David Rohde - RankMe

53 papers A* 6A 6C 2Journal 32Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Point Process Model for Optimizing Repeated Personalized Action Delivery to Users. CoRR Alexander Merkov, David Rohde
2025	A	`conf`	Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. AISTATS Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
2025	J	`jnl`	Confounding is a Pervasive Problem in Real World Recommender Systems. CoRR Alexander Merkov, David Rohde, Alexandre Gilotte, Benjamin Heymann
2025	J	`jnl`	Practical Improvements of A/B Testing with Off-Policy Estimation. CoRR Otmane Sakhi, Alexandre Gilotte, David Rohde
2024	J	`jnl`	Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. CoRR Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
2024	J	`jnl`	Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking. CoRR David Rohde
2024	A	`conf`	Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. UAI Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
2024	J	`jnl`	Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. CoRR Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
2024	—	`conf`	Welfare-Optimized Recommender Systems. SURE@RecSys Benjamin Heymann, Flavian Vasile, David Rohde
2024	A	`conf`	Why the Shooting in the Dark Method Dominates Recommender Systems Practice. RecSys David Rohde
2023	A*	`conf`	Exponential Smoothing for Off-Policy Learning. ICML Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
2023	J	`jnl`	Exponential Smoothing for Off-Policy Learning. CoRR Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
2023	A*	`conf`	Fast Offline Policy Optimization for Large Scale Recommendation. AAAI Otmane Sakhi, David Rohde, Alexandre Gilotte
2023	J	`jnl`	Fast Slate Policy Optimization: Going Beyond Plackett-Luce. CoRR Otmane Sakhi, David Rohde, Nicolas Chopin
2023	J	`jnl`	Fast Slate Policy Optimization: Going Beyond Plackett-Luce. Trans. Mach. Learn. Res. Otmane Sakhi, David Rohde, Nicolas Chopin
2023	—	`ed.`	Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 16 December 2023, New Orleans, Louisiana, USA. ICBINB Javier Antorán, Arno Blaas, Kelly Buchanan, Fan Feng, Vincent Fortuin, Sahra Ghalebikesabi, Andreas Kriegler, Ian Mason, David Rohde, Francisco J. R. Ruiz, Tobias Uelwer, Yubin Xie, Rui Yang
2022	J	`jnl`	A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation. CoRR Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian Vasile
2022	J	`jnl`	Fast Offline Policy Optimization for Large Scale Recommendation. CoRR Otmane Sakhi, David Rohde, Alexandre Gilotte
2022	J	`jnl`	Learning from aggregated data with a maximum entropy model. CoRR Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde
2022	J	`jnl`	Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation. CoRR Imad Aouali, Amine Benhalloum, Martin Bompaire, Benjamin Heymann, Olivier Jeunen, David Rohde, Otmane Sakhi, Flavian Vasile
2022	—	`ed.`	Proceedings on "I Can't Believe It's Not Better! - Understanding Deep Learning Through Empirical Falsification" at NeurIPS 2022 Workshops, 03 December 2022, New Orleans, Louisiana, USA ICBINB Javier Antorán, Arno Blaas, Fan Feng, Sahra Ghalebikesabi, Ian Mason, Melanie F. Pradier, David Rohde, Francisco J. R. Ruiz, Aaron Schein
2022	A*	`conf`	Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search. KDD Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono
2022	J	`jnl`	Welfare-Optimized Recommender Systems. CoRR Benjamin Heymann, Flavian Vasile, David Rohde
2021	A*	`conf`	Bayesian Causal Inference for Real World Interactive Systems. KDD Nicolas Chopin, Mike Gartrell, Dawen Liang, Alberto Lumbreras, David Rohde, Yixin Wang
2021	—	`conf`	Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. ICBINB@NeurIPS David Rohde
2021	J	`jnl`	Combining Reward and Rank Signals for Slate Recommendation. CoRR Imad Aouali, Sergey Ivanov, Mike Gartrell, David Rohde, Flavian Vasile, Victor Zaytsev, Diego Legrand
