On-device research index

arXiv research

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.

168,742 papers · 148 categories

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48 results for “social bandit”

Bandits Under The Influence (Extended Version)cs.LG

New bandit algorithms adapt to evolving user interests influenced by social circles.

problem Adapting to evolving user interests in recommendation systems.

method Online recommendation algorithms tailored for social influence, based on LinREL and Thompson Sampling.

result Our adaptations maintain asymptotic regret bounds similar to non-social cases.

bandits recommendation systems user interests social influence asymptotic regret

2020-09-21abs ↗pdf ↗

Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approachcs.LG

Algorithm improves learning by integrating diverse agents' behaviors.

problem Lack of social learning in reinforcement learning algorithms.

method Free energy approach for social bandit learning.

result Algorithm converges to optimal policy and enhances learning.

reinforcement learning social bandit expertise integration free energy logarithmic regret

2026-03-12abs ↗pdf ↗

Semi-Parametric Contextual Bandits with Graph-Laplacian Regularizationstat.ML

Proposes a new algorithm for graph-based semi-parametric contextual bandits.

problem Non-stationarity in human behavior and social interaction.

method SemiGraphTS algorithm for graph-based semi-parametric reward model.

result Derives an upper bound of cumulative regret for graph-based semi-parametric model.

contextual bandits graph-based semi-parametric Thompson sampling non-stationarity

2022-05-17abs ↗pdf ↗

Automatic Ensemble Learning for Online Influence Maximizationcs.LG

Aims to optimize influence spread in social networks using bandit algorithms.

problem Maximizing influence spread in unknown social networks.

method Combines Thompson Sampling and Epsilon Greedy algorithms with automatic ensemble learning.

result Demonstrates effectiveness of automatic ensemble learning for combinatorial bandit problems.

influence maximization social networks bandit algorithms ensemble learning semi-bandit feedback

2019-11-25abs ↗pdf ↗

Interactive Restless Multi-armed Bandit Game and Swarm Intelligence Effectcs.AI

We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability $p_{c}$ per round. The agents and player choose one of three options: (1) Exploit (a …

2015-03-13abs ↗pdf ↗

Learning Fair Division from Bandit Feedbackcs.LG

Algorithm learns fair division from noisy feedback in uncertain markets.

problem Learning fair division in uncertain markets with noisy feedback.

method Wrapper algorithms using dual averaging to learn item and agent values from bandit feedback.

result Asymptotically achieves optimal Nash social welfare in linear Fisher markets.

fair division online learning bandit feedback Nash social welfare Fisher markets

2023-11-15abs ↗pdf ↗

Incentivising Exploration and Recommendations for Contextual Bandits with Paymentscs.LG

Proposes a model to incentivize exploration in web platforms with payments.

problem Learning and social welfare goals of web platforms with myopic users.

method Contextual bandit model with payments to incentivize exploration.

result Achieves sublinear regret while maximizing cumulative social welfare.

contextual bandits incentivization web platforms myopic users cumulative social welfare

2020-01-22abs ↗pdf ↗

On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approachecon.TH

The study uses a multi-armed bandit model to analyze and mitigate hiring discrimination.

problem Hiring discrimination due to insufficient data on worker skill and characteristics.

method Multi-armed bandit model to simulate firms' learning process and policy solutions.

result Temporary affirmative actions effectively alleviate discrimination caused by data insufficiency.

hiring discrimination multi-armed bandit affirmative action social learning

2020-10-02abs ↗pdf ↗

A Gang of Banditscs.LG

Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems. In many cases, however, these applications have a…

2013-06-04abs ↗pdf ↗

Contextual Bandits with Side-Observationscs.LG

A new algorithm for social network recommendations using side-observations.

problem Designing recommendation algorithms for users influenced by their social network.

method Contextual bandits with side-observations modeled by a social network graph.

result The proposed algorithm achieves asymptotically optimal regret, matching the lower-bound as

T o \infty

contextual bandits social networks recommendation systems graph theory regret minimization

2020-06-06abs ↗pdf ↗

DP-NCB: Privacy Preserving Fair Banditscs.LG

DP-NCB algorithm ensures privacy and fairness in bandit decisions.

problem Achieving both privacy and fairness in bandit algorithms.

method Differentially Private Nash Confidence Bound (DP-NCB) framework.

result DP-NCB achieves optimal Nash regret while maintaining privacy.

