Search Results for author: Padhraic Smyth

Found 24 papers, 8 papers with code

Fair Generalized Linear Models with a Convex Penalty

1 code implementation18 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.


Automating Data Science: Prospects and Challenges

no code implementations12 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.

AutoML BIG-bench Machine Learning

Variational Beam Search for Novelty Detection

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.

online learning

User-Dependent Neural Sequence Models for Continuous-Time Event Data

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.

Variational Inference

Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference

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.

Bayesian Inference Fairness

Active Bayesian Assessment for Black-Box Classifiers

1 code implementation16 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.

text-classification Text Classification

Dropout as a Structured Shrinkage Prior

1 code implementation9 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).

Bayesian Inference

Mondrian Processes for Flow Cytometry Analysis

no code implementations21 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.

Learning Approximately Objective Priors

no code implementations4 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.

Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling

no code implementations11 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.

Data Augmentation speech-recognition +1

Stick-Breaking Variational Autoencoders

2 code implementations20 May 2016 Eric Nalisnick, Padhraic Smyth

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

A Scale Mixture Perspective of Multiplicative Noise in Neural Networks

no code implementations10 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.

Model Compression

Hot Swapping for Online Adaptation of Optimization Hyperparameters

no code implementations20 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.

Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation

no code implementations10 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.

Bayesian Inference Topic Models +1

The Author-Topic Model for Authors and Documents

1 code implementation11 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.

On Smoothing and Inference for Topic Models

1 code implementation9 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.

Topic Models Variational Inference

Continuous-Time Regression Models for Longitudinal Networks

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.

Learning concept graphs from text with stick-breaking priors

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.

Topic Models

Particle-based Variational Inference for Continuous Systems

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.

Variational Inference

Asynchronous Distributed Learning of Topic Models

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.

Topic Models

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