Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods

• URL: http://arxiv.org/abs/2505.10448v1
• Date: Thu, 15 May 2025 16:06:44 GMT
• Title: Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
• Authors: Conor Rosato, Harvinder Lehal, Simon Maskell, Lee Devlin, Malcolm Strens,
• Abstract summary: We explore several sampling algorithms that make use of subset evaluations to reduce computational overhead.<n>We introduce data-driven proxies in place of Taylor expansions and define a novel-cost aware adaptive controller.<n>We find our improved version of Importance with Nested Training Samples (HINTS), with adaptive proposals and a data-driven proxy, obtains the best sampling error in a fixed computational budget.
• Score: 2.299872239734834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian inference with Markov Chain Monte Carlo (MCMC) is challenging when the likelihood function is irregular and expensive to compute. We explore several sampling algorithms that make use of subset evaluations to reduce computational overhead. We adapt the subset samplers for this setting where gradient information is not available or is unreliable. To achieve this, we introduce data-driven proxies in place of Taylor expansions and define a novel computation-cost aware adaptive controller. We undertake an extensive evaluation for a challenging disease modelling task and a configurable task with similar irregularity in the likelihood surface. We find our improved version of Hierarchical Importance with Nested Training Samples (HINTS), with adaptive proposals and a data-driven proxy, obtains the best sampling error in a fixed computational budget. We conclude that subset evaluations can provide cheap and naturally-tempered exploration, while a data-driven proxy can pre-screen proposals successfully in explored regions of the state space. These two elements combine through hierarchical delayed acceptance to achieve efficient, exact sampling.

Related papers

• Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach [51.76826149868971]
  Policy evaluation via Monte Carlo simulation is at the core of many MC Reinforcement Learning (RL) algorithms. We propose as a quality index a surrogate of the mean squared error of a return estimator that uses trajectories of different lengths. We present an adaptive algorithm called Robust and Iterative Data collection strategy Optimization (RIDO)
  arXiv  Detail & Related papers  (2024-10-17T11:47:56Z)
• Solving Stochastic Orienteering Problems with Chance Constraints Using a GNN Powered Monte Carlo Tree Search [3.3088495893219885]
  We present a Monte Carlo Tree Search (MCTS) method to solve orienteering problems with chance constraints. While adhering to an assigned travel budget the algorithm seeks to maximize collected reward while incurring travel costs. We demonstrate how the approach is capable of generalizing beyond the characteristics of the training dataset.
  arXiv  Detail & Related papers  (2024-09-06T23:31:01Z)
• An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning [55.467047686093025]
  A common approach to alleviate such forgetting is to rehearse samples from prior tasks during fine-tuning.<n>We propose a sampling scheme, textttbf mix-cd, that prioritizes rehearsal of collateral damage'' samples.<n>Our approach is computationally efficient, easy to implement, and outperforms several leading continual learning methods in compute-constrained settings.
  arXiv  Detail & Related papers  (2024-02-12T22:32:12Z)
• Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling [7.357511266926065]
  Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice forally exact conditional sampling is Metropolis-within-Gibbs (MWG)
  arXiv  Detail & Related papers  (2023-08-17T16:08:18Z)
• High-dimensional Contextual Bandit Problem without Sparsity [8.782204980889077]
  We propose an explore-then-commit (EtC) algorithm to address this problem and examine its performance. We derive the optimal rate of the ETC algorithm in terms of $T$ and show that this rate can be achieved by balancing exploration and exploitation. We introduce an adaptive explore-then-commit (AEtC) algorithm that adaptively finds the optimal balance.
  arXiv  Detail & Related papers  (2023-06-19T15:29:32Z)
• Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs [60.40452803295326]
  We propose a new reward-free algorithm for learning linear Markov decision processes (MDPs) At the core of our algorithm is uncertainty-weighted value-targeted regression with exploration-driven pseudo-reward. We show that our algorithm only needs to explore $tilde O( d2varepsilon-2)$ episodes to find an $varepsilon$-optimal policy.
  arXiv  Detail & Related papers  (2023-03-17T17:53:28Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs [3.491202838583993]
  Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. They do not provide a mechanism for obtaining exact samples from these distributions. We propose a new approximate sampling technique, Quasi Rejection Sampling (QRS), that allows for a trade-off between sampling efficiency and sampling quality.
  arXiv  Detail & Related papers  (2021-12-10T17:51:37Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)
• A Provably Efficient Sample Collection Strategy for Reinforcement Learning [123.69175280309226]
  One of the challenges in online reinforcement learning (RL) is that the agent needs to trade off the exploration of the environment and the exploitation of the samples to optimize its behavior. We propose to tackle the exploration-exploitation problem following a decoupled approach composed of: 1) An "objective-specific" algorithm that prescribes how many samples to collect at which states, as if it has access to a generative model (i.e., sparse simulator of the environment); 2) An "objective-agnostic" sample collection responsible for generating the prescribed samples as fast as possible.
  arXiv  Detail & Related papers  (2020-07-13T15:17:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.