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1 code implementation • 9 Mar 2023 • Aengus Lynch, Gbètondji J-S Dovonon, Jean Kaddour, Ricardo Silva

The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data.

1 code implementation • 28 Jan 2023 • Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

We introduce a causal framework for designing optimal policies that satisfy fairness constraints.

no code implementations • 30 Jun 2022 • Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).

no code implementations • 18 Jun 2022 • Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt Kusner, Ricardo Silva

We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error.

no code implementations • 26 May 2022 • Marrone Silvério Melo Dantas, Iago Richard Rodrigues, Assis Tiago Oliveira Filho, Gibson Barbosa, Daniel Bezerra, Djamel F. H. Sadok, Judith Kelner, Maria Marquezini, Ricardo Silva

We run the approach using a pose estimation task for a robotic arm and compare the results in a high-end device and a constrained device.

1 code implementation • 11 May 2022 • David S. Watson, Ricardo Silva

Under a structural assumption called the $\textit{confounder blanket principle}$, which we argue is essential for tractable causal discovery in high dimensions, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity.

no code implementations • 28 Feb 2022 • Jakob Zeitler, Ricardo Silva

Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model.

1 code implementation • 22 Feb 2022 • Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus

Causal effect estimation is important for many tasks in the natural and social sciences.

1 code implementation • 1 Feb 2022 • Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.

1 code implementation • 9 Jun 2021 • Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).

2 code implementations • NeurIPS 2021 • Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva

We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).

2 code implementations • 10 May 2021 • Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet

In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.

1 code implementation • NeurIPS 2020 • Niki Kilbertus, Matt J. Kusner, Ricardo Silva

Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making.

no code implementations • 23 May 2020 • Sorawit Saengkyongam, Ricardo Silva

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders.

1 code implementation • 3 Mar 2020 • Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

Discovering the causal effect of a decision is critical to nearly all forms of decision-making.

no code implementations • 11 Feb 2020 • Alessio Pagani, Zhuangkun Wei, Ricardo Silva, Weisi Guo

Infrastructure monitoring is critical for safe operations and sustainability.

no code implementations • 9 Dec 2019 • Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva

In this paper, we explore whether adversarial inverse RL algorithms can be adapted and trained within such latent space simulations from real market data, while maintaining their ability to recover agent rewards robust to variations in the underlying dynamics, and transfer them to new regimes of the original environment.

no code implementations • 20 Aug 2019 • Nicolo Colombo, Ricardo Silva, Soong M Kang, Arthur Gretton

The inference problem is how information concerning perturbations, with particular covariates such as location and time, can be generalized to predict the effect of novel perturbations.

1 code implementation • 1 Jul 2019 • Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.

no code implementations • 11 Jun 2019 • Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva

Given the expert agent's demonstrations, we attempt to discover their strategy by modelling their latent reward function using linear and Gaussian process (GP) regressors from previous literature, and our own approach through Bayesian neural networks (BNN).

2 code implementations • 2 Nov 2018 • Pawel Chilinski, Ricardo Silva

We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs).

2 code implementations • NeurIPS 2018 • Yin Cheng Ng, Nicolo Colombo, Ricardo Silva

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs.

no code implementations • 6 Jun 2018 • Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness.

no code implementations • 15 May 2018 • Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva

In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making.

no code implementations • 2 May 2018 • Jean-Baptiste Regli, Ricardo Silva

This paper introduces a variational approximation framework using direct optimization of what is known as the {\it scale invariant Alpha-Beta divergence} (sAB divergence).

1 code implementation • 22 Feb 2018 • Thomas E. Bartlett, Ioannis Kosmidis, Ricardo Silva

Separation of these two types of sparsity is achieved with the introduction of a novel prior structure, which draws on ideas from the Bayesian lasso and from copula modelling.

Methodology

no code implementations • NeurIPS 2017 • Nicolò Colombo, Ricardo Silva, Soong Moon Kang

The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations.

no code implementations • NeurIPS 2017 • Chris Russell, Matt J. Kusner, Joshua Loftus, Ricardo Silva

In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification.

no code implementations • 11 Oct 2017 • Yin Cheng Ng, Ricardo Silva

We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense.

2 code implementations • NeurIPS 2017 • Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.

no code implementations • NeurIPS 2016 • Yin Cheng Ng, Pawel Chilinski, Ricardo Silva

Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences.

no code implementations • NeurIPS 2016 • Ricardo Silva

In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes.

no code implementations • 9 Nov 2015 • Ricardo Silva, Shohei Shimizu

Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions.

no code implementations • 9 Nov 2015 • Ricardo Silva

Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplication will result in a copula model, as discussed by Liebscher (J Mult Analysis, 2008).

no code implementations • 9 Aug 2014 • Ricardo Silva, Robert B. Gramacy

In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure.

no code implementations • NeurIPS 2014 • Ricardo Silva, Robin Evans

The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths.

no code implementations • NeurIPS 2013 • Alfredo Kalaitzis, Ricardo Silva

Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction.

no code implementations • NeurIPS 2011 • Ricardo Silva

In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features.

no code implementations • 28 Dec 2009 • Ricardo Silva, Katherine Heller, Zoubin Ghahramani, Edoardo M. Airoldi

Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?

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