Search: Bandit - ai.jp.net

research #agent 📝 BlogAnalyzed: Jan 18, 2026 02:00

Deep Dive into Contextual Bandits: A Practical Approach

Published:Jan 18, 2026 01:56

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1 min read

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Qiita ML

Analysis

This article offers a fantastic introduction to contextual bandit algorithms, focusing on practical implementation rather than just theory! It explores LinUCB and other hands-on techniques, making it a valuable resource for anyone looking to optimize web applications using machine learning.

Key Takeaways

•Explores the use of Contextual Bandit algorithms for web optimization.
•Implements algorithms not initially covered in a specific textbook to enhance comprehension.
•Focuses on LinUCB, a prominent contextual bandit technique.

Reference

“The article aims to deepen understanding by implementing algorithms not directly included in the referenced book.”

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Research Paper #Machine Learning, Bandits, Network Learning 🔬 ResearchAnalyzed: Jan 3, 2026 06:18

Semi-overlapping Multi-bandit for Support Network Learning

Published:Dec 31, 2025 16:42

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1 min read

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ArXiv

Analysis

This paper introduces a novel framework, Sequential Support Network Learning (SSNL), to address the problem of identifying the best candidates in complex AI/ML scenarios where evaluations are shared and computationally expensive. It proposes a new pure-exploration model, the semi-overlapping multi-bandit (SOMMAB), and develops a generalized GapE algorithm with improved error bounds. The work's significance lies in providing a theoretical foundation and performance guarantees for sequential learning tools applicable to various learning problems like multi-task learning and federated learning.

Key Takeaways

•Introduces Sequential Support Network Learning (SSNL) for identifying best candidates in shared evaluation scenarios.
•Proposes the semi-overlapping multi-bandit (SOMMAB) model.
•Develops a generalized GapE algorithm with improved error bounds.
•Provides theoretical foundation and performance guarantees for sequential learning tools in various applications (MTL, ATL, FL, MAS).

Reference

“The paper introduces the semi-overlapping multi-(multi-armed) bandit (SOMMAB), in which a single evaluation provides distinct feedback to multiple bandits due to structural overlap among their arms.”

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Paper #Multi-Task Learning, Bandit Algorithms, Knowledge Transfer 🔬 ResearchAnalyzed: Jan 3, 2026 08:46

BandiK: Efficient Multi-Task Learning with Multi-Bandits

Published:Dec 31, 2025 08:25

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of efficient auxiliary task selection in multi-task learning, a crucial aspect of knowledge transfer, especially relevant in the context of foundation models. The core contribution is BandiK, a novel method using a multi-bandit framework to overcome the computational and combinatorial challenges of identifying beneficial auxiliary task sets. The paper's significance lies in its potential to improve the efficiency and effectiveness of multi-task learning, leading to better knowledge transfer and potentially improved performance in downstream tasks.

Key Takeaways

•Proposes BandiK, a novel three-stage multi-task auxiliary task subset selection method.
•Utilizes a multi-bandit framework to efficiently evaluate candidate auxiliary task sets.
•Addresses the computational and combinatorial challenges of multi-task learning.
•Aims to improve knowledge transfer and downstream task performance.

Reference

“BandiK employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits.”

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Research Paper #Bandit Algorithms, Machine Learning, Dimensionality Reduction 🔬 ResearchAnalyzed: Jan 3, 2026 08:49

Optimal Single-Index Bandit Algorithm Overcoming Dimensionality Curse

Published:Dec 31, 2025 06:48

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1 min read

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ArXiv

Analysis

This paper presents a novel single-index bandit algorithm that addresses the curse of dimensionality in contextual bandits. It provides a non-asymptotic theory, proves minimax optimality, and explores adaptivity to unknown smoothness levels. The work is significant because it offers a practical solution for high-dimensional bandit problems, which are common in real-world applications like recommendation systems. The algorithm's ability to adapt to unknown smoothness is also a valuable contribution.

