Author name cluster

Desi R. Ivanova

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

ICML Conference 2025 Conference Paper

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

Marcel Hedman
Desi R. Ivanova
Cong Guan
Tom Rainforth

We develop a semi-amortized, policy-based, approach to Bayesian experimental design (BED) called Stepwise Deep Adaptive Design (Step-DAD). Like existing, fully amortized, policy-based BED approaches, Step-DAD trains a design policy upfront before the experiment. However, rather than keeping this policy fixed, Step-DAD periodically updates it as data is gathered, refining it to the particular experimental instance. This test-time adaptation improves both the flexibility and the robustness of the design strategy compared with existing approaches. Empirically, Step-DAD consistently demonstrates superior decision-making and robustness compared with current state-of-the-art BED methods.

Details

ICML Conference 2024 Conference Paper

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference

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

We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data. In a nutshell, we invert Bayes’ theorem and estimate the marginal likelihood based on approximate representations of the joint model. Upon perfect approximation, the marginal likelihood is constant across all parameter values by definition. However, errors in approximate inference lead to undesirable variance in the marginal likelihood estimates across different parameter values. We penalize violations of this symmetry with a self-consistency loss which significantly improves the quality of approximate inference in low data regimes and can be used to augment the training of popular neural density estimators. We apply our method to a number of synthetic problems and realistic scientific models, discovering notable advantages in the context of both neural posterior and likelihood approximation.

Details

ICML Conference 2023 Conference Paper

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

Desi R. Ivanova
Joel Jennings
Tom Rainforth
Cheng Zhang 0005
Adam Foster 0001

We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED—a general, model-agnostic framework for designing contextual experiments using information-theoretic principles. After formulating a suitable information-based objective, we employ black-box variational methods to simultaneously estimate it and optimize the designs in a single stochastic gradient scheme. In addition, to accommodate discrete actions within our framework, we propose leveraging continuous relaxation schemes, which can naturally be integrated into our variational objective. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems. We illustrate its effectiveness in a number of experiments, where CO-BED demonstrates competitive performance even when compared to bespoke, model-specific alternatives.

Details

ICML Conference 2023 Conference Paper

Differentiable Multi-Target Causal Bayesian Experimental Design

Panagiotis Tigas
Yashas Annadani
Desi R. Ivanova
Andrew Jesson
Yarin Gal
Adam Foster 0001
Stefan Bauer

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting — a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-value pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.

Details

ICML Conference 2021 Conference Paper

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

Adam Foster 0001
Desi R. Ivanova
Ilyas Malik
Tom Rainforth

We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of adaptive Bayesian experimental design that allows experiments to be run in real-time. Traditional sequential Bayesian optimal experimental design approaches require substantial computation at each stage of the experiment. This makes them unsuitable for most real-world applications, where decisions must typically be made quickly. DAD addresses this restriction by learning an amortized design network upfront and then using this to rapidly run (multiple) adaptive experiments at deployment time. This network represents a design policy which takes as input the data from previous steps, and outputs the next design using a single forward pass; these design decisions can be made in milliseconds during the live experiment. To train the network, we introduce contrastive information bounds that are suitable objectives for the sequential setting, and propose a customized network architecture that exploits key symmetries. We demonstrate that DAD successfully amortizes the process of experimental design, outperforming alternative strategies on a number of problems.

Details