Silvia Chiappa | DeepAI

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∙ 06/13/2023

Additive Causal Bandits with Unknown Graph

We explore algorithms to select actions in the causal bandit setting whe...

0 Alan Malek, et al. ∙

∙ 06/10/2023

Functional Causal Bayesian Optimization

We propose functional causal Bayesian optimization (fCBO), a method for ...

0 Limor Gultchin, et al. ∙

∙ 05/31/2023

Constrained Causal Bayesian Optimization

We propose constrained causal Bayesian optimization (cCBO), an approach ...

10 Virginia Aglietti, et al. ∙

∙ 04/12/2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and c...

0 Nan Rosemary Ke, et al. ∙

∙ 04/11/2022

Learning to Induce Causal Structure

The fundamental challenge in causal induction is to infer the underlying...

0 Nan Rosemary Ke, et al. ∙

∙ 02/22/2022

Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness

In addition to reproducing discriminatory relationships in the training ...

0 Carolyn Ashurst, et al. ∙

∙ 02/02/2022

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?

Fairness and robustness are often considered as orthogonal dimensions wh...

31 Jessica Schrouff, et al. ∙

∙ 10/21/2021

Statistical discrimination in learning agents

Undesired bias afflicts both human and algorithmic decision making, and ...

0 Edgar A. Duéñez-Guzmán, et al. ∙

∙ 07/02/2021

Prequential MDL for Causal Structure Learning with Neural Networks

Learning the structure of Bayesian networks and causal relationships fro...

0 Jörg Bornschein, et al. ∙

∙ 01/06/2021

Fairness with Continuous Optimal Transport

Whilst optimal transport (OT) is increasingly being recognized as a powe...

0 Silvia Chiappa, et al. ∙

∙ 12/31/2020

Fairness in Machine Learning

Machine learning based systems are reaching society at large and in many...

0 Luca Oneto, et al. ∙

∙ 09/12/2019

Explicit-Duration Markov Switching Models

Markov switching models (MSMs) are probabilistic models that employ mult...

0 Silvia Chiappa, et al. ∙

∙ 07/28/2019

Wasserstein Fair Classification

We propose an approach to fair classification that enforces independence...

7 Ray Jiang, et al. ∙

∙ 07/20/2019

Unsupervised Separation of Dynamics from Pixels

We present an approach to learn the dynamics of multiple objects from im...

0 Silvia Chiappa, et al. ∙

∙ 07/15/2019

A Causal Bayesian Networks Viewpoint on Fairness

We offer a graphical interpretation of unfairness in a dataset as the pr...

0 Silvia Chiappa, et al. ∙

∙ 05/08/2019

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for buildi...

16 Pedro A. Ortega, et al. ∙

∙ 02/27/2019

Degenerate Feedback Loops in Recommender Systems

Machine learning is used extensively in recommender systems deployed in ...

14 Ray Jiang, et al. ∙

∙ 01/23/2019

Causal Reasoning from Meta-reinforcement Learning

Discovering and exploiting the causal structure in the environment is a ...

0 Ishita Dasgupta, et al. ∙

∙ 02/22/2018

Path-Specific Counterfactual Fairness

We consider the problem of learning fair decision systems in complex sce...

0 Silvia Chiappa, et al. ∙

∙ 04/07/2017

Recurrent Environment Simulators

Models that can simulate how environments change in response to actions ...

0 Silvia Chiappa, et al. ∙

∙ 04/11/2011

Unified Treatment of Hidden Markov Switching Models

Many real-world problems encountered in several disciplines deal with th...

0 Silvia Chiappa, et al. ∙

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