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ORCIDDavid Rohde
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2020 – today
- 2024
[c15]David Rohde
:
Why the Shooting in the Dark Method Dominates Recommender Systems Practice. RecSys 2024: 847-849- [i23]David Rohde:
Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking. CoRR abs/2402.02152 (2024) - [i22]Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces. CoRR abs/2402.14664 (2024) - [i21]Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. CoRR abs/2406.03434 (2024) - 2023
- [j6]Otmane Sakhi, David Rohde, Nicolas Chopin:
Fast Slate Policy Optimization: Going Beyond Plackett-Luce. Trans. Mach. Learn. Res. 2023 (2023) - [c14]Otmane Sakhi, David Rohde, Alexandre Gilotte:
Fast Offline Policy Optimization for Large Scale Recommendation. AAAI 2023: 9686-9694 - [c13]Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Exponential Smoothing for Off-Policy Learning. ICML 2023: 984-1017 - [e2]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:
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. Proceedings of Machine Learning Research 239, PMLR 2023 [contents] - [i20]Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Exponential Smoothing for Off-Policy Learning. CoRR abs/2305.15877 (2023) - [i19]Otmane Sakhi, David Rohde, Nicolas Chopin:
Fast Slate Policy Optimization: Going Beyond Plackett-Luce. CoRR abs/2308.01566 (2023) - 2022
- [c12]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: 4772-4773 - [e1]Javier Antorán, Arno Blaas, Fan Feng, Sahra Ghalebikesabi, Ian Mason, Melanie F. Pradier, David Rohde, Francisco J. R. Ruiz, Aaron Schein:
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. Proceedings of Machine Learning Research 187, PMLR 2022 [contents] - [i18]Benjamin Heymann, Flavian Vasile, David Rohde:
Welfare-Optimized Recommender Systems. CoRR abs/2206.13845 (2022) - [i17]Otmane Sakhi, David Rohde, Alexandre Gilotte:
Fast Offline Policy Optimization for Large Scale Recommendation. CoRR abs/2208.05327 (2022) - [i16]Imad Aouali, Achraf Ait Sidi Hammou, Sergey Ivanov, Otmane Sakhi, David Rohde, Flavian Vasile:
A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation. CoRR abs/2208.06263 (2022) - [i15]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 abs/2209.08642 (2022) - [i14]Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde:
Learning from aggregated data with a maximum entropy model. CoRR abs/2210.02450 (2022) - 2021
- [c11]David Rohde:
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts. ICBINB@NeurIPS 2021: 75-79 - [c10]Nicolas Chopin, Mike Gartrell, Dawen Liang, Alberto Lumbreras, David Rohde, Yixin Wang:
Bayesian Causal Inference for Real World Interactive Systems. KDD 2021: 4114-4115 - [c9]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: 803-805 - [i13]Imad Aouali, Sergey Ivanov, Mike Gartrell, David Rohde, Flavian Vasile, Victor Zaytsev, Diego Legrand:
Combining Reward and Rank Signals for Slate Recommendation. CoRR abs/2107.12455 (2021) - 2020
- [c8]Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile:
BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. KDD 2020: 783-793 - [c7]Olivier Jeunen, David Rohde, Flavian Vasile, Martin Bompaire:
Joint Policy-Value Learning for Recommendation. KDD 2020: 1223-1233 - [c6]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: 742-744 - [c5]Flavian Vasile, David Rohde, Olivier Jeunen, Amine Benhalloum:
A Gentle Introduction to Recommendation as Counterfactual Policy Learning. UMAP 2020: 392-393 - [i12]Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile:
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals. CoRR abs/2008.12504 (2020) - [i11]Philomène Chagniot, Flavian Vasile, David Rohde:
From Clicks to Conversions: Recommendation for long-term reward. CoRR abs/2009.00497 (2020)
2010 – 2019
- 2019
- [i10]David Rohde, Stephen Bonner:
Latent Variable Session-Based Recommendation. CoRR abs/1904.10784 (2019) - [i9]Dmytro Mykhaylov, David Rohde, Flavian Vasile:
Three Methods for Training on Bandit Feedback. CoRR abs/1904.10799 (2019) - [i8]Finnian Lattimore, David Rohde:
Replacing the do-calculus with Bayes rule. CoRR abs/1906.07125 (2019) - [i7]David Rohde:
A Bayesian Solution to the M-Bias Problem. CoRR abs/1906.07136 (2019) - [i6]Olivier Jeunen, David Rohde, Flavian Vasile:
On the Value of Bandit Feedback for Offline Recommender System Evaluation. CoRR abs/1907.12384 (2019) - [i5]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 abs/1909.04190 (2019) - [i4]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 abs/1909.08471 (2019) - [i3]Otmane Sakhi, Stephen Bonner, David Rohde, Flavian Vasile:
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks. CoRR abs/1910.00877 (2019) - [i2]Finnian Lattimore, David Rohde:
Causal inference with Bayes rule. CoRR abs/1910.01510 (2019) - 2018
- [i1]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 abs/1808.00720 (2018) - 2016
- [j5]David Rohde, Matt P. Wand:
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues. J. Mach. Learn. Res. 17: 172:1-172:47 (2016) - 2014
- [c4]David Rohde, Jonathan Corcoran:
MCMC methods for univariate exponential family models with intractable normalization constants. SSP 2014: 356-359 - 2013
- [j4]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): 401-420 (2013) - [c3]David Rohde, Ruth Huang, Jonathan Corcoran, Gentry White:
Visual Data Mining Methods for Kernel Smoothed Estimates of Cox Processes. PAKDD Workshops 2013: 83-94 - 2012
- [j3]David Rohde, Jonathan Corcoran:
Graphical tools for conditional probabilistic exploration of multivariate spatial datasets. Comput. Environ. Urban Syst. 36(5): 359-370 (2012) - [c2]David Rohde, Jonathan Corcoran, Gentry White, Ruth Huang:
Visualization of Predictive Distributions for Discrete Spatial-Temporal Log Cox Processes Approximated with MCMC. IDEAL 2012: 286-293 - 2011
- [j2]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): 193-226 (2011) - 2010
- [j1]David Rohde, Jonathan Corcoran, Prem Chhetri:
Spatial forecasting of residential urban fires: A Bayesian approach. Comput. Environ. Urban Syst. 34(1): 58-69 (2010)
2000 – 2009
- 2007
- [b1]David Rohde:
Development and Application of Statistical and Machine Learning Techniques in Probabilistic Astronomical Catalogue-Matching Problems. University of Queensland, Australia, 2007 - 2004
- [c1]David Rohde, Michael Drinkwater, Marcus Gallagher, Tom Downs, Marianne Doyle:
Machine Learning for Matching Astronomy Catalogues. IDEAL 2004: 702-707
Coauthor Index
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