| 2025 | AISTATS | 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 |
| 2025 | ICML | Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints. | Sam Bowyer, Laurence Aitchison, Desi R. Ivanova |
| 2025 | ICML | Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design. | Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth |
| 2024 | ICML | Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. | Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Kthe, Paul-Christian Brkner, Stefan T. Radev |
| 2023 | ICML | CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. | Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster |
| 2023 | ICML | Differentiable Multi-Target Causal Bayesian Experimental Design. | Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer |
| 2021 | ICML | Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. | Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth |