1 citations · 2 across the 23 of their papers we have counts for
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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…
Robust Experimental Design via Generalised Bayesian Inference
Yasir Zubayr Barlas, Sabina J. Sloman, Samuel Kaski
Bayesian optimal experimental design is a principled framework for conducting experiments that leverages Bayesian inference to quantify how much information one can expect to gain…
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…
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
Zachris Björkman, Jorge Loría, Sophie Wharrie +1
Bayesian causal discovery benefits from prior information elicited from domain experts, and in heterogeneous domains any prior knowledge would be badly needed. However, so far prio…
Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification
Roubing Tang, Sabina J. Sloman, Samuel Kaski
In many science and industry settings, a central challenge is designing experiments under time and budget constraints. Bayesian Optimal Experimental Design (BOED) is a paradigm to…
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…
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift
Sabina J. Sloman, Michele Caprio, Samuel Kaski
Uncertainty-aware machine learners, such as Bayesian neural networks, output a quantification of uncertainty instead of a point prediction. We provide uncertainty-aware learners wi…
Robust and Computation-Aware Gaussian Processes
Marshal Arijona Sinaga, Julien Martinelli, Samuel Kaski
Gaussian processes (GPs) are widely used for regression and optimization tasks such as Bayesian optimization (BO) due to their expressiveness and principled uncertainty estimates.…
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…
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization
Minttu Alakuijala, Ya Gao, Georgy Ananov +4
As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience remains a key chal…
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…