Samuel Kaski

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…

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…

cs.LG2025★ 1 cited

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…