Machine Learning for Matching Astronomy Catalogues. - David Rohde; Michael Drinkwater; Marcus Gallagher; Tom Downs; Marianne Doyle (IDEAL, 2004)

Development and Application of Statistical and Machine Learning Techniques in Probabilistic Astronomical Catalogue-Matching Problems - David Rohde (2007)

Spatial forecasting of residential urban fires: A Bayesian approach. - David Rohde; Jonathan Corcoran; Prem Chhetri (Comput. Environ. Urban Syst., 2010)

Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study. - Jonathan Corcoran; Gary Higgs; David Rohde; Prem Chhetri (J. Geogr. Syst., 2011)

Visualization of Predictive Distributions for Discrete Spatial-Temporal Log Cox Processes Approximated with MCMC. - David Rohde; Jonathan Corcoran; Gentry White; Ruth Huang (IDEAL, 2012)

Graphical tools for conditional probabilistic exploration of multivariate spatial datasets. - David Rohde; Jonathan Corcoran (Comput. Environ. Urban Syst., 2012)

Visual Data Mining Methods for Kernel Smoothed Estimates of Cox Processes. - David Rohde; Ruth Huang; Jonathan Corcoran; Gentry White (PAKDD Workshops, 2013)

The Sensitivity of the Number of Clusters in a Gaussian Mixture Model to Prior Distributions. - Cristian Cruz; William Lima Leão; David Rohde (Math. Comput. Sci., 2013)

MCMC methods for univariate exponential family models with intractable normalization constants. - David Rohde; Jonathan Corcoran (SSP, 2014)

Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues. - David Rohde; Matt P. Wand (J. Mach. Learn. Res., 2016)

RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. - David Rohde; Stephen Bonner; Travis Dunlop; Flavian Vasile; Alexandros Karatzoglou (CoRR, 2018)

Latent Variable Session-Based Recommendation. - David Rohde; Stephen Bonner (CoRR, 2019)

Three Methods for Training on Bandit Feedback. - Dmytro Mykhaylov; David Rohde; Flavian Vasile (CoRR, 2019)

Replacing the do-calculus with Bayes rule. - Finnian Lattimore; David Rohde (CoRR, 2019)

A Bayesian Solution to the M-Bias Problem. - David Rohde (CoRR, 2019)

On the Value of Bandit Feedback for Offline Recommender System Evaluation. - Olivier Jeunen; David Rohde; Flavian Vasile (CoRR, 2019)

Recommendation System-based Upper Confidence Bound for Online Advertising. - Nhan Nguyen-Thanh; Dana Marinca; Kinda Khawam; David Rohde; Flavian Vasile; Elena Simona Lohan; Steven Martin 001; Dominique Quadri (CoRR, 2019)

Learning from Bandit Feedback: An Overview of the State-of-the-art. - Olivier Jeunen; Dmytro Mykhaylov; David Rohde; Flavian Vasile; Alexandre Gilotte; Martin Bompaire (CoRR, 2019)

Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks. - Otmane Sakhi; Stephen Bonner; David Rohde; Flavian Vasile (CoRR, 2019)

Causal inference with Bayes rule. - Finnian Lattimore; David Rohde (CoRR, 2019)

Joint Policy-Value Learning for Recommendation. - Olivier Jeunen; David Rohde; Flavian Vasile; Martin Bompaire (KDD, 2020)

BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. - Otmane Sakhi; Stephen Bonner; David Rohde; Flavian Vasile (KDD, 2020)

Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation. - David Rohde; Flavian Vasile; Sergey Ivanov; Otmane Sakhi (RecSys, 2020)

A Gentle Introduction to Recommendation as Counterfactual Policy Learning. - Flavian Vasile; David Rohde; Olivier Jeunen; Amine Benhalloum (UMAP, 2020)

From Clicks to Conversions: Recommendation for long-term reward. - Philomène Chagniot; Flavian Vasile; David Rohde (CoRR, 2020)

Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. - David Rohde (ICBINB@NeurIPS, 2021)

Bayesian Causal Inference for Real World Interactive Systems. - Nicolas Chopin; Mike Gartrell; Dawen Liang; Alberto Lumbreras; David Rohde; Yixin Wang 002 (KDD, 2021)

SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. - Michael D. Ekstrand; Allison Chaney; Pablo Castells; Robin Burke; David Rohde; Manel Slokom (RecSys, 2021)

Combining Reward and Rank Signals for Slate Recommendation. - Imad Aouali; Sergey Ivanov; Mike Gartrell; David Rohde; Flavian Vasile; Victor Zaytsev; Diego Legrand (CoRR, 2021)

Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search. - Imad Aouali; Amine Benhalloum; Martin Bompaire; Achraf Ait Sidi Hammou; Sergey Ivanov; Benjamin Heymann; David Rohde; Otmane Sakhi; Flavian Vasile; Maxime Vono (KDD, 2022)

A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation. - Imad Aouali; Achraf Ait Sidi Hammou; Sergey Ivanov; Otmane Sakhi; David Rohde; Flavian Vasile (CoRR, 2022)

Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation. - Imad Aouali; Amine Benhalloum; Martin Bompaire; Benjamin Heymann; Olivier Jeunen; David Rohde; Otmane Sakhi; Flavian Vasile (CoRR, 2022)

Learning from aggregated data with a maximum entropy model. - Alexandre Gilotte; Ahmed Ben Yahmed; David Rohde (CoRR, 2022)

Fast Offline Policy Optimization for Large Scale Recommendation. - Otmane Sakhi; David Rohde; Alexandre Gilotte (AAAI, 2023)

Exponential Smoothing for Off-Policy Learning. - Imad Aouali; Victor-Emmanuel Brunel; David Rohde; Anna Korba (ICML, 2023)

Fast Slate Policy Optimization: Going Beyond Plackett-Luce. - Otmane Sakhi; David Rohde; Nicolas Chopin (Trans. Mach. Learn. Res., 2023)

Why the Shooting in the Dark Method Dominates Recommender Systems Practice. - David Rohde (RecSys, 2024)

Welfare-Optimized Recommender Systems. - Benjamin Heymann; Flavian Vasile; David Rohde (SURE@RecSys, 2024)

Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. - Imad Aouali; Victor-Emmanuel Brunel; David Rohde; Anna Korba (UAI, 2024)

Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking. - David Rohde (CoRR, 2024)

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. - Imad Aouali; Victor-Emmanuel Brunel; David Rohde; Anna Korba (AISTATS, 2025)

A Point Process Model for Optimizing Repeated Personalized Action Delivery to Users. - Alexander Merkov; David Rohde (CoRR, 2025)

Practical Improvements of A/B Testing with Off-Policy Estimation. - Otmane Sakhi; Alexandre Gilotte; David Rohde (CoRR, 2025)

Confounding is a Pervasive Problem in Real World Recommender Systems. - Alexander Merkov; David Rohde; Alexandre Gilotte; Benjamin Heymann (CoRR, 2025)