Scott Sussex

According to our database, Scott Sussex authored at least 9 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Standardizing Structural Causal Models.

Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Adversarial Causal Bayesian Optimization.

Scott Sussex, Pier Giuseppe Sessa, Anastasia Makarova, Andreas Krause

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Model-based Causal Bayesian Optimization.

Scott Sussex, Pier Giuseppe Sessa, Anastasiia Makarova, Andreas Krause

CoRR, 2023

Model-based Causal Bayesian Optimization.

Scott Sussex, Anastasia Makarova, Andreas Krause

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Model-based Causal Bayesian Optimization.

Scott Sussex, Anastasiia Makarova, Andreas Krause

CoRR, 2022

Learning Long-Term Crop Management Strategies with CyclesGym.

Matteo Turchetta, Luca Corinzia, Scott Sussex, Amanda Burton, Juan Herrera, Ioannis Athanasiadis, Joachim M. Buhmann, Andreas Krause

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Amortized Inference for Causal Structure Learning.

Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning.

Scott Sussex, Caroline Uhler, Andreas Krause

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2019

Combining parametric and nonparametric models for off-policy evaluation.

Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez

Proceedings of the 36th International Conference on Machine Learning, 2019