Key Takeaways

•Proposes a single-index bandit algorithm for high-dimensional contextual bandits.
•Proves minimax optimality, overcoming the curse of dimensionality.
•Addresses adaptivity to unknown smoothness levels.
•Provides a phase transition analysis as the dimension increases.

Reference

“The algorithm achieves minimax-optimal regret independent of the ambient dimension $d$, thereby overcoming the curse of dimensionality.”

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Paper #LLM 🔬 ResearchAnalyzed: Jan 3, 2026 18:34

BOAD: Hierarchical SWE Agents via Bandit Optimization

Published:Dec 29, 2025 17:41

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of single-agent LLM systems in complex software engineering tasks by proposing a hierarchical multi-agent approach. The core contribution is the Bandit Optimization for Agent Design (BOAD) framework, which efficiently discovers effective hierarchies of specialized sub-agents. The results demonstrate significant improvements in generalization, particularly on out-of-distribution tasks, surpassing larger models. This work is important because it offers a novel and automated method for designing more robust and adaptable LLM-based systems for real-world software engineering.

Key Takeaways

Reference

“BOAD outperforms single-agent and manually designed multi-agent systems. On SWE-bench-Live, featuring more recent and out-of-distribution issues, our 36B system ranks second on the leaderboard at the time of evaluation, surpassing larger models such as GPT-4 and Claude.”

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Paper #Graph Neural Networks, Machine Learning, Sampling Techniques 🔬 ResearchAnalyzed: Jan 3, 2026 20:06

BLISS: Efficient GNN Training with Adaptive Node Sampling

Published:Dec 26, 2025 21:25

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1 min read

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ArXiv

Analysis

This paper addresses the computational bottleneck of training Graph Neural Networks (GNNs) on large graphs. The core contribution is BLISS, a novel Bandit Layer Importance Sampling Strategy. By using multi-armed bandits, BLISS dynamically selects the most informative nodes at each layer, adapting to evolving node importance. This adaptive approach distinguishes it from static sampling methods and promises improved performance and efficiency. The integration with GCNs and GATs demonstrates its versatility.

Key Takeaways

•BLISS introduces a novel bandit-based sampling strategy for GNN training.
•It dynamically selects informative nodes, adapting to node importance.
•BLISS integrates with GCNs and GATs, demonstrating versatility.
•Experiments show BLISS maintains or exceeds full-batch training accuracy.

Reference

“BLISS adapts to evolving node importance, leading to more informed node selection and improved performance.”

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Research Paper #LoRa Networks, Multi-Armed Bandit, Resource Allocation, Dynamic Environments, Energy Efficiency 🔬 ResearchAnalyzed: Jan 3, 2026 16:32

SIC-Aided Bandit for Dynamic LoRa Resource Allocation

Published:Dec 26, 2025 17:27

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of dynamic environments in LoRa networks by proposing a distributed learning method for transmission parameter selection. The integration of the Schwarz Information Criterion (SIC) with the Upper Confidence Bound (UCB1-tuned) algorithm allows for rapid adaptation to changing communication conditions, improving transmission success rate and energy efficiency. The focus on resource-constrained devices and the use of real-world experiments are key strengths.

Key Takeaways

•Proposes a distributed learning method for transmission parameter selection in LoRa networks.
•Integrates Schwarz Information Criterion (SIC) with UCB1-tuned to adapt to dynamic environments.
•Improves transmission success rate and energy efficiency.
•Designed for resource-constrained LoRa End Devices (EDs).
•Validated with real LoRa device experiments.

Reference

“The proposed method achieves superior transmission success rate, energy efficiency, and adaptability compared with the conventional UCB1-tuned algorithm without SIC.”

