Topic Modelling Black Box Optimization

• URL: http://arxiv.org/abs/2512.16445v1
• Date: Thu, 18 Dec 2025 12:00:24 GMT
• Title: Topic Modelling Black Box Optimization
• Abstract summary: Choosing the number of topics $T$ in Latent Dirichlet Allocation (LDA) is a key design decision that strongly affects both the statistical fit and interpretability of topic models.<n>We formulate the selection of $T$ as a discrete black-box optimization problem, where each function evaluation corresponds to training an LDA model and measuring its validation perplexity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Choosing the number of topics $T$ in Latent Dirichlet Allocation (LDA) is a key design decision that strongly affects both the statistical fit and interpretability of topic models. In this work, we formulate the selection of $T$ as a discrete black-box optimization problem, where each function evaluation corresponds to training an LDA model and measuring its validation perplexity. Under a fixed evaluation budget, we compare four families of optimizers: two hand-designed evolutionary methods - Genetic Algorithm (GA) and Evolution Strategy (ES) - and two learned, amortized approaches, Preferential Amortized Black-Box Optimization (PABBO) and Sharpness-Aware Black-Box Optimization (SABBO). Our experiments show that, while GA, ES, PABBO, and SABBO eventually reach a similar band of final perplexity, the amortized optimizers are substantially more sample- and time-efficient. SABBO typically identifies a near-optimal topic number after essentially a single evaluation, and PABBO finds competitive configurations within a few evaluations, whereas GA and ES require almost the full budget to approach the same region.

Related papers

• Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings [73.44599934855067]
  LookaHES is a nonmyopic BO framework designed for dynamic, history-dependent cost environments.<n>LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization.<n>Our innovation is the integration of neural policies, including large language models, to effectively navigate structured, domain-specific action spaces.
  arXiv  Detail & Related papers  (2026-01-10T09:49:45Z)
• None To Optima in Few Shots: Bayesian Optimization with MDP Priors [40.4319486959011]
  We introduce the Procedure-inFormed BO (ProfBO) algorithm, which solves black-box optimization with remarkably few function evaluations.<n>ProfBO consistently outperforms state-of-the-art methods by achieving high-quality tuning solutions with significantly fewer evaluations.
  arXiv  Detail & Related papers  (2025-11-02T16:53:17Z)
• Trust Region-Based Bayesian Optimisation to Discover Diverse Solutions [8.727449523567673]
  We explore the effectiveness of trust region-based BO algorithms for diversity optimisation in different dimensional black box problems.<n>We propose diversity optimisation approaches extending TuRBO1, which is the first BO method that uses a trust region-based approach for scalability.<n>We evaluate proposed algorithms on benchmark functions with dimensions 2 to 20.
  arXiv  Detail & Related papers  (2025-11-02T00:31:37Z)
• $f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization [54.94545757220999]
  $f$-PO is a novel framework that generalizes and extends existing approaches.<n>We conduct experiments on state-of-the-art language models using benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-29T02:11:45Z)
• Sharpness-Aware Black-Box Optimization [47.95184866255126]
  We propose a Sharpness-Aware Black-box Optimization (SABO) algorithm, which applies a sharpness-aware minimization strategy to improve the model generalization. Empirically, extensive experiments on the black-box prompt fine-tuning tasks demonstrate the effectiveness of the proposed SABO method in improving model generalization performance.
  arXiv  Detail & Related papers  (2024-10-16T11:08:06Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
  We consider a generalization of Shannon entropy from work in statistical decision theory. We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures. We then show how alternative choices for the loss yield a flexible family of acquisition functions.
  arXiv  Detail & Related papers  (2022-10-04T04:43:58Z)
• MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation [0.01652719262940403]
  optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular model-based approach for optimising such black-box functions. We extend previous work on BO by density-ratio estimation (BORE) to the multi-objective setting.
  arXiv  Detail & Related papers  (2022-03-31T09:27:59Z)
• A unified surrogate-based scheme for black-box and preference-based optimization [2.561649173827544]
  We show that black-box and preference-based optimization problems are closely related and can be solved using the same family of approaches. We propose the generalized Metric Response Surface (gMRS) algorithm, an optimization scheme that is a generalization of the popular MSRS framework.
  arXiv  Detail & Related papers  (2022-02-03T08:47:54Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)
• Scalable Constrained Bayesian Optimization [10.820024633762596]
  The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and the scientific community. We propose the scalable constrained Bayesian optimization (SCBO) algorithm that overcomes the above challenges and pushes the state-the-art.
  arXiv  Detail & Related papers  (2020-02-20T01:48:46Z)
• Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [100.83444258562263]
  We propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions. We are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose.
  arXiv  Detail & Related papers  (2020-01-12T09:42:19Z)

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.