Privacy Preserving Fairness Bandits Differential Privacy Nash Regret

2025-08-05abs ↗pdf ↗

Combinatorial Stochastic-Greedy Banditcs.LG

New algorithm for combinatorial bandit problems reduces regret.

problem Optimal selection of sets of arms in bandit problems.

method SGB algorithm with optimized exploration of unselected arms.

result Achieves

(1-1/e)

-regret bound of

\mathcal{O}(n^{\frac{1}{3}} k^{\frac{2}{3}} T^{\frac{2}{3}} \log(T)^{\frac{2}{3}})

bandit problems stochastic-greedy social influence maximization submodular rewards regret bound

2023-12-13abs ↗pdf ↗

Combinatorial Rising Banditscs.LG

CRB tackles rising rewards in combinatorial online learning.

problem Rising rewards in combinatorial online learning.

method CRB framework and CRUCB algorithm.

result Empirical and theoretical validation of CRUCB's effectiveness.

rising rewards combinatorial bandits upper confidence bound reinforcement learning sequential decision making

2024-12-01abs ↗pdf ↗

Budgeted Online Influence Maximizationcs.LG

New framework for online influencer selection considering cost constraints.

problem Real-world social advertising budget limitations and cost variability of influencers.

method Introduces a budgeted framework for online influence maximization using an algorithm with semi-bandit feedback.

result Improves the state of the art regret bound for cardinality constraint setting.

influence maximization social advertising budgeted independent cascade semi-bandit

2026-04-21abs ↗pdf ↗

Leveraging Side Observations in Stochastic Banditscs.LG

This paper considers stochastic bandits with side observations, a model that accounts for both the exploration/exploitation dilemma and relationships between arms. In this setting, after pulling an arm i, the decision maker also observes the rewards for some other actions related to i. We will see that this model is su…

2012-10-16abs ↗pdf ↗

A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible foodcs.LG

Algorithm improves resource allocation for food outreach to homeless.

problem Resource-constrained outreach for homeless individuals and food rescue.

method Thompson sampling with Markov chain recovery (via Stein variational gradient descent) for partially-observed episodic restless bandits.

result Significantly outperforms baselines in both organizations' problems.

Thompson sampling restless bandits resource-constrained outreach food rescue

2024-03-15abs ↗pdf ↗

A Correction of Pseudo Log-Likelihood Methodcs.LG

Corrects pseudo log-likelihood method issues in various applications.

problem Log-likelihood function unbounded issues in pseudo log-likelihood methods.

method Provided a counterexample and corrected algorithms in previous literature.

result Ensured well-definedness of maximum pseudo log-likelihood estimation.

pseudo log-likelihood maximum likelihood estimation contextual bandits influence maximization causal bandits

2024-03-26abs ↗pdf ↗

Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learningstat.ML

This study shows how social insects and machine learning methods share a common mathematical framework.

problem Understanding how decentralized systems achieve optimal decision-making.

method Developed a rigorous mathematical framework to show isomorphism between ant colonies and ensemble machine learning.

result Demonstrated that ant colony decision-making and random forest learning implement identical variance reduction strategies through decorrelation of identical units.

social insects ensemble machine learning stochastic ensemble intelligence random forest Bayesian inference

2026-03-20abs ↗pdf ↗

Stochastic Top-

K

Subset Bandits with Linear Space and Non-Linear Feedbackcs.LG

Many real-world problems like Social Influence Maximization face the dilemma of choosing the best $K$ out of $N$ options at a given time instant. This setup can be modeled as a combinatorial bandit which chooses $K$ out of $N$ arms at each time, with an aim to achieve an efficient trade-off between exploration and expl…

