Search Results for author:
Found 17 papers, 14 papers with code
Prediction-Oriented Bayesian Active Learning
Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BALD score.
Modern Bayesian Experimental Design
Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments.
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
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.
Differentiable Multi-Target Causal Bayesian Experimental Design
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.
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design.
Learning Instance-Specific Augmentations by Capturing Local Invariances
We introduce InstaAug, a method for automatically learning input-specific augmentations from data.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models.
On Contrastive Representations of Stochastic Processes
Learning representations of stochastic processes is an emerging problem in machine learning with applications from meta-learning to physical object models to time series.
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including computational biology.
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
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.
Improving Transformation Invariance in Contrastive Representation Learning
We propose methods to strengthen the invariance properties of representations obtained by contrastive learning.
Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs
In this paper, applying the idea of randomized multilevel Monte Carlo (MLMC) methods, we introduce an unbiased Monte Carlo estimator for the gradient of the expected information gain with finite expected squared $\ell_2$-norm and finite expected computational cost per sample.
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED).
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources.
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
Empirical evidence suggests that heavy-tailed degree distributions occurring in many real networks are well-approximated by power laws with exponents $\eta$ that may take values either less than and greater than two.
Probabilistic Verb Selection for Data-to-Text Generation
In data-to-text Natural Language Generation (NLG) systems, computers need to find the right words to describe phenomena seen in the data.