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Research #Bandits 🔬 ResearchAnalyzed: Jan 10, 2026 07:16

Novel Bandit Algorithm for Probabilistically Triggered Arms

Published:Dec 26, 2025 08:42

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1 min read

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ArXiv

Analysis

This research explores a novel approach to the Multi-Armed Bandit problem, focusing on arms that are triggered probabilistically. The paper likely details a new algorithm, potentially with applications in areas like online advertising or recommendation systems where actions have uncertain outcomes.

Key Takeaways

•Focuses on a specific variant of the Multi-Armed Bandit problem.
•Addresses the challenge of arms that trigger with uncertainty.
•Potentially introduces a new algorithm for improved decision-making.

Reference

“The article's source is ArXiv.”

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Research #Bandits 🔬 ResearchAnalyzed: Jan 10, 2026 07:21

Prioritized Arm Capacity Sharing in Multi-Play Stochastic Bandits

Published:Dec 25, 2025 11:19

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1 min read

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ArXiv

Analysis

This ArXiv paper explores a novel approach to the multi-armed bandit problem, specifically addressing the challenge of allocating resources (arm capacity) in a prioritized manner. The research potentially contributes to more efficient resource allocation in scenarios with multiple competing options.

Key Takeaways

•Addresses the problem of resource allocation in multi-armed bandit scenarios.
•Introduces a prioritized approach to arm capacity sharing.
•Potentially improves efficiency in scenarios with multiple options.

Reference

“The paper focuses on multi-play stochastic bandits with prioritized arm capacity sharing.”

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Research #Raft 🔬 ResearchAnalyzed: Jan 10, 2026 07:39

BALLAST: Improving Raft Consensus with AI for Latency-Aware Timeouts

Published:Dec 24, 2025 13:25

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1 min read

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ArXiv

Analysis

This research explores the application of bandit-assisted learning to optimize timeouts in the Raft consensus algorithm, addressing latency issues. The paper's novelty lies in its use of reinforcement learning to dynamically adjust timeouts, potentially enhancing the performance of distributed systems.

Key Takeaways

•Applies bandit-assisted learning to Raft.
•Addresses latency issues in distributed systems.
•Aims to improve timeout stability and performance.

Reference

“The research focuses on latency-aware stable timeouts in the Raft consensus algorithm.”

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Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 04:31

Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability

Published:Dec 24, 2025 05:00

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1 min read

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ArXiv Stats ML

Analysis

This ArXiv paper addresses a critical challenge in contextual bandit algorithms: the \

Key Takeaways

•Adaptive sampling in contextual bandits can lead to inflated confidence intervals.
•The Lai-Wei stability condition allows for valid inference without the usual price of adaptivity.
•A penalized EXP4 algorithm is proposed that satisfies the stability condition and achieves minimax optimal regret.

Reference

“When stability holds, the ordinary least-squares estimator satisfies a central limit theorem, and classical Wald-type confidence intervals -- designed for i.i.d. data -- become asymptotically valid even under adaptation, \emph{without} incurring the $\\sqrt{d \\log T}$ price of adaptivity.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:32

Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability

Published:Dec 23, 2025 13:53

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1 min read

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ArXiv

Analysis

This article, sourced from ArXiv, likely presents a research paper. The title suggests a focus on improving the efficiency of inference within the framework of linear contextual bandits. The phrase "price of adaptivity" hints at a trade-off, possibly between exploration and exploitation, or computational cost and performance. The use of "stability" suggests a novel approach to address this trade-off, potentially by improving the robustness or convergence of the inference process.

Key Takeaways

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:05

Information-directed sampling for bandits: a primer

Published:Dec 23, 2025 06:49

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1 min read

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ArXiv

Analysis

This article is a primer on information-directed sampling for bandit problems. It likely introduces the concept and provides a basic understanding of the technique. The source being ArXiv suggests it's a research paper, focusing on a specific area within reinforcement learning.