2018-11-29abs ↗pdf ↗

Collaborative Learning of Stochastic Bandits over a Social Networkcs.LG

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social n…

2016-02-29abs ↗pdf ↗

No-Regret Learning for Fair Multi-Agent Social Welfare Optimizationcs.LG

This paper tackles no-regret learning for fair multi-agent social welfare optimization.

problem Maximizing social welfare in a fair manner for multiple agents.

method Developed algorithms for stochastic and adversarial multi-agent settings, proving regret bounds and tightness.

result Achieved no-regret learning for fair multi-agent social welfare optimization in various settings.

no-regret learning fairness multi-agent social welfare regret bounds

2024-05-31abs ↗pdf ↗

Adaptive maximization of social welfareecon.EM

The paper tackles adaptive policy selection to maximize social welfare, achieving optimal regret bounds.

problem Maximizing social welfare through adaptive policy selection, considering both private utility and public revenue.

method The approach involves learning response functions through experimentation, deriving lower and upper bounds for regret, and using algorithms like Exp3.

result The algorithm achieves optimal regret bounds, showing that welfare maximization is harder than multi-armed bandit problems.

social welfare adaptive policy selection regret bounds Exp3 algorithm monopoly pricing

2023-10-14abs ↗pdf ↗

Bandit Simulation for Average Reward Inferencestat.ML

Proposes BSI for valid statistical inference on bandit algorithms.

problem Valid statistical inference on bandit algorithms' performance.

method Fits a simulator of the bandit environment from observed data and uses it to estimate mean reward under any policy.

result Proves asymptotically valid confidence intervals and maintains nominal coverage.

bandit algorithms inference confidence intervals simulator adaptive algorithms

2026-05-30abs ↗pdf ↗

Bandit Learning with Delayed Impact of Actionscs.LG

New algorithm for bandits with delayed action effects, reducing regret.

problem Delayed impact of actions in multi-armed bandits.

method Formulated a new bandit setting with delayed action effects, proposed an algorithm with regret bound.

result Achieved a regret of

ilde{\mathcal{O}}(KT^{2/3})

and showed a matching lower bound.

bandit learning delayed actions multi-armed bandits regret minimization fair algorithms

2020-02-24abs ↗pdf ↗

Robust Multi-Agent Multi-Armed Banditscs.LG

New algorithm reduces regret in multi-agent bandits with malicious agents.

problem Collaboration between honest and malicious agents in multi-armed bandits.

method Dynamic reduction of communication with malicious agents, learning who is malicious.

result Algorithm reduces regret even with a single malicious agent, assuming

m

is small compared to

K

multi-agent multi-armed bandits malicious agents regret reduction dynamic communication

2020-07-07abs ↗pdf ↗

Online Competitive Influence Maximizationcs.LG

New algorithm for competing influence spread in unknown networks.

problem Maximizing influence spread in a social network with unknown probabilities.

method Combinatorial multi-armed bandit (CMAB) framework, Triggering Probability Modulated (TPM) condition, OCIM-TS, OCIM-OFU, OCIM-ETC.

result Sublinear Bayesian and frequentist regret for OCIM-TS and OCIM-OFU, respectively.

influence maximization social networks bandit algorithms monotonicity competitive influence

2020-06-24abs ↗pdf ↗

Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedbackcs.LG

We study the online influence maximization problem in social networks under the independent cascade model. Specifically, we aim to learn the set of "best influencers" in a social network online while repeatedly interacting with it. We address the challenges of (i) combinatorial action space, since the number of feasibl…

2016-05-21abs ↗pdf ↗

Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationalitycs.GT

Our work extends Coase's theorem to settings with uncertainty, showing how to maximize social welfare through property rights and learning.

problem Theoretical models of externality often assume perfect knowledge, limiting practical solutions.

method We extend Coase's theorem to a two-player bandit setting with uncertainty, designing a learning policy to maximize social welfare.

result We show that property rights and learning can recover Coase's theorem in settings with uncertainty.

