13 citations · 33 across the 17 of their papers we have counts for
17 papers
Runtime Governance for AI Agents: Policies on Paths
Maurits Kaptein, Vassilis-Javed Khan, Andriy Podstavnychy
AI agents -- systems that plan, reason, and act using large language models -- produce non-deterministic, path-dependent behavior that cannot be fully governed at design time, wher…
Rethinking Knowledge Transfer in Learning Using Privileged Information
Danil Provodin, Bram van den Akker, Christina Katsimerou +2
In supervised machine learning, privileged information (PI) is information that is unavailable at inference, but is accessible during training time. Research on learning using priv…
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy
We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…
Making accurate and interpretable treatment decisions for binary outcomes
Lingjie Shen, Gijs Geleijnse, Maurits Kaptein
Optimal treatment rules can improve health outcomes on average by assigning a treatment associated with the most desirable outcome to each individual. Due to an unknown data genera…
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achi…
On efficient covariate adjustment selection in causal effect estimation
Hongyi Chen, Maurits Kaptein
In order to achieve unbiased and efficient estimators of causal effects from observational data, covariate selection for confounding adjustment becomes an important task in causal…
A novel framework extending cause-effect inference methods to multivariate causal discovery
Hongyi Chen, Maurits Kaptein
We focus on the extension of bivariate causal learning methods into multivariate problem settings in a systematic manner via a novel framework. It is purposive to augment the scale…
Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity
Xynthia Kavelaars, Joris Mulder, Maurits Kaptein
In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected…
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We study a posterior sampling approach to efficient exploration in constrained reinforcement learning. Alternatively to existing algorithms, we propose two simple algorithms that a…
Bayesian multivariate logistic regression for superiority and inferiority decision-making under observable treatment heterogeneity
Xynthia Kavelaars, Joris Mulder, Maurits Kaptein
The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specifi…
The Impact of Batch Learning in Stochastic Linear Bandits
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We consider a special case of bandit problems, named batched bandits, in which an agent observes batches of responses over a certain time period. Unlike previous work, we consider…
The Impact of Batch Learning in Stochastic Bandits
Danil Provodin, Pratik Gajane, Mykola Pechenizkiy +1
We consider a special case of bandit problems, namely batched bandits. Motivated by natural restrictions of recommender systems and e-commerce platforms, we assume that a learning…
Decision-making with multiple correlated binary outcomes in clinical trials
X. M. Kavelaars, J. Mulder, M. C. Kaptein
Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to c…
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models
Reza Mohammadi, Matthew Pratola, Maurits Kaptein
Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) sear…
Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream
Maurits Kaptein, Paul Ketelaar
In marketing we are often confronted with a continuous stream of responses to marketing messages. Such streaming data provide invaluable information regarding message effectiveness…
Investigation of the concept of beauty via a lock-in feedback experiment
M. C. Kaptein, R. van Emden, D. Iannuzzi
Lock-in feedback circuits are routinely used in physics laboratories all around the world to extract small signals out of a noisy environment. In a recent paper (M. Kaptein, R. van…
Lock in Feedback in Sequential Experiments
Maurits Kaptein, Davide Iannuzzi
We often encounter situations in which an experimenter wants to find, by sequential experimentation, , where is a (possibly unknown) function of…