Mercury Machine Learning Lab – Delft

A collaboration between Booking.com, the University of Amsterdam, and Delft University of Technology.

The Mercury Machine Learning Lab is a collaboration between University of Amsterdam, Delft University of Technology and Booking.com. The lab focuses on the development and applications of artificial intelligence to the specific domain of online travel booking and recommendation service systems.

The collaboration of Mercury Machine Learning Lab combines expertise of scientists from the University of Amsterdam (information retrieval, causality and natural language processing), Delft University of Technology (reinforcement learning) with the unique expertise, experience and availability of big data at Booking. Over the period of five years, six PhD researchers and two postdocs work in the lab on six work packages.

The research projects cover fundamental research topics, ranging from model-based exploration, parallel model-based reinforcement learning, methods for combined online and offline evaluation, prediction methods that correct for undesired feedback loops and selection bias, domain generalization and domain adaptation, and novel language processing models for better generalization. These topics are both of fundamental scientific importance, as well as of immediate practical relevance for modern online businesses like Booking that aim to maximize customer satisfaction in quickly changing markets with the help of sophisticated data analytics.

Sustainable Development Goals

About the lab

Research projects

Model-based Exploration: Effectively performing exploration in the non-stationary and multi-faceted environments that Booking.com interacts with.

Bridging online and offline evaluation: To develop and evaluate methods, both theoretically and experimentally, that bridge the gap between online evaluation and offline (off-policy) evaluation.

Novel language processing models for better generalisation: To develop methods for training NLP models that explicitly target generalization across multiple related tasks.

Publications

Mercury Machine Learning Lab

2026

O. Zoeter P. Hager, M. de Rijke

CLAX: Fast and Flexible Neural Click Models in JAX Journal Article

In: 2026, (SIGIR 2026).

BibTeX

Bhadane, S.; Mooij, J. M.; Boeken, P.; Zoeter, O.

Testing Partially Identifiable Causal Queries using Ternary Tests Miscellaneous

2026, (UAI 2026).

BibTeX

2025

Ferreira, P.; Aziz, W.; Titov, I.

Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanations Journal Article

In: 2025, (ICLR (2026)).

BibTeX

Hager, P.; Zoeter, O.; de Rijke, M.

Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank (Extended abstract) Journal Article

In: 2025, (CONSEQUENCES Workshop at RecSys (2025)).

BibTeX

Ferreira, P.; Aziz, W.; Titov, I.

Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanations Journal Article

In: 2025, (Workshop on Actionable Interpretability@ICML2025).

BibTeX

Boeken, P.; Zoeter, O.; Mooij, J.

Conditional Forecasts and Proper Scoring Rules for Reliable and Accurate Performative Predictions Journal Article

In: 2025, (NeurIPS (2025)).

BibTeX

Hager, P.; Zoeter, O.; de Rijke, M.

Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank Journal Article

In: 2025, (ICTIR 2025).

BibTeX

Ferreira, P.; Aziz, W.; Titov, I.

Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanations Journal Article

In: 2025, (COLM (2025)).

BibTeX

Bhadane, S.; Mooij, J. M.; Boeken, P.; Zoeter, O.

Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses Journal Article

In: 2025, (UAI (2025)).

BibTeX

Boeken, P.; Forré, P.; Mooij, J. M.

Are Bayesian networks typically faithful? Journal Article

In: 2025, (Bernoulli).

BibTeX

Chen, L.; Mooij, J.

On Selection Bias in Statistical Causality Journal Article

In: 2025, (dagstat (2025)).

BibTeX

2024

Ferreira, P.; Titov, I.; Aziz, W.

Explanation Regularisation through the Lens of Attributions Journal Article

In: 2024, (COLING (2025)).

BibTeX

Brita, C.; Bongers, S.; Oliehoek, F. A.

SimuDICE: Offline Policy Optimization Through World Model Updates and DICE Estimation Journal Article

In: 2024, (BNAIC).