Coase theorem externality property rights learning bargaining

2024-06-28abs ↗pdf ↗

A Batched Multi-Armed Bandit Approach to News Headline Testingcs.LG

Optimizing news headlines is important for publishers and media sites. A compelling headline will increase readership, user engagement and social shares. At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a…

2019-08-17abs ↗pdf ↗

Selfish Robustness and Equilibria in Multi-Player Banditscs.LG

New algorithm for multi-player bandits with selfish players, achieving logarithmic regret.

problem Challenges of robustness to selfish players in multi-player bandits.

method First algorithm robust to selfish players achieving logarithmic regret, with or without collision observation.

result Achieved logarithmic regret for robust algorithms to selfish players in multi-player bandits.

multi-player bandits selfish players logarithmic regret Nash equilibrium collision observation

2020-02-04abs ↗pdf ↗

Regret, stability & fairness in matching markets with bandit learnerscs.LG

Study shows how competition affects learning in matching markets, proving it's possible to balance stability, fairness, and regret.

problem How competition affects learning in matching markets and the impossibility of simultaneously guaranteeing stability and low optimal regret.

method Modeling a two-sided matching market with bandit learners and adding components of costs and transfers.

result It is possible to simultaneously guarantee stability, low optimal regret, fairness in the distribution of regret, and high social welfare.

matching markets bandit learners regret stability fairness

2021-02-11abs ↗pdf ↗

Exploring Partially Observed Networks with Nonparametric Banditsstat.ML

Real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topology, and nodes of the original network are unknown. In this paper we formalize the Adaptive Graph Exploring problem. We assume that we are …

2018-04-19abs ↗pdf ↗

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedbackstat.ML

Mechanism designs for unknown agent values in stochastic bandit settings.

problem Designing truthful mechanisms for maximizing social welfare in settings with unknown agent values and stochastic feedback.

method Developed a VCG-like mechanism with regret bounds for multi-round allocations, balancing agent and seller welfare.

result Achieved an $Ω(T^{rac{2}{3}})$ lower bound for the maximum of welfare, agent utilities, and mechanism utility after

T

rounds.

VCG mechanism stochastic bandit truthful mechanism regret bounds multi-round allocations

2020-04-19abs ↗pdf ↗

Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networkscs.LG

We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure. In our model, a bipartite graph captures the relationship between actions and a common set of unknowns such that choosing an action reveals observations…

2017-04-26abs ↗pdf ↗

Group Meritocratic Fairness in Linear Contextual Banditsstat.ML

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

problem Selecting candidates from different sensitive groups in a fair manner.

method A greedy policy that constructs a ridge regression estimate and computes relative rank using empirical cumulative distribution function.

result The greedy policy achieves fair pseudo-regret of order

\sqrt{dT}

after

T

rounds, satisfying demographic parity.

fairness linear contextual bandits hiring relative rank ridge regression

2022-06-07abs ↗pdf ↗

PHASE: PHysically-grounded Abstract Social Events for Machine Social Perceptioncs.AI

PHASE dataset simulates complex social interactions in physical environments.

problem Lack of datasets for evaluating physically grounded perception of complex social interactions.

method Created PHASE dataset of 2D animations with procedural generation and physics engine.

result SIMPLE model outperforms neural networks in recognizing complex social interactions.

social perception physical environments Bayesian inverse planning social recognition social prediction

2021-03-02abs ↗pdf ↗

Actively Learning to Attract Followers on Twitterstat.ML

Twitter, a popular social network, presents great opportunities for on-line machine learning research. However, previous research has focused almost entirely on learning from passively collected data. We study the problem of learning to acquire followers through normative user behavior, as opposed to the mass following…

2015-04-16abs ↗pdf ↗

Factorization Bandits for Online Influence Maximizationcs.LG

We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with it, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the i…

2019-06-09abs ↗pdf ↗

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendationcs.SI

Improved neural model for social recommendation by integrating social and interest networks.