Key Takeaways

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:13

QoS-Aware Load Balancing in the Computing Continuum via Multi-Player Bandits

Published:Dec 21, 2025 23:18

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1 min read

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ArXiv

Analysis

This article likely presents a research paper exploring the application of multi-player bandit algorithms for load balancing in a computing continuum, focusing on Quality of Service (QoS) considerations. The use of 'computing continuum' suggests a distributed computing environment, and the focus on QoS implies an effort to optimize performance and resource allocation. The 'multi-player bandits' approach likely involves multiple agents competing for resources, with the algorithm learning to allocate resources effectively based on feedback.

Key Takeaways

Reference

“”

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Research #Bandits 🔬 ResearchAnalyzed: Jan 10, 2026 09:10

Unifying Regret Analysis for Optimism Bandit Algorithms

Published:Dec 20, 2025 16:11

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1 min read

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ArXiv

Analysis

This research paper, originating from ArXiv, focuses on a significant aspect of reinforcement learning: regret analysis in optimism-based bandit algorithms. The unifying theorem proposed potentially simplifies and broadens the understanding of these algorithms' performance.

Key Takeaways

•The paper presents a unifying theorem for the regret analysis of optimism-based bandit algorithms.
•This could lead to a simpler and more general understanding of algorithm performance.
•The research likely contributes to advancements in reinforcement learning theory.

Reference

“The paper focuses on regret analysis of optimism bandit algorithms.”

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Research #Spectrum 🔬 ResearchAnalyzed: Jan 10, 2026 09:48

AI for Stable Spectrum Sharing: A Distributed Learning Approach

Published:Dec 19, 2025 01:43

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1 min read

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ArXiv

Analysis

This ArXiv article likely presents a novel approach to spectrum sharing using distributed learning, specifically addressing the challenges of Markovian restless bandits in interference graphs. The research probably focuses on improving the stability and efficiency of wireless communication by optimizing spectrum allocation.

Key Takeaways

•Applies distributed learning techniques.
•Addresses challenges in spectrum sharing.
•Focuses on stability and efficiency.

Reference

“The article's context suggests the research focuses on distributed learning within the framework of Markovian restless bandits and interference graphs.”

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Research #Bandits 🔬 ResearchAnalyzed: Jan 10, 2026 10:36

Self-Driving Microscopies: Applying Restless Bandits to Enhance Image Acquisition

Published:Dec 16, 2025 21:42

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1 min read

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ArXiv

Analysis

This research paper explores the application of Restless Multi-Process Multi-Armed Bandits to optimize the image acquisition process in self-driving microscopies. The paper's contribution likely lies in the novel application of a bandit algorithm to a practical problem with a focus on automation and efficiency.

Key Takeaways

•Applies Restless Multi-Process Multi-Armed Bandits to self-driving microscopies.
•Focuses on optimizing image acquisition through algorithmic decision-making.
•Presented as a research paper available on ArXiv.

Reference

“The research is published on ArXiv, indicating it's a pre-print or early-stage research.”

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Research #Bandits 🔬 ResearchAnalyzed: Jan 10, 2026 11:23

Novel Multi-Task Bandit Algorithm Explores and Exploits Shared Structure

Published:Dec 14, 2025 13:56

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1 min read

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ArXiv

Analysis

This research paper explores a novel approach to multi-task bandit problems by leveraging shared structure. The focus on co-exploration and co-exploitation offers potential advancements in areas where multiple related tasks need to be optimized simultaneously.

Key Takeaways

•Addresses the challenge of optimizing multiple related tasks simultaneously.
•Focuses on leveraging shared structure to improve exploration and exploitation.
•Potential for applications in various fields requiring multi-objective optimization.

Reference

“The paper investigates co-exploration and co-exploitation via shared structure in Multi-Task Bandits.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:53

Fast EXP3 Algorithms

Published:Dec 12, 2025 01:18

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1 min read

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ArXiv

Analysis

The article likely discusses improvements or optimizations to the EXP3 algorithm, a common algorithm used in reinforcement learning and online learning for the multi-armed bandit problem. The focus is on achieving faster performance.

