthetic experimental setting…

  • Survey on Algorithms for multi-index models

    Survey on Algorithms for multi-index models arXiv:2504.05426v1 Announce Type: new Abstract: We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases,…

  • Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements

    Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements arXiv:2503.02437v1 Announce Type: new Abstract: This paper addresses the challenge of allocating heterogeneous resources among multiple agents in a decentralized manner. Our proposed method, LGTC-IPPO, builds upon Independent Proximal Policy Optimization (IPPO) by integrating dynamic cluster consensus, a mechanism that allows agents to form…

  • Multiple Linked Tensor Factorization

    Multiple Linked Tensor Factorization arXiv:2502.20286v1 Announce Type: new Abstract: In biomedical research and other fields, it is now common to generate high content data that are both multi-source and multi-way. Multi-source data are collected from different high-throughput technologies while multi-way data are collected over multiple dimensions, yielding multiple tensor arrays. Integrative analysis of these data…

  • Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

    Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data arXiv:2502.18756v1 Announce Type: new Abstract: Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., multi-omics) that demand integrative analysis. Existing canonical correlation analysis (CCA) and generalized CCA methods address at most two of the following three key aspects simultaneously: (i) nonlinear dependence, (ii) sparsity for…

  • Multi-View Oriented GPLVM: Expressiveness and Efficiency

    Multi-View Oriented GPLVM: Expressiveness and Efficiency arXiv:2502.08253v1 Announce Type: new Abstract: The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral…

  • Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks

    Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks arXiv:2501.17882v1 Announce Type: new Abstract: We consider a multi-player multi-armed bandit setting in the presence of adversaries that attempt to negatively affect the rewards received by the players in the system. The reward distributions for any given arm are heterogeneous across the players. In the event of…

  • Sequential Learning of the Pareto Front for Multi-objective Bandits

    Sequential Learning of the Pareto Front for Multi-objective Bandits arXiv:2501.17513v1 Announce Type: new Abstract: We study the problem of sequential learning of the Pareto front in multi-objective multi-armed bandits. An agent is faced with K possible arms to pull. At each turn she picks one, and receives a vector-valued reward. When she thinks she has…

  • Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis

    Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis arXiv:2501.16768v1 Announce Type: new Abstract: Multiview learning has drawn widespread attention for its efficacy in leveraging cross-view consensus and complementarity information to achieve a comprehensive representation of data. While multi-view learning has undergone vigorous development and achieved remarkable success, the theoretical understanding of its generalization behavior…

  • Multi-Headed Cross Attention — By Hand

    Multi-Headed Cross Attention — By Hand Hand computing a fundamental component of multimodal models Continue reading on Towards Data Science » Daniel Warfield Go to original source

  • Multi-Output Conformal Regression: A Unified Comparative Study with New Conformity Scores

    Multi-Output Conformal Regression: A Unified Comparative Study with New Conformity Scores arXiv:2501.10533v1 Announce Type: new Abstract: Quantifying uncertainty in multivariate regression is essential in many real-world applications, yet existing methods for constructing prediction regions often face limitations such as the inability to capture complex dependencies, lack of coverage guarantees, or high computational cost. Conformal prediction…

  • Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph

    Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Continue reading on Towards Data Science » Youness Mansar Go to original source

  • Robust Multi-Dimensional Scaling via Accelerated Alternating Projections

    Robust Multi-Dimensional Scaling via Accelerated Alternating Projections arXiv:2501.02208v1 Announce Type: new Abstract: We consider the robust multi-dimensional scaling (RMDS) problem in this paper. The goal is to localize point locations from pairwise distances that may be corrupted by outliers. Inspired by classic MDS theories, and nonconvex works for the robust principal component analysis (RPCA) problem,…

  • Multi-Agentic RAG with Hugging Face Code Agents

    Multi-Agentic RAG with Hugging Face Code Agents Using Qwen2.5–7B-Instruct powered code agents to create a local, open source, multi-agentic RAG system Photo by Jaredd Craig on Unsplash Large Language Models have shown impressive capabilities and they are still undergoing steady improvements with each new generation of models released. Applications such as chatbots and summarisation can directly exploit…

  • Fast Multi-Group Gaussian Process Factor Models

    Fast Multi-Group Gaussian Process Factor Models arXiv:2412.16773v1 Announce Type: new Abstract: Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending Gaussian…