Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. - Adam Foster 001; Desi R. Ivanova; Ilyas Malik; Tom Rainforth (ICML, 2021)

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. - Desi R. Ivanova; Adam Foster 001; Steven Kleinegesse; Michael U. Gutmann; Thomas Rainforth (NeurIPS, 2021)

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. - Desi R. Ivanova; Joel Jennings; Cheng Zhang 005; Adam Foster 001 (CoRR, 2022)

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. - Desi R. Ivanova; Joel Jennings; Tom Rainforth; Cheng Zhang 005; Adam Foster 001 (ICML, 2023)

Differentiable Multi-Target Causal Bayesian Experimental Design. - Panagiotis Tigas; Yashas Annadani; Desi R. Ivanova; Andrew Jesson; Yarin Gal; Adam Foster 001; Stefan Bauer (ICML, 2023)

Modern Bayesian Experimental Design. - Tom Rainforth; Adam Foster 001; Desi R. Ivanova; Freddie Bickford Smith (CoRR, 2023)

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. - Marvin Schmitt; Desi R. Ivanova; Daniel Habermann; Ullrich Köthe; Paul-Christian Bürkner; Stefan T. Radev (ICML, 2024)

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 (AISTATS, 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, 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, 2025)

Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design. - Marcel Hedman; Desi R. Ivanova; Cong Guan; Tom Rainforth (CoRR, 2025)