David Rohde

According to our database, David Rohde authored at least 39 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces.

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

CoRR, 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

2023

Fast Slate Policy Optimization: Going Beyond Plackett-Luce.

Otmane Sakhi, David Rohde, Nicolas Chopin

CoRR, 2023

Exponential Smoothing for Off-Policy Learning.

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

Proceedings of the International Conference on Machine Learning, 2023

Fast Offline Policy Optimization for Large Scale Recommendation.

Otmane Sakhi, David Rohde, Alexandre Gilotte

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Learning from aggregated data with a maximum entropy model.

Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde

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

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

Welfare-Optimized Recommender Systems.

Benjamin Heymann, Flavian Vasile, David Rohde

CoRR, 2022

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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

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

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

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Bayesian Causal Inference for Real World Interactive Systems.

Nicolas Chopin, Mike Gartrell, Dawen Liang, Alberto Lumbreras, David Rohde, Yixin Wang

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts.

David Rohde

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

From Clicks to Conversions: Recommendation for long-term reward.

Philomène Chagniot, Flavian Vasile, David Rohde

CoRR, 2020

A Gentle Introduction to Recommendation as Counterfactual Policy Learning.

Flavian Vasile, David Rohde, Olivier Jeunen, Amine Benhalloum

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020

Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation.

David Rohde, Flavian Vasile, Sergey Ivanov, Otmane Sakhi

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals.

Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Joint Policy-Value Learning for Recommendation.

Olivier Jeunen, David Rohde, Flavian Vasile, Martin Bompaire

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Causal inference with Bayes rule.

Finnian Lattimore, David Rohde

CoRR, 2019

Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks.

Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile

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

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, Dominique Quadri

CoRR, 2019

On the Value of Bandit Feedback for Offline Recommender System Evaluation.

Olivier Jeunen, David Rohde, Flavian Vasile

CoRR, 2019

A Bayesian Solution to the M-Bias Problem.

David Rohde

CoRR, 2019

Replacing the do-calculus with Bayes rule.

Finnian Lattimore, David Rohde

CoRR, 2019

Three Methods for Training on Bandit Feedback.

Dmytro Mykhaylov, David Rohde, Flavian Vasile

CoRR, 2019

Latent Variable Session-Based Recommendation.

David Rohde, Stephen Bonner

CoRR, 2019

2018

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

2016

Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues.

David Rohde, Matt P. Wand

J. Mach. Learn. Res., 2016

2014

MCMC methods for univariate exponential family models with intractable normalization constants.

David Rohde, Jonathan Corcoran

Proceedings of the IEEE Workshop on Statistical Signal Processing, 2014

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

Visual Data Mining Methods for Kernel Smoothed Estimates of Cox Processes.

David Rohde, Ruth Huang, Jonathan Corcoran, Gentry White

Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining, 2013

2012

Graphical tools for conditional probabilistic exploration of multivariate spatial datasets.

David Rohde, Jonathan Corcoran

Comput. Environ. Urban Syst., 2012

Visualization of Predictive Distributions for Discrete Spatial-Temporal Log Cox Processes Approximated with MCMC.

David Rohde, Jonathan Corcoran, Gentry White, Ruth Huang

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2012, 2012

2011

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

2010

Spatial forecasting of residential urban fires: A Bayesian approach.

David Rohde, Jonathan Corcoran, Prem Chhetri

Comput. Environ. Urban Syst., 2010

2007

Development and Application of Statistical and Machine Learning Techniques in Probabilistic Astronomical Catalogue-Matching Problems

David Rohde

PhD thesis, 2007

2004

Machine Learning for Matching Astronomy Catalogues.

David Rohde, Michael Drinkwater, Marcus Gallagher, Tom Downs, Marianne Doyle

Proceedings of the Intelligent Data Engineering and Automated Learning, 2004