Timezone: »
Complex machine learning models, such as deep neural networks, have recently achieved great predictive successes for visual object recognition, speech perception, language modelling, and information retrieval. These predictive successes are enabled by automatically learning expressive features from the data. Typically, these learned features are a priori unknown, difficult to engineer by hand, and hard to interpret. This workshop is about interpreting the structure and predictions of these complex models.
Interpreting the learned features and the outputs of complex systems allows us to more fundamentally understand our data and predictions, and to build more effective models. For example, we may build a complex model to predict long range crime activity. But by interpreting the learned structure of the model, we can gain new insights into the processing driving crime events, enabling us to develop more effective public policy. Moreover, if we learn, for example, that the model is making good predictions by discovering how the geometry of clusters of crime events affect future activity, we can use this knowledge to design even more successful predictive models.
This 1 day workshop is focused on interpretable methods for machine learning, with an emphasis on the ability to learn structure which provides new fundamental insights into the data, in addition to accurate predictions. We will consider a wide range of topics, including deep learning, kernel methods, tensor methods, generalized additive models, rule based models, symbolic regression, visual analytics, and causality. A poster session, coffee breaks, and a panel guided discussion will encourage interaction between attendees. We wish to carefully review and enumerate modern approaches to the challenges of interpretability, share insights into the underlying properties of popular machine learning algorithms, and discuss future directions.
Thu 11:45 p.m.  12:00 a.m.

Opening Remarks


Fri 12:00 a.m.  12:30 a.m.

Honglak Lee
(Invited Talk)


Fri 12:30 a.m.  1:00 a.m.

Why Interpretability: A Taxonomy of Interpretability and Implications for Principled Evaluation (Finale DoshiVelez)
(Invited Talk)
»
With a growing interest in interpretability, there is an increasing need to characterize what exactly we mean by it and how to sensibly compare the interpretability of different approaches. In this talk, I suggest that our current desire for "interpretability" is as vague as asking for "good predictions"  a desire that. while entirely reasonable, must be formalized into concrete needs such as high average test performance (perhaps heldout likelihood is a good metric) or some kind of robust performance (perhaps sensitivity or specificity are more appropriate metrics). This objective of this talk is to start a conversation to do the same for interpretability: I will describe distinct, concrete objectives that all fall under the umbrella term of interpretability and how each objective suggests natural evaluation procedures. I will also describe highlight important open questions in the evaluation of interpretable models. Joint work with Been Kim, and the product of discussions with countless collaborators and colleagues. 

Fri 1:00 a.m.  1:30 a.m.

Best paper award talks
(Contributed Talk)
»
Title: An unexpected unity among methods for interpreting model predictions Scott Lundberg and SuIn Lee Title: Feature Importance Measure for Nonlinear Learning Algorithms Marina M.C. Vidovic, Nico Görnitz, KlausRobert Müller, and Marius Kloft 

Fri 2:00 a.m.  2:30 a.m.

Intelligible Machine Learning for HealthCare (Rich Caruana)
(Invited Talk)
»
In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible (e.g., deep neural nets, boosted trees, and random forests), and the most intelligible models usually are less accurate (e.g., linear/logistic regression). This tradeoff often limits the accuracy of models that can be applied in missioncritical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a learned model is important. We have developed a learning method based on generalized additive models (GAMs) that is often as accurate as full complexity models, but remains as intelligible as linear/logistic regression models. In the talk I’ll present two case studies where these highperformance generalized additive models (GA2Ms) are applied to healthcare problems yielding intelligible models with stateoftheart accuracy. In the pneumonia risk prediction case study, the intelligible model uncovers surprising patterns in the data that previously prevented complex learned models from being deployed, but because it is intelligible and modular allows these patterns to easily be recognized and removed. In the 30day hospital readmission case study, we show that the same methods scale to large datasets containing hundreds of thousands of patients and thousands of attributes while remaining intelligible and providing accuracy comparable to the best (unintelligible) machine learning methods. 