BibTeX

Aslan, Y.; Bongers, S.; Oliehoek, F.

Use of sample-splitting and cross-fitting techniques to mitigate the risks of double-dipping in behaviour-agnostic reinforcement learning Journal Article

In: 2024, (BNAIC).

BibTeX

de Haan, M.; Hager, P.

Understanding the Effects of the Baidu-ULTR Logging Policy on Two-Tower Models Journal Article

In: 2024, (CONSEQUENCES Workshop at RecSys (2024)).

BibTeX

Hager, P.; Deffayet, R.; Renders, J. M.; Zoeter, O.; de Rijke, M.

Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset Journal Article

In: 2024, (SIGIR (2024)).

BibTeX

Azizi, O.; Boeken, P.; Zoeter, O.; Oliehoek, F. A.; Spaan, M. T. J.

Leveraging diverse offline data in POMDPs with unobserved confounders Journal Article

In: 2024, (EWRL (2024)).

BibTeX

Boeken, P.; Mooij, J.

Dynamic Structural Causal Models Journal Article

In: 2024, (Causal Inference for Time Series Workshop @UAI (2024)).

BibTeX

Gupta, S.; Hager, P.; Huang, J.; Vardasbi, A.; Oosterhuis, H.

Unbiased Learning to Rank: On Recent Advances and Practical Applications Journal Article

In: 2024, (WSDM (2024)).

BibTeX

Boeken, P.; Zoeter, O.; Mooij, J.

Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift Journal Article

In: 2024, (CLeaR (2024)).

BibTeX

Mambelli, D.; Bongers, S.; Zoeter, O.; Spaan, M. T. J.; Oliehoek, F. A.

When Do Off-Policy and On-Policy Policy Gradient Methods Align? Journal Article

In: 2024, (arXiv).

BibTeX

Gupta, S.; Hager, P.; Oosterhuis, H.

Recent Advancements in Unbiased Learning to Rank Journal Article

In: 2024, (FIRE (2023)).

BibTeX

Bénédict, G.; Zhang, R.; Metzler, D.; Yates, A.; Deffayet, R.; Hager, P.; Jullien, S.

Report on the 1st Workshop on Generative Information Retrieval Workshop

2024, (ACM SIGIR Forum (2024)).

BibTeX

2023

Analytis, P.; Hager, P.

Collaborative filtering algorithms are prone to mainstream-taste bias Journal Article

In: 2023, (RecSys (2023)).

BibTeX

Boeken, P.; de Kroon, A.; de Jong, M.; Mooij, J.; Zoeter, O.

Correcting for Nonignorable Selection Bias and Missing Response in Regression using Privileged Information Journal Article

In: 2023, (UAI (2023)).

BibTeX

Deffayet, R.; Hager, P.; Renders, J. M.; de Rijke, M.

An Offline Metric for the Debiasedness of Click Models Journal Article

In: 2023, (SIGIR (2023)).

BibTeX

Gupta, S.; Hager, P.; Huang, J.; Vardasbi, A.; Oosterhuis, H.

Recent Advances in the Foundations and Applications of Unbiased Learning to Rank Journal Article

In: 2023, (SIGIR (2023)).

BibTeX

Hager, P.; de Rijke, M.; Zoeter, O.

Unbiased Neural Click Models and Pointwise IPS Rankers Journal Article

In: 2023, (ECIR (2023)).

BibTeX

2022

Hager, P.; de Rijke, M.; Zoeter, O.

Are Neural Click Models Pointwise IPS Rankers? Miscellaneous

2022, (CONSEQUENCES+REVEAL Workshop at RecSys (2022)).

BibTeX

People

Partners

Booking.com has grown from a small Dutch startup in 1996 to one of the world’s leading digital travel companies.

Delft University of Technology (TU Delft) is a technical university in Delft. Top education and research are at the heart of the oldest and largest technical university in the Netherlands.

University of Amsterdam (UvA) is the Netherlands’ largest university, offering the widest range of academic programmes.

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