1 citations · 2 across the 23 of their papers we have counts for
16 papers · 1 filter
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…
Decoupled Conformal Optimisation: Efficient Prediction Sets via Independent Tuning and Calibration
Fanyi Wu, Lihua Niu, Samuel Kaski +1
Bayesian conformal optimisation methods often use the same held-out data both to search for efficient prediction sets and to certify coverage or risk. This coupling is natural for…
Elicitation-Augmented Bayesian Optimization
Alvar Haltia, Ville Hyvönen, Samuel Kaski
Human-in-the-loop Bayesian optimization (HITL BO) methods utilize human expertise to improve the sample-efficiency of BO. Most HITL BO methods assume that a domain expert can quant…
Online Sharp-Calibrated Bayesian Optimization
Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1
Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box functions, commonly based on Gaussian process (GP) surrogate models. Its effectiveness reli…
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
Zahra Rahiminasab, Reza Soumi, Arto Klami +1
Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes. Exi…
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
Lotta Mäkinen, Jorge Loría, Samuel Kaski
Meta-learning methods perform well on new within-distribution tasks but often fail when adapting to out-of-distribution target tasks, where transfer from source tasks can induce ne…
Bayesian Conformal Prediction as a Decision Risk Problem
Fanyi Wu, Veronika Lohmanova, Samuel Kaski +1
We propose Bayesian Conformal Prediction (BCP), a framework that combines Bayesian posterior predictive distributions with PAC-style conformal risk control to produce prediction se…
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…
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…
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.…
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…
Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model
Hans Moen, Vishnu Raj, Andrius Vabalas +4
Health registers contain rich information about individuals' health histories. Here our interest lies in understanding how individuals' health trajectories evolve in a nationwide l…
Proxy-informed Bayesian transfer learning with unknown sources
Sabina J. Sloman, Julien Martinelli, Samuel Kaski
Generalization outside the scope of one's training data requires leveraging prior knowledge about the effects that transfer, and the effects that don't, between different data sour…
Bayesian Active Learning in the Presence of Nuisance Parameters
Sabina J. Sloman, Ayush Bharti, Julien Martinelli +1
In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parame…
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal Mechanisms
Sophie Wharrie, Lisa Eick, Lotta Mäkinen +3
Machine learning strategies like multi-task learning, meta-learning, and transfer learning enable efficient adaptation of machine learning models to specific applications in health…