Key Takeaways

Reference

“”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:33

Conformal Bandits: Bringing statistical validity and reward efficiency to the small-gap regime

Published:Dec 10, 2025 17:34

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1 min read

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ArXiv

Analysis

This article likely discusses a new approach to multi-armed bandit problems, focusing on improving performance in scenarios where the differences between the rewards of different actions are small. The use of "conformal" suggests a connection to conformal prediction, potentially offering guarantees on the validity of the chosen actions. The focus on statistical validity and reward efficiency indicates a focus on both the reliability and the speed of learning.

Key Takeaways

Reference

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Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 14:16

OVOD-Agent: A Novel Framework for Proactive Visual Reasoning and Adaptive Object Detection

Published:Nov 26, 2025 05:08

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1 min read

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ArXiv

Analysis

The article likely introduces a new AI framework, OVOD-Agent, leveraging a Markov-Bandit approach for visual reasoning and object detection. Further analysis would require the actual content to assess its novelty, effectiveness, and potential impact on computer vision.

Key Takeaways

•The research focuses on proactive visual reasoning, which implies anticipating and planning future observations.
•Self-evolving detection suggests the model can adapt and improve its object detection capabilities over time.
•The use of a Markov-Bandit framework suggests an approach combining reinforcement learning with Bayesian methods.

Reference

“OVOD-Agent is a Markov-Bandit Framework for Proactive Visual Reasoning and Self-Evolving Detection.”

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Research #AI Theory 📝 BlogAnalyzed: Dec 29, 2025 07:45

A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck - #551

Published:Jan 10, 2022 17:23

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1 min read

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Practical AI

Analysis

This article summarizes an interview from the "Practical AI" podcast featuring Sebastien Bubeck, a Microsoft research manager and author of a NeurIPS 2021 award-winning paper. The conversation covers convex optimization, its applications to problems like multi-armed bandits and the K-server problem, and Bubeck's research on the necessity of overparameterization for data interpolation across various data distributions and model classes. The interview also touches upon the connection between the paper's findings and the work in adversarial robustness. The article provides a high-level overview of the topics discussed.

Key Takeaways

•The interview focuses on Sebastien Bubeck's research on robustness in machine learning.
•The discussion covers convex optimization and its applications.
•The paper explores the relationship between overparameterization and data interpolation.

Reference

“We explore the problem that convex optimization is trying to solve, the application of convex optimization to multi-armed bandit problems, metrical task systems and solving the K-server problem.”

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Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 12:49

BanditPAM: Almost Linear-Time k-medoids Clustering via Multi-Armed Bandits

Published:Dec 17, 2021 08:00

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1 min read

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Stanford AI

Analysis

This article announces the public release of BanditPAM, a new k-medoids clustering algorithm developed at Stanford AI. The key advantage of BanditPAM is its speed, achieving O(n log n) complexity compared to the O(n^2) of previous algorithms. This makes k-medoids, which offers benefits like interpretable cluster centers and robustness to outliers, more practical for large datasets. The article highlights the ease of use, with a simple pip install and an interface similar to scikit-learn's KMeans. The availability of a video summary, PyPI package, GitHub repository, and full paper further enhances accessibility and encourages adoption by ML practitioners. The comparison to k-means is helpful for understanding the context and motivation behind the work.

Key Takeaways

•BanditPAM is a new, faster k-medoids clustering algorithm.
•It offers improved speed (O(n log n)) compared to previous k-medoids algorithms.
•It's easy to install and use, with a scikit-learn-like interface.

Reference

“In k-medoids, however, we require that the cluster centers must be actual datapoints, which permits greater interpretability of the cluster centers.”

Permalink Stanford AI

Research #Machine Learning 📝 BlogAnalyzed: Jan 3, 2026 07:17

Multi-Armed Bandits and Pure-Exploration

Published:Nov 20, 2020 20:36

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1 min read

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ML Street Talk Pod

Analysis

This article summarizes a podcast episode discussing multi-armed bandits and pure exploration, focusing on the work of Dr. Wouter M. Koolen. The episode explores the concepts of exploration vs. exploitation in decision-making, particularly in the context of reinforcement learning and game theory. It highlights Koolen's expertise in machine learning theory and his research on pure exploration, including its applications and future directions.