2021	A	`conf`	SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. RecSys Michael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, Manel Slokom
2020	A	`conf`	A Gentle Introduction to Recommendation as Counterfactual Policy Learning. UMAP Flavian Vasile, David Rohde, Olivier Jeunen, Amine Benhalloum
2020	J	`jnl`	BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. CoRR Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
2020	A*	`conf`	BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. KDD Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
2020	A	`conf`	Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendation. RecSys David Rohde, Flavian Vasile, Sergey Ivanov, Otmane Sakhi
2020	J	`jnl`	From Clicks to Conversions: Recommendation for long-term reward. CoRR Philomène Chagniot, Flavian Vasile, David Rohde
2020	A*	`conf`	Joint Policy-Value Learning for Recommendation. KDD Olivier Jeunen, David Rohde, Flavian Vasile, Martin Bompaire
2019	J	`jnl`	A Bayesian Solution to the M-Bias Problem. CoRR David Rohde
2019	J	`jnl`	Causal inference with Bayes rule. CoRR Finnian Lattimore, David Rohde
2019	J	`jnl`	Latent Variable Session-Based Recommendation. CoRR David Rohde, Stephen Bonner
2019	J	`jnl`	Learning from Bandit Feedback: An Overview of the State-of-the-art. CoRR Olivier Jeunen, Dmytro Mykhaylov, David Rohde, Flavian Vasile, Alexandre Gilotte, Martin Bompaire
2019	J	`jnl`	On the Value of Bandit Feedback for Offline Recommender System Evaluation. CoRR Olivier Jeunen, David Rohde, Flavian Vasile
2019	J	`jnl`	Recommendation System-based Upper Confidence Bound for Online Advertising. CoRR Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam, David Rohde, Flavian Vasile, Elena Simona Lohan, Steven Martin, Dominique Quadri
2019	J	`jnl`	Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks. CoRR Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile
2019	J	`jnl`	Replacing the do-calculus with Bayes rule. CoRR Finnian Lattimore, David Rohde
2019	J	`jnl`	Three Methods for Training on Bandit Feedback. CoRR Dmytro Mykhaylov, David Rohde, Flavian Vasile
2018	J	`jnl`	RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. CoRR David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, Alexandros Karatzoglou
2016	J	`jnl`	Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues. J. Mach. Learn. Res. David Rohde, Matt P. Wand
2014	—	`conf`	MCMC methods for univariate exponential family models with intractable normalization constants. SSP David Rohde, Jonathan Corcoran
2013	J	`jnl`	The Sensitivity of the Number of Clusters in a Gaussian Mixture Model to Prior Distributions. Math. Comput. Sci. Cristian Cruz, William Lima Leão, David Rohde
2013	—	`conf`	Visual Data Mining Methods for Kernel Smoothed Estimates of Cox Processes. PAKDD Workshops David Rohde, Ruth Huang, Jonathan Corcoran, Gentry White
2012	J	`jnl`	Graphical tools for conditional probabilistic exploration of multivariate spatial datasets. Comput. Environ. Urban Syst. David Rohde, Jonathan Corcoran
2012	C	`conf`	Visualization of Predictive Distributions for Discrete Spatial-Temporal Log Cox Processes Approximated with MCMC. IDEAL David Rohde, Jonathan Corcoran, Gentry White, Ruth Huang
2011	J	`jnl`	Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study. J. Geogr. Syst. Jonathan Corcoran, Gary Higgs, David Rohde, Prem Chhetri
2010	J	`jnl`	Spatial forecasting of residential urban fires: A Bayesian approach. Comput. Environ. Urban Syst. David Rohde, Jonathan Corcoran, Prem Chhetri
2007	—	`—`	Development and Application of Statistical and Machine Learning Techniques in Probabilistic Astronomical Catalogue-Matching Problems David Rohde
2004	C	`conf`	Machine Learning for Matching Astronomy Catalogues. IDEAL David Rohde, Michael Drinkwater, Marcus Gallagher, Tom Downs, Marianne Doyle