David Rohde

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 35

Top Topics

Upper Confidence Bound

Policy Evaluation

Causal Inference

Exponential Family

Top Venues

Publications

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. CoRR (2024)
Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. CoRR (2024)
David Rohde
Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking. CoRR (2024)
Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
Exponential Smoothing for Off-Policy Learning. CoRR (2023)
Otmane Sakhi, David Rohde, Alexandre Gilotte
Fast Offline Policy Optimization for Large Scale Recommendation. AAAI (2023)
Otmane Sakhi, David Rohde, Nicolas Chopin
Fast Slate Policy Optimization: Going Beyond Plackett-Luce. Trans. Mach. Learn. Res. 2023 (2023)
Otmane Sakhi, David Rohde, Nicolas Chopin
Fast Slate Policy Optimization: Going Beyond Plackett-Luce. CoRR (2023)
Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
Exponential Smoothing for Off-Policy Learning. ICML (2023)
Benjamin Heymann, Flavian Vasile, David Rohde
Welfare-Optimized Recommender Systems. CoRR (2022)
Imad Aouali, Amine Benhalloum, Martin Bompaire, Benjamin Heymann, Olivier Jeunen, David Rohde, Otmane Sakhi, Flavian Vasile
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation. CoRR (2022)
Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono
Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search. KDD (2022)
Otmane Sakhi, David Rohde, Alexandre Gilotte
Fast Offline Policy Optimization for Large Scale Recommendation. CoRR (2022)
Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian Vasile
A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation. CoRR (2022)
Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde
Learning from aggregated data with a maximum entropy model. CoRR (2022)
Michael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, Manel Slokom
SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. RecSys (2021)
David Rohde
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. ICBINB@NeurIPS (2021)
Imad Aouali, Sergey Ivanov, Mike Gartrell, David Rohde, Flavian Vasile, Victor Zaytsev, Diego Legrand
Combining Reward and Rank Signals for Slate Recommendation. CoRR (2021)
Nicolas Chopin, Mike Gartrell, Dawen Liang, Alberto Lumbreras, David Rohde, Yixin Wang
Bayesian Causal Inference for Real World Interactive Systems. KDD (2021)
Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. CoRR (2020)
Flavian Vasile, David Rohde, Olivier Jeunen, Amine Benhalloum
A Gentle Introduction to Recommendation as Counterfactual Policy Learning. UMAP (2020)
David Rohde, Flavian Vasile, Sergey Ivanov, Otmane Sakhi
Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation. RecSys (2020)
Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. KDD (2020)
Olivier Jeunen, David Rohde, Flavian Vasile, Martin Bompaire
Joint Policy-Value Learning for Recommendation. KDD (2020)
Philomène Chagniot, Flavian Vasile, David Rohde
From Clicks to Conversions: Recommendation for long-term reward. CoRR (2020)
Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks. CoRR (2019)
David Rohde, Stephen Bonner
Latent Variable Session-Based Recommendation. CoRR (2019)
Olivier Jeunen, Dmytro Mykhaylov, David Rohde, Flavian Vasile, Alexandre Gilotte, Martin Bompaire
Learning from Bandit Feedback: An Overview of the State-of-the-art. CoRR (2019)
Olivier Jeunen, David Rohde, Flavian Vasile
On the Value of Bandit Feedback for Offline Recommender System Evaluation. CoRR (2019)
Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam, David Rohde, Flavian Vasile, Elena Simona Lohan, Steven Martin, Dominique Quadri
Recommendation System-based Upper Confidence Bound for Online Advertising. CoRR (2019)
Finnian Lattimore, David Rohde
Replacing the do-calculus with Bayes rule. CoRR (2019)
Dmytro Mykhaylov, David Rohde, Flavian Vasile
Three Methods for Training on Bandit Feedback. CoRR (2019)
David Rohde
A Bayesian Solution to the M-Bias Problem. CoRR (2019)
Finnian Lattimore, David Rohde
Causal inference with Bayes rule. CoRR (2019)
David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, Alexandros Karatzoglou
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. CoRR (2018)
David Rohde, Matt P. Wand
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues. J. Mach. Learn. Res. 17 (2016)
David Rohde, Jonathan Corcoran
MCMC methods for univariate exponential family models with intractable normalization constants. SSP (2014)
David Rohde, Ruth Huang, Jonathan Corcoran, Gentry White
Visual Data Mining Methods for Kernel Smoothed Estimates of Cox Processes. PAKDD Workshops (2013)
Cristian Cruz, William Lima Leão, David Rohde
The Sensitivity of the Number of Clusters in a Gaussian Mixture Model to Prior Distributions. Math. Comput. Sci. 7 (4) (2013)
David Rohde, Jonathan Corcoran
Graphical tools for conditional probabilistic exploration of multivariate spatial datasets. Comput. Environ. Urban Syst. 36 (5) (2012)
David Rohde, Jonathan Corcoran, Gentry White, Ruth Huang
Visualization of Predictive Distributions for Discrete Spatial-Temporal Log Cox Processes Approximated with MCMC. IDEAL (2012)
Jonathan Corcoran, Gary Higgs, David Rohde, Prem Chhetri
Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study. J. Geogr. Syst. 13 (2) (2011)
David Rohde, Jonathan Corcoran, Prem Chhetri
Spatial forecasting of residential urban fires: A Bayesian approach. Comput. Environ. Urban Syst. 34 (1) (2010)
David Rohde, Michael Drinkwater, Marcus Gallagher, Tom Downs, Marianne Doyle
Machine Learning for Matching Astronomy Catalogues. IDEAL (2004)