Search Results for author: Ioana Bica

Found 19 papers, 7 papers with code

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

no code implementations16 Jun 2022 Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar

This allows us to evaluate treatment effect estimators along a new and important dimension that has been overlooked in previous work: We construct a benchmarking environment to empirically investigate the ability of personalized treatment effect models to identify predictive covariates -- covariates that determine differential responses to treatment.

Feature Importance

Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

no code implementations13 Jan 2022 Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic

Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings.

Representation Learning Self-Supervised Image Classification +2

Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time

no code implementations7 Dec 2021 Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin McKinney, Mihaela van der Schaar

Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time.

Time-series Generation by Contrastive Imitation

no code implementations NeurIPS 2021 Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

In this work, we study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy, where the reinforcement signal is provided by a global (but stepwise-decomposable) energy model trained by contrastive estimation.

Time Series

Invariant Causal Imitation Learning for Generalizable Policies

no code implementations NeurIPS 2021 Ioana Bica, Daniel Jarrett, Mihaela van der Schaar

By leveraging data from multiple environments, we propose Invariant Causal Imitation Learning (ICIL), a novel technique in which we learn a feature representation that is invariant across domains, on the basis of which we learn an imitation policy that matches expert behavior.

Imitation Learning

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes

1 code implementation NeurIPS 2021 Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

Most of the medical observational studies estimate the causal treatment effects using electronic health records (EHR), where a patient's covariates and outcomes are both observed longitudinally.

The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation

1 code implementation8 Jun 2021 Alex J. Chan, Ioana Bica, Alihan Huyuk, Daniel Jarrett, Mihaela van der Schaar

Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes.

Decision Making

Model-Attentive Ensemble Learning for Sequence Modeling

no code implementations23 Feb 2021 Victor D. Bourgin, Ioana Bica, Mihaela van der Schaar

Medical time-series datasets have unique characteristics that make prediction tasks challenging.

Ensemble Learning Time Series

Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge

no code implementations11 Feb 2021 Trent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar

We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting.

Causal Inference Model Selection +1

Learning Matching Representations for Individualized Organ Transplantation Allocation

1 code implementation28 Jan 2021 Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar

Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients.

Representation Learning

SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding

no code implementations1 Jan 2021 Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

Estimating causal treatment effects using observational data is a problem with few solutions when the confounder has a temporal structure, e. g. the history of disease progression might impact both treatment decisions and clinical outcomes.

Clairvoyance: A Pipeline Toolkit for Medical Time Series

no code implementations ICLR 2021 Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar

Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.

AutoML Time Series

Learning "What-if" Explanations for Sequential Decision-Making

no code implementations ICLR 2021 Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i. e. trajectories of observations and actions made by an expert maximizing some unknown reward function -- is essential for introspecting and auditing policies in different institutions.

Decision Making reinforcement-learning

Strictly Batch Imitation Learning by Energy-based Distribution Matching

1 code implementation NeurIPS 2020 Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

Through experiments with application to control and healthcare settings, we illustrate consistent performance gains over existing algorithms for strictly batch imitation learning.

Imitation Learning

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

1 code implementation NeurIPS 2020 Ioana Bica, James Jordon, Mihaela van der Schaar

While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter.

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

3 code implementations ICLR 2020 Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.

Cannot find the paper you are looking for? You can Submit a new open access paper.