Unreproducible Research is Reproducible

The apparent contradiction in the title is a wordplay on the different meanings attributed to the word reproducible across different scientific fields. What we imply is that unreproducible findings...

kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection

Model selection is an essential task for many applications in scientific discovery. The most common approaches rely on univariate linear measures of association between each feature and the outcome...

Simple Regression Models

Developing theories of when and why simple predictive models
perform well is a key step in understanding decisions of cognitively
bounded humans and intelligent machines. We are interested in
ho...

Convex envelopes of complexity controlling penalties: the case against premature envelopment

Convex envelopes of the cardinality and rank function, l_1 and nuclear norm, have gained immense popularity due to their sparsity inducing properties. This gave rise to a natural approach to buil...

Synthesizing Programs for Images using Reinforced Adversarial Learning

Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak ind...

Learning Population-Level Diffusions with Generative RNNs

We estimate stochastic processes that govern the dynamics of evolving populations such as cell differentiation. The problem is challenging since longitudinal trajectory measurements of individuals ...

Fast Context Adaptation via Meta-Learning

We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two ...

Topological Data Analysis of Decision Boundaries with Application to Model Selection

We propose the labeled Cech complex, the plain labeled Vietoris-Rips complex, and the locally scaled labeled Vietoris-Rips complex to perform persistent homology inference of decision boundaries in...

Proceedings of Machine Learning Research

Proceedings of the Thirty-Second Conference on Learning Theory
Held in Phoenix, USA on 25-28 June 2019
Published as Volume 99 by the Proceedings of Machine Learning Research on 25 June 2019.
Volume Edited by:
Alina Beygelzimer
Daniel...

Discovering Context Effects from Raw Choice Data

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgeme...

Analogies Explained: Towards Understanding Word Embeddings

Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy “woman is to queen as man is to king”...

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised l...

Locally Private Bayesian Inference for Count Models

We present a general and modular method for privacy-preserving Bayesian inference for Poisson factorization, a broad class of models that includes some of the most widely used models in the social ...

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-w...

Adaptive Neural Trees

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is character...