Thompson Sampling

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Most implemented papers archive 2025-07-28

30 shown of 135 papers with code (655 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
  26 Feb 2018 4 repositories listed
  At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
• Scalable Exploration via Ensemble++
  18 Jul 2024 2 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  Thompson Sampling is a principled method for balancing exploration and exploitation, but its real-world adoption faces computational challenges in large-scale or non-conjugate settings.
• Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
  30 Apr 2021 2 repositories listed
  We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations.
• Neural Thompson Sampling
  2 Oct 2020 2 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-armed bandit problems.
• Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
  2 Jul 2020 2 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.
• Thompson Sampling Algorithms for Mean-Variance Bandits
  1 Feb 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the exploration-exploitation tradeoff.
• Randomized Exploration for Non-Stationary Stochastic Linear Bandits
  11 Dec 2019 2 repositories listed
  We investigate two perturbation approaches to overcome conservatism that optimism based algorithms chronically suffer from in practice.
• Adapting multi-armed bandits policies to contextual bandits scenarios
  11 Nov 2018 2 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box…
• Randomized Value Functions via Multiplicative Normalizing Flows
  6 Jun 2018 2 repositories listed
  In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function.
• A Tutorial on Thompson Sampling
  7 Jul 2017 2 repositories listed
  Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to…
• Thompson Sampling for Contextual Bandits with Linear Payoffs
  15 Sep 2012 2 repositories listed
  Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems.
• Representative Action Selection for Large Action-Space Meta-Bandits
  23 May 2025 1 repository listed
  We study the problem of selecting a subset from a large action space shared by a family of bandits, with the goal of achieving performance nearly matching that of using the full action space.
• Steering Generative Models with Experimental Data for Protein Fitness Optimization
  21 May 2025 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  Protein fitness optimization involves finding a protein sequence that maximizes desired quantitative properties in a combinatorially large design space of possible sequences.
• Addressing Missing Data Issue for Diffusion-based Recommendation
  18 May 2025 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  To preserve user preference evolution in sequences despite extra missing data, we introduce Dual-side Thompson Sampling to implement simulation with two probability models, sampling by exploiting user preference from…
• Dynamic Assortment Selection and Pricing with Censored Preference Feedback
  3 Apr 2025 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  In this study, we investigate the problem of dynamic multi-product selection and pricing by introducing a novel framework based on a \textit{censored multinomial logit} (C-MNL) choice model.
• Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
  3 Mar 2025 1 repository listed
  Our results show that the selection of prompt design strategies improves the performance of EvoPrompt, and the Thompson sampling-based mechanism achieves the best overall results.
• Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning
  29 Jan 2025 1 repository listed Syntology ran 6 of 11 samples · 5 unverified
  Existing actor-critic algorithms, which are popular for continuous control reinforcement learning (RL) tasks, suffer from poor sample efficiency due to lack of principled exploration mechanism within them.
• Fast, Precise Thompson Sampling for Bayesian Optimization
  26 Nov 2024 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems.
• Sample-Efficient Alignment for LLMs
  3 Nov 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  The results demonstrate that SEA achieves highly sample-efficient alignment with oracle's preferences, outperforming recent active exploration methods for LLMs.
• Minimum Empirical Divergence for Sub-Gaussian Linear Bandits
  31 Oct 2024 1 repository listed
  We propose a novel linear bandit algorithm called LinMED (Linear Minimum Empirical Divergence), which is a linear extension of the MED algorithm that was originally designed for multi-armed bandits.
• Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
  29 Oct 2024 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  Bayesian optimization devolves the global optimization of a costly objective function to the global optimization of a sequence of acquisition functions.
• Distributed Thompson sampling under constrained communication
  21 Oct 2024 1 repository listed
  We apply distributed Thompson sampling, using a Gaussian process as a surrogate model, to approach the multi-agent Bayesian optimization problem.
• Queueing Matching Bandits with Preference Feedback
  14 Oct 2024 1 repository listed
  In this study, we consider multi-class multi-server asymmetric queueing systems consisting of N queues on one side and K servers on the other side, where jobs randomly arrive in queues at each time.
• Batched Bayesian optimization by maximizing the probability of including the optimum
  8 Oct 2024 1 repository listed
  Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation.
• Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox
  7 Oct 2024 1 repository listed
  We consider Thompson Sampling (TS) for linear combinatorial semi-bandits and subgaussian rewards.
• Improving Portfolio Optimization Results with Bandit Networks
  5 Oct 2024 1 repository listed
  We then extend this approach to the Portfolio Optimization problem by introducing the Combinatorial Adaptive Discounted Thompson Sampling (CADTS) algorithm, which addresses computational challenges within Combinatorial…
• Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
  8 Aug 2024 1 repository listed
  Vertical federated learning (VFL), where each participating client holds a subset of data features, has found numerous applications in finance, healthcare, and IoT systems.
• Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic
  6 Aug 2024 1 repository listed
  Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in multi-agent systems.
• Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems
  5 Aug 2024 1 repository listed
  This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system, marking a significant step forward in digital catalysis and generally in optimization methods for chemical engineering.
• More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
  18 Jun 2024 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  Empirically, we show that in tasks where deep exploration is necessary, our proposed algorithms that combine FGTS and approximate sampling perform significantly better compared to other strong baselines.

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.