3.4k citations
- Google (United States)US52 papers
- Google (United Kingdom)GB14 papers
- Centre de Recherche en InformatiqueFR6 papers
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR6 papers
- University of AlbertaCA6 papers
- University of OxfordGB6 papers
- University of TorontoCA5 papers
- Afterschool AllianceUS4 papers
- Carnegie Mellon UniversityUS4 papers
- École Normale Supérieure - PSLFR4 papers
- McGill UniversityCA4 papers
- Meta (Israel)IL4 papers
18 papers · 1 filter
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon with states, and actions. The pe…
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Computing a Gaussian process (GP) posterior has a computational cost cubical in the number of historical points. A reformulation of the same GP posterior highlights that this compl…
Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin +1
Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity ori…
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu +2
A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges t…
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
Tadashi Kozuno, Pierre Ménard, Rémi Munos +1
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play. Precisely, we focus on two-player, zero-sum, episodic, tabular II…
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran +4
We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering. In contrast to NeRF, our model takes into accou…