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Huong Ha

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7 papers

1 author row

AAMAS Conference 2025 Conference Paper

Beyond Goal Recognition: A Reinforcement Learning-based Approach to Inferring Agent Behaviour

Sheryl Mantik
Michael Dann
Minyi Li
Huong Ha
Julie Porteous

Goal recognition (GR) involves inferring an agent’s goals based on observed actions. In addition to goals, however, in various cases it may be useful to infer additional agent attributes, such as preferences, beliefs, and ability level, so as to gain deeper insights into the agent’s decision-making process. Recent advances in GR have incorporated Reinforcement Learning (RL), which provides greater practicality and adaptability, especially in stochastic environments. This adaptability creates the opportunity to extend RL-based frameworks beyond goal recognition. In this work, we build upon a recent RL-based GR framework to propose a generalised approach capable of inferring a wider range of agent attributes. By integrating these attributes within the problem formulation, we demonstrate how off-the-shelf RL techniques can be applied to infer them effectively. Our results show that this extended framework accurately distinguishes fine-grained differences in agent attributes across diverse scenarios. Moreover, we show that recognising these additional attributes can in turn improve goal recognition accuracy.

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AAAI Conference 2025 Conference Paper

BOIDS: High-Dimensional Bayesian Optimization via Incumbent-Guided Direction Lines and Subspace Embeddings

Lam Ngo
Huong Ha
Jeffrey Chan
Hongyu Zhang

When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of dimensions, hence scaling BO to high dimension is of much interest. However, state-of-the-art high-dimensional BO methods still suffer from the curse of dimensionality, highlighting the need for further improvements. In this work, we introduce BOIDS, a novel high-dimensional BO algorithm that guides optimization by a sequence of one-dimensional direction lines using a novel tailored line-based optimization procedure. To improve the efficiency, we also propose an adaptive selection technique to identify most optimal lines for each round of line-based optimization. Additionally, we incorporate a subspace embedding technique for better scaling to high-dimensional spaces. We further provide theoretical analysis of our proposed method to analyze its convergence property. Our extensive experimental results show that BOIDS outperforms state-of-the-art baselines on various synthetic and real-world benchmark problems.

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NeurIPS Conference 2025 Conference Paper

MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions

Lam Ngo
Huong Ha
Jeffrey Chan
Hongyu Zhang

Bayesian Optimization (BO) is a powerful tool for optimizing expensive black-box objective functions. While extensive research has been conducted on the single-objective optimization problem, the multi-objective optimization problem remains challenging. In this paper, we propose MOBO-OSD, a multi-objective Bayesian Optimization algorithm designed to generate a diverse set of Pareto optimal solutions by solving multiple constrained optimization problems, referred to as MOBO-OSD subproblems, along orthogonal search directions (OSDs) defined with respect to an approximated convex hull of individual objective minima. By employing a well-distributed set of OSDs, MOBO-OSD ensures broad coverage of the objective space, enhancing both solution diversity and hypervolume performance. To further improve the density of the set of the Pareto optimal candidate solutions without requiring an excessive number of subproblems, we leverage a Pareto Front Estimation technique to generate additional solutions in the neighborhood of existing solutions. Additionally, MOBO-OSD supports batch optimization, enabling parallel function evaluations to accelerate the optimization process when resources are available. Through extensive experiments and analysis on a variety of synthetic and real-world benchmark functions with two to six objectives, we demonstrate that MOBO-OSD consistently outperform the state-of-the-art algorithms.

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TMLR Journal 2024 Journal Article

High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy

Lam Ngo
Huong Ha
Jeffrey Chan
Vu Nguyen
Hongyu Zhang

Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high likelihood of containing the global optimum, and then use BO to optimize the objective function within these regions. In this paper, we propose a novel technique for defining the local regions using the Covariance Matrix Adaptation (CMA) strategy. Specifically, we use CMA to learn a search distribution that can estimate the probabilities of data points being the global optimum of the objective function. Based on this search distribution, we then define the local regions consisting of data points with high probabilities of being the global optimum. Our approach serves as a meta-algorithm as it can incorporate existing black-box BO optimizers, such as BO, TuRBO, and BAxUS, to find the global optimum of the objective function within our derived local regions. We evaluate our proposed method on various benchmark synthetic and real-world problems. The results demonstrate that our method outperforms existing state-of-the-art techniques.

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AAAI Conference 2021 Conference Paper

High Dimensional Level Set Estimation with Bayesian Neural Network

Huong Ha
Sunil Gupta
Santu Rana
Svetha Venkatesh

Level Set Estimation (LSE) is an important problem with applications in various fields such as material design, biotechnology, machine operational testing, etc. Existing techniques suffer from the scalability issue, that is, these methods do not work well with high dimensional inputs. This paper proposes novel methods to solve the high dimensional LSE problems using Bayesian Neural Networks. In particular, we consider two types of LSE problems: (1) explicit LSE problem where the threshold level is a fixed user-specified value, and, (2) implicit LSE problem where the threshold level is defined as a percentage of the (unknown) maximum of the objective function. For each problem, we derive the corresponding theoretic information based acquisition function to sample the data points so as to maximally increase the level set accuracy. Furthermore, we also analyse the theoretical time complexity of our proposed acquisition functions, and suggest a practical methodology to efficiently tune the network hyper-parameters to achieve high model accuracy. Numerical experiments on both synthetic and real-world datasets show that our proposed method can achieve better results compared to existing state-of-the-art approaches.

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NeurIPS Conference 2020 Conference Paper

Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces

Hung Tran-The
Sunil Gupta
Santu Rana
Huong Ha
Svetha Venkatesh

Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold. To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a \emph{hyperharmonic series}. Further, we propose another variant of our algorithm that scales to high dimensions. We show theoretically that for both our algorithms, the cumulative regret grows at sub-linear rates. Our experiments with synthetic and real-world optimisation tasks demonstrate the superiority of our algorithms over the current state-of-the-art methods for Bayesian optimisation in unknown search space.

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NeurIPS Conference 2019 Conference Paper

Bayesian Optimization with Unknown Search Space

Huong Ha
Santu Rana
Sunil Gupta
Thanh Nguyen
Hung Tran-The
Svetha Venkatesh

Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose function value within epsilon of the objective function maximum. Without the need to specify any parameters, our algorithm automatically triggers a minimal expansion required iteratively. We derive analytic expressions for when to trigger the expansion and by how much to expand. We also provide theoretical analysis to show that our method achieves epsilon-accuracy after a finite number of iterations. We demonstrate our method on both benchmark test functions and machine learning hyper-parameter tuning tasks and demonstrate that our method outperforms baselines.

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