Ivanova, Desi R.

11 publications

AISTATS 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

ICML 2025 Position: Don’t Use the CLT in LLM Evals with Fewer than a Few Hundred Datapoints Sam Bowyer, Laurence Aitchison, Desi R. Ivanova

ICLRW 2025 Semantic-Level Confidence Calibration of Language Models via Temperature Scaling Tom A. Lamb, Desi R. Ivanova, Philip Torr, Tim G. J. Rudner

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

NeurIPSW 2024 Data-Efficient Variational Mutual Information Estimation via Bayesian Self-Consistency Desi R. Ivanova, Marvin Schmitt, Stefan T. Radev

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

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

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

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

ICML 2021 Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster, Desi R Ivanova, Ilyas Malik, Tom Rainforth

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