Search Results for author: Silvia Chiappa

Found 19 papers, 2 papers with code

Learning to Induce Causal Structure

no code implementations11 Apr 2022 Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jorg Bornschein, Theophane Weber, Anirudh Goyal, Matthew Botvinic, Michael Mozer, Danilo Jimenez Rezende

The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.

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

no code implementations22 Feb 2022 Carolyn Ashurst, Ryan Carey, Silvia Chiappa, Tom Everitt

In addition to reproducing discriminatory relationships in the training data, machine learning systems can also introduce or amplify discriminatory effects.

Asymptotically Best Causal Effect Identification with Multi-Armed Bandits

no code implementations NeurIPS 2021 Alan Malek, Silvia Chiappa

This paper considers the problem of selecting a formula for identifying a causal quantity of interest among a set of available formulas.

Multi-Armed Bandits

Statistical discrimination in learning agents

no code implementations21 Oct 2021 Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo

Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.

Decision Making Multi-agent Reinforcement Learning

Prequential MDL for Causal Structure Learning with Neural Networks

no code implementations2 Jul 2021 Jorg Bornschein, Silvia Chiappa, Alan Malek, Rosemary Nan Ke

Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology.

Fairness with Continuous Optimal Transport

no code implementations6 Jan 2021 Silvia Chiappa, Aldo Pacchiano

Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the use of discrete OT.

Fairness

Fairness in Machine Learning

no code implementations31 Dec 2020 Luca Oneto, Silvia Chiappa

Machine learning based systems are reaching society at large and in many aspects of everyday life.

Fairness

Explicit-Duration Markov Switching Models

no code implementations12 Sep 2019 Silvia Chiappa

Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time.

Time Series

Wasserstein Fair Classification

1 code implementation28 Jul 2019 Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances.

Classification Fairness +1

Unsupervised Separation of Dynamics from Pixels

no code implementations20 Jul 2019 Silvia Chiappa, Ulrich Paquet

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way.

A Causal Bayesian Networks Viewpoint on Fairness

no code implementations15 Jul 2019 Silvia Chiappa, William S. Isaac

We show that causal Bayesian networks provide us with a powerful tool to measure unfairness in a dataset and to design fair models in complex unfairness scenarios.

Fairness

Path-Specific Counterfactual Fairness

no code implementations22 Feb 2018 Silvia Chiappa, Thomas P. S. Gillam

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways.

Fairness

Recurrent Environment Simulators

no code implementations7 Apr 2017 Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, Shakir Mohamed

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently.

Atari Games Car Racing

Movement extraction by detecting dynamics switches and repetitions

no code implementations NeurIPS 2010 Silvia Chiappa, Jan R. Peters

Many time-series such as human movement data consist of a sequence of basic actions, e. g., forehands and backhands in tennis.

Time Series

Using Bayesian Dynamical Systems for Motion Template Libraries

no code implementations NeurIPS 2008 Silvia Chiappa, Jens Kober, Jan R. Peters

Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.

Imitation Learning Time Series

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