156 citations
- Amazon (United States)US12 papers
- Massachusetts Institute of TechnologyUS7 papers
- Carnegie Mellon UniversityUS6 papers
- Google (United States)US5 papers
- Johns Hopkins UniversityUS5 papers
- Meta (Israel)IL5 papers
- California Southern UniversityUS4 papers
- Microsoft Research (United Kingdom)GB4 papers
- Toyota Technological Institute at ChicagoUS4 papers
- University of California, Los AngelesUS4 papers
- University of Southern CaliforniaUS4 papers
- National Yang Ming Chiao Tung UniversityTW3 papers
5 papers · 2 filters
Practical and sample efficient zero-shot HPO
Fela Winkelmolen, Nikita Ivkin, H. Furkan Bozkurt +1
Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement e…
Causal Bayesian Optimization
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes +1
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem aris…
A Linear Bandit for Seasonal Environments
Giuseppe Di Benedetto, Vito Bellini, Giovanni Zappella
Contextual bandit algorithms are extremely popular and widely used in recommendation systems to provide online personalised recommendations. A recurrent assumption is the stationar…
Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler +2
Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world. However, current…
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi, Runzhe Wan, Rui Song +2
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. In this paper, we propose a novel Forward-Backward Learning procedure to test MA in s…