problem Data sparsity and lack of higher-order relationships in social recommendation.

method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.

result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.

social recommendation neural networks interest diffusion influence diffusion user-item interest network

2020-01-15abs ↗pdf ↗

Decoding Digital Influence: The Role of Social Media Behavior in Scientific Stratification Through Logistic Attribution Methodcs.CY

Social media enhances or diminishes scientific status, depending on usage.

problem Impact of social media on scientific stratification and mobility.

method Logistic Attribution Analysis combining statistical and machine learning methods.

result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.

social media scientific stratification mobility logistic attribution machine learning

2024-07-23abs ↗pdf ↗

Social and individual learning in the Minority Gamephysics.soc-ph

Social learning can make financial markets inefficient, but individual learning can fix this.

problem Inefficiencies in financial markets due to social learning.

method Study of the Minority Game model with social and individual learning mechanisms.

result Individual learning can rescue a population from the inefficiencies caused by social learning.

Minority Game Social Learning Individual Learning Financial Market Inefficiency

2023-07-21abs ↗pdf ↗

Predicting Opinion Dynamics via Sociologically-Informed Neural Networkscs.SI

SINN combines social science and deep learning for predicting opinion dynamics.

problem Predicting opinion dynamics in social networks using traditional models requires extensive calibration with real data.

method SINN integrates theoretical models and social media data using physics-informed neural networks (PINNs) and matrix factorization.

result SINN outperforms six baseline methods in predicting opinion dynamics on real-world and synthetic datasets.

opinion dynamics social networks neural networks sociology deep learning

2022-07-07abs ↗pdf ↗

An Analytical Approach to (Meta)Relational Models Theory, and its Application to Triple Bottom Line (Profit, People, Planet) -- Towards Social Relations Portfolio Managementphysics.soc-ph

Optimizes social interactions for profit, people, and planet using mathematical models.

problem Determining the most effective social configurations for mutual objectives.

method Formulated as a mathematical optimization problem, using (meta)relational models theory.

result Identifies the most suitable combination of sociality forms for mutual objectives.

Social Interaction Optimization Relational Models Theory Triple Bottom Line Meta-Relational Models Social Relations Portfolio

2024-02-29abs ↗pdf ↗

Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crisesstat.ML

Model predicts increased social unrest during COVID-19 using social media data.

problem Detecting rising conflict potential in societies during pandemics.

method Neural implicit motive pattern recognition from social media texts.

result Significant increase in conflict indicators during the pandemic.

COVID-19 Social unrest Implicit motives Psychometrics Social media

2020-12-08abs ↗pdf ↗

Machine Learning for Socially Responsible Portfolio Optimisationq-fin.PM

Enhances traditional MV model for socially responsible investors.

problem Traditional MV models ignore ESG scores relevant to socially responsible investors.

method Implemented an amended MV model considering ESG scores.

result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.

portfolio optimization socially responsible investing ESG scores Mean-Variance model Sharpe Ratio

2023-05-21abs ↗pdf ↗

The ecology of social interactions in online and offline environmentscs.SI

The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…

2016-01-28abs ↗pdf ↗

Approximation Theory Based Methods for RKHS Banditscs.LG

New algorithms tackle RKHS bandits with reduced complexity and improved performance.

problem Adversarial and stochastic RKHS bandit problems with high computational complexity.

method Combining approximation theory with misspecified linear bandit methods.

result First general algorithm for adversarial RKHS bandit problem.

approximation theory RKHS bandits misspecified linear bandit stochastic bandits adversarial bandits

2020-10-23abs ↗pdf ↗

Bridging Adversarial and Nonstationary Multi-armed Banditcs.LG

Unified formulation bridges adversarial and nonstationary bandits.

problem Handling time-varying reward distributions in multi-armed bandit problems.

method Unified oracle that switches between adversarial and nonstationary bandit oracles based on window size.

result Optimal regret achieved with matching lower bound.

multi-armed bandit adversarial bandit nonstationary bandit oracle regret

2022-01-05abs ↗pdf ↗