Fri 5:30 a.m.  6:00 a.m.

Maya Gupta
(Invited Talk)


Fri 5:30 a.m.  6:00 a.m.

The Power of Monotonicity for Practical Machine Learning (Maya Gupta)
(Invited Talk)
»
What prior knowledge do humans have about machine learning problems that we can take advantage of as regularizers? One common intuition is that certain inputs should have a positive (only) effect on the output, for example, the price of a house should only increase as its size goes up, if all else is the same. Incorporating such monotonic priors into our machine learning algorithms can dramatically increase their interpretability and debuggability. We'll discuss stateoftheart algorithms to learn flexible monotonic functions, and share some stories about why monotonicity is such an important regularizer for practical problems where train and test samples are not IID, especially when learning from clicks. 

Fri 6:30 a.m.  7:00 a.m.

Finding interpretable sparse structure in fMRI data with dependent relevance determination priors (Jonathan Pillow)
(Invited Talk)
»
In many problem settings, parameters are not merely sparse, but dependent in such a way that nonzero coefficients tend to cluster together. We refer to this form of dependency as region sparsity". Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), which models parameters as independent a priori, and therefore do not exploit such dependencies. Here we introduce a hierarchical model for smooth, regionsparse weight vectors and tensors in a linear regression setting. Our approach represents a hierarchical extension of the relevance determination framework, where we add a transformed Gaussian process to model the dependencies between the prior variances of regression weights. We combine this with a structured model of the prior variances of Fourier coefficients, which eliminates unnecessary high frequencies. The resulting prior encourages weights to be regionsparse in two different bases simultaneously. We develop Laplace approximation and Monte Carlo Markov Chain (MCMC) sampling to provide efficient inference for the posterior, and show substantial improvements over existing methods for both simulated and real fMRI datasets. 

Fri 7:00 a.m.  7:30 a.m.

Poster session
(Posters)


Fri 7:30 a.m.  8:00 a.m.

Better Machine Learning Through Data (Saleema Amershi)
(Invited Talk)
»
Machine learning is the product of both an algorithm and data. While machine learning research tends to focus on algorithmic advances, taking the data as given, machine learning practice is quite the opposite. Most of the influence practitioners have in using machine learning to build predictive models comes through interacting with data, including crafting the data used for training and examining results on new data to inform future iterations. In this talk, I will present tools and techniques we have been developing in the Machine Teaching Group at Microsoft Research to support the model building process. I will then discuss some of the open challenges and opportunities in improving the practice of machine learning. 

Fri 8:00 a.m.  9:00 a.m.

Future Directions in Interpretable Machine Learning
(Panel Discussion)

Author Information
Andrew Wilson (Cornell University)
I am a professor of machine learning at New York University.
Been Kim (Google Brain)
William Herlands (Carnegie Mellon University)
More from the Same Authors

