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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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17 results for “Exploitation-Exploration”

Epsilon-Greedy Thompson Sampling to Bayesian Optimizationcs.LG

Improved Thompson Sampling for Bayesian Optimization.

problem Handling the exploitation-exploration dilemma in Bayesian optimization.

method Incorporating epsilon-greedy policy into Thompson Sampling.

result Epsilon-greedy Thompson Sampling outperforms standard TS extremes.

Thompson Sampling Bayesian Optimization Epsilon-Greedy Reinforcement Learning Exploitation-Exploration

2024-03-01abs ↗pdf ↗

Probabilistic Knowledge Graph Construction: Compositional and Incremental Approachesstat.ML

Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinde…

2016-08-21abs ↗pdf ↗

Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systemsstat.ML

XploVAE improves recommendation by balancing known and novel items.

problem Balancing known and novel items for better recommendations.

method Constructs user-specific subgraphs for exploitation and exploration, learns personalized item embeddings.

result Demonstrates effectiveness on various real-world datasets.

recommender systems variational auto-encoder exploitation exploration personalized recommendations

2020-06-05abs ↗pdf ↗

EE-Net: Exploitation-Exploration Neural Networks in Contextual Banditscs.LG

Proposes EE-Net for neural exploration in contextual bandits.

problem Exploitation-Exploration tradeoff in contextual bandits.

method Uses two neural networks: Exploitation and Exploration, to learn reward function and adaptively explore.

result Achieves

\mathcal{O}(\sqrt{T\log T})

regret and outperforms existing methods.

contextual bandits neural networks exploration exploitation regret

2021-10-07abs ↗pdf ↗

Stochastic Linear Contextual Bandits with Diverse Contextscs.LG

New algorithm reduces regret with diverse contexts in bandits.

problem Impact of context diversity on stochastic linear contextual bandits.

method Design of LinUCB-d algorithm and analysis of its regret performance.

result Cumulative expected regret is bounded by a constant under diverse context assumption.

context diversity stochastic linear contextual bandits LinUCB-d regret analysis exploitation

2020-03-05abs ↗pdf ↗

Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualizationcs.LG

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active l…

2018-04-20abs ↗pdf ↗

Learning what matters - Sampling interesting patternsstat.ML

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into acc…

2017-02-07abs ↗pdf ↗

Pseudo-Bayesian Optimizationstat.ML

Pseudo-Bayesian Optimization improves black-box function optimization using simple local regression.

problem Optimizing expensive black-box functions with uncertainty quantification.

method Axiomatic framework for convergence guarantees, combined with simple local regression and randomized prior.

result Empirically superior algorithms outperform state-of-the-art benchmarks in various applications.

Bayesian Optimization Black-box Function Uncertainty Quantification Convergence Guarantees Local Regression

2023-10-15abs ↗pdf ↗

Zooming for Efficient Model-Free Reinforcement Learning in Metric Spacescs.LG

ZoomRL learns efficient strategies for large state-action spaces using a metric.

problem Handling large state-action spaces in reinforcement learning.

method ZoomRL leverages continuous bandits to adaptively discretize the joint space.

result Achieves worst-case regret of $ ilde{O}(H^{rac{5}{2}} K^{rac{d+1}{d+2}})$.

Reinforcement Learning Metric Spaces Continuous Bandits Adaptive Discretization Regret Analysis

2020-03-09abs ↗pdf ↗

Conversational Contextual Bandit: Algorithm and Applicationcs.LG

Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses…

2019-06-04abs ↗pdf ↗

Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Boundcs.LG

Exploration in reinforcement learning (RL) suffers from the curse of dimensionality when the state-action space is large. A common practice is to parameterize the high-dimensional value and policy functions using given features. However existing methods either have no theoretical guarantee or suffer a regret that is ex…

2019-05-24abs ↗pdf ↗

Online Decision Making with Generative Action Setscs.LG

New algorithm optimizes online decision-making with dynamically generated actions.

problem Balancing action generation costs with optimal decision-making in online learning.

method Doubly-optimistic algorithm using LCB for action selection and UCB for action generation.

result Achieves optimal regret bound of

O(T^{\frac{d}{d+2}}d^{\frac{d}{d+2}} + d\sqrt{T\log T})

online learning generative AI doubly-optimistic action generation regret bound

2025-09-30abs ↗pdf ↗

Neural Bandit with Arm Group Graphcs.LG

AGG-UCB uses neural networks to optimize group behaviors in contextual bandits.

problem Optimizing group behaviors in contextual bandits with mutual impacts.

method Introduces Arm Group Graph (AGG) and AGG-UCB algorithm using neural networks and graph neural networks.

result Achieves near-optimal regret bound with over-parameterized neural networks.

contextual bandits arm group graph graph neural networks upper confidence bound neural networks

2022-06-08abs ↗pdf ↗

Multi-fidelity Bayesian Optimization: A Reviewcs.CE

MF BO combines MFO and BO to optimize expensive problems.

problem Expensive engineering design optimization problems.

method Gaussian process-based multi-fidelity surrogates and acquisition functions.

result Structured understanding of MF BO.

Bayesian optimization multi-fidelity expensive optimization surrogates acquisition functions

2023-11-21abs ↗pdf ↗

Online Feedback Efficient Active Target Discovery in Partially Observable Environmentscs.AI

DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.

problem Efficiently discovering targets in partially observable environments with limited sampling.

method DiffATD uses diffusion dynamics to maintain a belief distribution over unobserved states, balancing exploration and exploitation.

result DiffATD outperforms baselines and supervised methods in diverse domains.

active target discovery diffusion dynamics partially observable environments target sampling exploration-exploitation

2025-05-10abs ↗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 ↗

Provably Efficient Safe Exploration via Primal-Dual Policy Optimizationcs.LG

New algorithm optimizes reward while ensuring safety in complex decision-making problems.

problem Maximizing reward while adhering to safety constraints in complex decision-making problems.

method Optimistic Primal-Dual Proximal Policy Optimization (OPDOP) algorithm combining least-squares policy evaluation and a bonus term for safe exploration.

result Achieves

ilde{O}(d H^{2.5}\sqrt{T})

regret and

ilde{O}(d H^{2.5}\sqrt{T})

constraint violation.

Safe Reinforcement Learning CMDP Optimization Proximal Policy Optimization Function Approximation

2020-03-01abs ↗pdf ↗