# Advanced Bandit Algorithms Research

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/advanced-bandit-algorithms-research/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the optimization of multi-armed bandit problems, including topics such as Bayesian optimization, contextual bandits, online learning, convex optimization, Thompson sampling, regret analysis, Gaussian process optimization, hyperparameter optimization, and adversarial multi-armed bandits. |
| Domain | Social Sciences |
| Field | Decision Sciences |
| OpenAlex ID | t12101 |
| Works | 13 |

## Topic papers all

- [Characterizing concept drift](https://scholariq.org/papers/characterizing-concept-drift/)
- [Streaming Session-based Recommendation](https://scholariq.org/papers/streaming-session-based-recommendation/)
- [Personal recommendation using deep recurrent neural networks in NetEase](https://scholariq.org/papers/personal-recommendation-using-deep-recurrent-neural-networks-in-netease/)
- [Collaborative Self-Attention Network for Session-based Recommendation](https://scholariq.org/papers/collaborative-self-attention-network-for-session-based-recommendation/)
- [Bayesian policy reuse](https://scholariq.org/papers/bayesian-policy-reuse/)
- [First Order Constrained Optimization in Policy Space](https://scholariq.org/papers/first-order-constrained-optimization-in-policy-space/)
- [Reaching Cooperation using Emerging Empathy and Counter-empathy](https://scholariq.org/papers/reaching-cooperation-using-emerging-empathy-and-counter-empathy/)
- [Sampling Based Approaches for Minimizing Regret in Uncertain Markov Decision Processes (MDPs)](https://scholariq.org/papers/sampling-based-approaches-for-minimizing-regret-in-uncertain-markov-decision/)
- [Survey of multiarmed bandit algorithms applied to recommendation systems](https://scholariq.org/papers/survey-of-multiarmed-bandit-algorithms-applied-to-recommendation-systems/)
- [Popularity Bias in Recommender Systems - A Review](https://scholariq.org/papers/popularity-bias-in-recommender-systems-a-review/)
- [Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysis](https://scholariq.org/papers/curriculum-learning-empowered-reinforcement-learning-for-graph-based-portfolio/)
- [Empirical analysis of Machine Learning Techniques for context aware Recommender Systems in the environment of IoT](https://scholariq.org/papers/empirical-analysis-of-machine-learning-techniques-for-context-aware-recommender/)
- [Feature-Based Dynamic Pricing with Online Learning and Offline Data](https://scholariq.org/papers/feature-based-dynamic-pricing-with-online-learning-and-offline-data/)

## Topic primary papers

- [Bayesian policy reuse](https://scholariq.org/papers/bayesian-policy-reuse/)
- [Reaching Cooperation using Emerging Empathy and Counter-empathy](https://scholariq.org/papers/reaching-cooperation-using-emerging-empathy-and-counter-empathy/)
- [Survey of multiarmed bandit algorithms applied to recommendation systems](https://scholariq.org/papers/survey-of-multiarmed-bandit-algorithms-applied-to-recommendation-systems/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.