9 citations · 10 across the 13 of their papers we have counts for
15 papers
In-Context Batch Black-box Optimization via Flow Matching
Quoc-Anh Hoang Nguyen, Xinyu Zhang, Daolang Huang +2
Batch black-box optimization is important for expensive systems where many candidates can be evaluated in parallel, yet only a few rounds of evaluation are affordable. In this regi…
Prediction-Powered Active Testing
Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang +2
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit th…
Efficient Adaptive Data Acquisition via Pretrained Belief Representations
Daolang Huang, Zhuoyue Huang, Conor Hassan +3
Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecifie…
Constrained Bayesian Experimental Design via Online Planning
Yujia Guo, Daolang Huang, Xinyu Zhang +3
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic const…
In-Context Multi-Objective Optimization
Xinyu Zhang, Conor Hassan, Julien Martinelli +2
Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such exp…
PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference
Yang Yang, Severi Rissanen, Paul E. Chang +5
Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or neuroscience, increasingly leveraging…
Efficient Autoregressive Inference for Transformer Probabilistic Models
Conor Hassan, Nasrulloh Loka, Cen-You Li +6
Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tabular foundation models, excel at singl…
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
Daolang Huang, Xinyi Wen, Ayush Bharti +2
Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instanta…
PABBO: Preferential Amortized Black-Box Optimization
Xinyu Zhang, Daolang Huang, Samuel Kaski +1
Preferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs. It relies on a statistical s…
Amortized Bayesian Experimental Design for Decision-Making
Daolang Huang, Yujia Guo, Luigi Acerbi +1
Many critical decisions, such as personalized medical diagnoses and product pricing, are made based on insights gained from designing, observing, and analyzing a series of experime…
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
Paul E. Chang, Nasrulloh Loka, Daolang Huang +3
Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are l…
Cost-aware simulation-based inference
Ayush Bharti, Daolang Huang, Samuel Kaski +1
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is t…
Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang, Manuel Haussmann, Ulpu Remes +5
Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification.…
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Daolang Huang, Ayush Bharti, Amauri Souza +2
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics t…
Bayesian Optimization Augmented with Actively Elicited Expert Knowledge
Daolang Huang, Louis Filstroff, Petrus Mikkola +2
Bayesian optimization (BO) is a well-established method to optimize black-box functions whose direct evaluations are costly. In this paper, we tackle the problem of incorporating e…