Desi R. Ivanova

According to our database, Desi R. Ivanova authored at least 11 papers between 2021 and 2025.

Collaborative distances:

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

2025

Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design.

Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth

CoRR, July, 2025

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs.

T. Duy Nguyen-Hien, Desi R. Ivanova, Yee Whye Teh, Wee Sun Lee

CoRR, June, 2025

Position: Don't use the CLT in LLM evals with fewer than a few hundred datapoints.

Sam Bowyer, Laurence Aitchison, Desi R. Ivanova

CoRR, March, 2025

Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition.

Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference.

Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Modern Bayesian Experimental Design.

Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith

CoRR, 2023

Differentiable Multi-Target Causal Bayesian Experimental Design.

Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer

Proceedings of the International Conference on Machine Learning, 2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design.

Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

Proceedings of the International Conference on Machine Learning, 2023

2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation.

Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster

CoRR, 2022

2021

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods.

Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

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

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design.

Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021