Key Takeaways

•The podcast episode focuses on multi-armed bandits and pure exploration.
•Dr. Wouter M. Koolen is a key researcher in this area.
•The discussion covers exploration vs. exploitation in decision-making.
•Connections to reinforcement learning and game theory are explored.
•The episode touches on applications and future directions of pure exploration.

Reference

“The podcast discusses when an agent can stop learning and start exploiting knowledge, and which strategy leads to minimal learning time.”

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Research #llm 📝 BlogAnalyzed: Dec 29, 2025 08:18

Holistic Optimization of the LinkedIn News Feed - TWiML Talk #224

Published:Jan 28, 2019 16:28

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1 min read

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Practical AI

Analysis

This article discusses the optimization of the LinkedIn news feed, focusing on a holistic approach. It features an interview with Tim Jurka, Head of Feed AI at LinkedIn, and covers technical and business challenges. The conversation delves into specific techniques like Multi-arm Bandits and Content Embeddings, and also explores the organizational aspects of machine learning at scale. The article promises insights into how LinkedIn approaches feed optimization, offering a look at the practical application of AI in a real-world context.

Key Takeaways

•The article discusses the holistic optimization of the LinkedIn news feed.
•It highlights the use of techniques like Multi-arm Bandits and Content Embeddings.
•The conversation touches upon organizing for machine learning at scale.

Reference

“The article doesn't contain a specific quote, but rather a description of the conversation.”

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Research #llm 📝 BlogAnalyzed: Dec 29, 2025 08:43

Interactive Machine Learning Systems with Alekh Agarwal - TWiML Talk #17

Published:Mar 31, 2017 15:59

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1 min read

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Practical AI

Analysis

This article summarizes a podcast episode featuring Alekh Agarwal, a researcher at Microsoft Research, discussing Interactive Machine Learning (IML). The discussion covers key aspects of IML, including active learning, reinforcement learning, and contextual bandits. The focus is on exploring the research landscape of IML, highlighting its various components and potential applications. The article serves as an introduction to the topic, providing a glimpse into the ongoing research and the areas being explored within the field of interactive machine learning.

Key Takeaways

•The article highlights the importance of Interactive Machine Learning.
•It mentions key areas within IML like active learning and reinforcement learning.
•The discussion is based on a podcast interview with a researcher from Microsoft Research.

Reference

“Alekh and I discuss various aspects of this exciting area of research such as active learning, reinforcement learning, contextual bandits and more.”

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Research #machine learning 📝 BlogAnalyzed: Dec 29, 2025 08:44

Xavier Amatriain - Engineering Practical Machine Learning Systems - TWiML Talk #3

Published:Aug 28, 2016 23:26

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1 min read

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Practical AI

Analysis

This article summarizes a podcast interview with Xavier Amatriain, a prominent figure in the machine learning field. The interview covers his experiences at Netflix, where he led the machine learning recommendations team, and his current role as VP of Engineering at Quora. The discussion delves into practical aspects of building machine learning systems, including the reasons behind Netflix's decision not to use the winning solution of the Netflix Prize, the challenges of engineering practical systems, Amatriain's skepticism towards the deep learning hype, and an explanation of multi-arm bandits. The article provides a glimpse into the real-world application of machine learning and the considerations involved in deploying such systems.

Key Takeaways

•The interview highlights the practical challenges of implementing machine learning solutions in real-world scenarios.
•It touches upon the importance of considering factors beyond pure algorithmic performance, such as system engineering and deployment.
•The discussion offers insights into the evolution of machine learning applications and the perspectives of experienced practitioners.

Reference

“Why Netflix invested $1 million in the Netflix Prize, but didn’t use the winning solution”

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