2021 Workshop: Bayesian Deep Learning »
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling 
2020 Poster: Bayesian Deep Learning and a Probabilistic Perspective of Generalization »
Andrew Wilson · Pavel Izmailov 
2020 Poster: Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints »
Marc Finzi · Ke Alexander Wang · Andrew Wilson 
2020 Spotlight: Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints »
Marc Finzi · Ke Alexander Wang · Andrew Wilson 
2020 Poster: BoTorch: A Framework for Efficient MonteCarlo Bayesian Optimization »
Maximilian Balandat · Brian Karrer · Daniel Jiang · Samuel Daulton · Ben Letham · Andrew Wilson · Eytan Bakshy 
2020 Poster: Learning Invariances in Neural Networks from Training Data »
Gregory Benton · Marc Finzi · Pavel Izmailov · Andrew Wilson 
2020 Poster: Improving GAN Training with Probability Ratio Clipping and Sample Reweighting »
Yue Wu · Pan Zhou · Andrew Wilson · Eric Xing · Zhiting Hu 
2020 Poster: Why Normalizing Flows Fail to Detect OutofDistribution Data »
Polina Kirichenko · Pavel Izmailov · Andrew Wilson 
2019 Workshop: Learning with Rich Experience: Integration of Learning Paradigms »
Zhiting Hu · Andrew Wilson · Chelsea Finn · Lisa Lee · Taylor BergKirkpatrick · Ruslan Salakhutdinov · Eric Xing 
2019 Poster: Exact Gaussian Processes on a Million Data Points »
Ke Alexander Wang · Geoff Pleiss · Jacob Gardner · Stephen Tyree · Kilian Weinberger · Andrew Gordon Wilson 
2019 Poster: FunctionSpace Distributions over Kernels »
Gregory Benton · Wesley J Maddox · Jayson Salkey · Julio Albinati · Andrew Gordon Wilson 
2019 Poster: A Simple Baseline for Bayesian Uncertainty in Deep Learning »
Wesley J Maddox · Pavel Izmailov · Timur Garipov · Dmitry Vetrov · Andrew Gordon Wilson 
2018 Workshop: Machine Learning for the Developing World (ML4D): Achieving sustainable impact »
William Herlands · Maria DeArteaga · Amanda Coston 
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel HernándezLobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling 
2018 Poster: Scaling Gaussian Process Regression with Derivatives »
David Eriksson · Kun Dong · Eric Lee · David Bindel · Andrew Wilson 
2018 Poster: GPyTorch: Blackbox MatrixMatrix Gaussian Process Inference with GPU Acceleration »
Jacob Gardner · Geoff Pleiss · Kilian Weinberger · David Bindel · Andrew Wilson 
2018 Spotlight: GPyTorch: Blackbox MatrixMatrix Gaussian Process Inference with GPU Acceleration »
Jacob Gardner · Geoff Pleiss · Kilian Weinberger · David Bindel · Andrew Wilson 
2018 Poster: Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs »
Timur Garipov · Pavel Izmailov · Dmitrii Podoprikhin · Dmitry Vetrov · Andrew Wilson 
2018 Spotlight: Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs »
Timur Garipov · Pavel Izmailov · Dmitrii Podoprikhin · Dmitry Vetrov · Andrew Wilson 
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel HernándezLobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling 
2017 Workshop: Machine Learning for the Developing World »
William Herlands · Maria DeArteaga 
2017 Symposium: Interpretable Machine Learning »
Andrew Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands 
2017 Poster: Bayesian GAN »
Yunus Saatci · Andrew Wilson 
2017 Spotlight: Bayesian GANs »
Yunus Saatci · Andrew Wilson 
2017 Poster: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier 
2017 Poster: Scalable Log Determinants for Gaussian Process Kernel Learning »
Kun Dong · David Eriksson · Hannes Nickisch · David Bindel · Andrew Wilson 
2017 Oral: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier 
2017 Poster: Scalable Levy Process Priors for Spectral Kernel Learning »
Phillip Jang · Andrew Loeb · Matthew Davidow · Andrew Wilson 
2016 Oral: Examples are not enough, learn to criticize! Criticism for Interpretability »
Been Kim · Sanmi Koyejo · Rajiv Khanna 
2016 Poster: Examples are not enough, learn to criticize! Criticism for Interpretability »
Been Kim · Sanmi Koyejo · Rajiv Khanna 
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing 
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing 
2015 Poster: Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction »
Been Kim · Julie A Shah · Finale DoshiVelez 
2015 Poster: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing 
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing 
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan L Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David LopezPaz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio 
2014 Poster: Fast Kernel Learning for Multidimensional Pattern Extrapolation »
Andrew Wilson · Elad Gilboa · John P Cunningham · Arye Nehorai 
2014 Poster: The Bayesian Case Model: A Generative Approach for CaseBased Reasoning and Prototype Classification »
Been Kim · Cynthia Rudin · Julie A Shah 
2010 Spotlight: Copula Processes »
Andrew Wilson · Zoubin Ghahramani 
2010 Poster: Copula Processes »
Andrew Wilson · Zoubin Ghahramani