You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 18 Jun 2022 • Hyungrok Do, Preston Putzel, Axel Martin, Padhraic Smyth, Judy Zhong

In addition, we demonstrate that the fair GLM can generate fair predictions for a range of response variables, other than binary and continuous outcomes.

1 code implementation • NeurIPS 2021 • Gavin Kerrigan, Padhraic Smyth, Mark Steyvers

An increasingly common use case for machine learning models is augmenting the abilities of human decision makers.

no code implementations • 12 May 2021 • Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.

no code implementations • NeurIPS 2021 • Aodong Li, Alex Boyd, Padhraic Smyth, Stephan Mandt

We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity.

no code implementations • pproximateinference AABI Symposium 2021 • Aodong Li, Alex James Boyd, Padhraic Smyth, Stephan Mandt

We consider the problem of online learning in the presence of sudden distribution shifts, which may be hard to detect and can lead to a slow but steady degradation in model performance.

1 code implementation • NeurIPS 2020 • Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence.

no code implementations • NeurIPS 2020 • Disi Ji, Padhraic Smyth, Mark Steyvers

We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful.

1 code implementation • 16 Feb 2020 • Disi Ji, Robert L. Logan IV, Padhraic Smyth, Mark Steyvers

Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications.

1 code implementation • 9 Oct 2018 • Eric Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth

We propose a novel framework for understanding multiplicative noise in neural networks, considering continuous distributions as well as Bernoulli noise (i. e. dropout).

no code implementations • 21 Nov 2017 • Disi Ji, Eric Nalisnick, Padhraic Smyth

Analysis of flow cytometry data is an essential tool for clinical diagnosis of hematological and immunological conditions.

no code implementations • 4 Apr 2017 • Eric Nalisnick, Padhraic Smyth

Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective priors.

no code implementations • 11 Jan 2017 • Tracy Holsclaw, Arthur M. Greene, Andrew W. Robertson, Padhraic Smyth

We extend such models to introduce additional non-homogeneity into the emission distribution using a generalized linear model (GLM), with data augmentation for sampling-based inference.

2 code implementations • 20 May 2016 • Eric Nalisnick, Padhraic Smyth

We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes.

no code implementations • 10 Jun 2015 • Eric Nalisnick, Anima Anandkumar, Padhraic Smyth

Corrupting the input and hidden layers of deep neural networks (DNNs) with multiplicative noise, often drawn from the Bernoulli distribution (or 'dropout'), provides regularization that has significantly contributed to deep learning's success.

no code implementations • 20 Dec 2014 • Kevin Bache, Dennis Decoste, Padhraic Smyth

We describe a general framework for online adaptation of optimization hyperparameters by `hot swapping' their values during learning.

no code implementations • 10 May 2013 • James Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, Max Welling

We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method.

1 code implementation • 11 Jul 2012 • Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth

A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors.

1 code implementation • 9 May 2012 • Arthur Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh

Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data.

no code implementations • NeurIPS 2011 • Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion

The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science.

no code implementations • NeurIPS 2010 • America Chambers, Padhraic Smyth, Mark Steyvers

We describe a generative model that is based on a stick-breaking process for graphs, and a Markov Chain Monte Carlo inference procedure.

no code implementations • NeurIPS 2009 • Andrew Frank, Padhraic Smyth, Alexander T. Ihler

Since the development of loopy belief propagation, there has been considerable work on advancing the state of the art for approximate inference over distributions defined on discrete random variables.

no code implementations • NeurIPS 2008 • Padhraic Smyth, Max Welling, Arthur U. Asuncion

Distributed learning is a problem of fundamental interest in machine learning and cognitive science.

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

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.