Themes • Fairness & Bias • More than just a consequence of sampling • Interpretability & Explainability • Don’t just take my word for it • Causality • “no unobserved confounders?”, “Not bloody likely.” • Bayesian/Probabilistic • Uncertainty matters, but it’s hard to compute • Adversarial Examples • Foolproof AI is hard • Learning from very few examples • K-shot learning but GANs are still the hotness
On Fairness and Calibration http://papers.nips.cc/paper/7151-on-fairness-and-calibration.pdf Calibration is compatible only with a single error constraint. i.e. You can ensure equal false- negatives rates across groups, or equal false-positive rates across groups, but not both simultaneously.
Explainability & Interpretability • Debate over what it even means for a model to be interpretable. • Some novel methods for explaining why models do what they do • Three main flavours: 1. What concepts does the model know about? 2. What is the model ‘looking’ at when making a prediction? 3. Prove that this model won’t do something we don’t want (secretly discriminate, for example).
https://arxiv.org/pdf/1711.08037.pdf “We should determine what the various stakeholders demanding interpretability want, and which of these desiderata can actually be satisfied within the current learning paradigm. To do any of this effectively, we must invite the stakeholders to participate in the conversation.”
Causality • Judea Pearl!! (literally wrote the book on causal inference) • Clever methods for inferring causal structure from observational data (eg not experiments) • Performing counterfactual reasoning with ML models • What would have happened if a different action had been taken?
Counterfactual Reasoning • Medical time-series application by Schulman & Saria • A causal GP model that enables treatment decision support https://papers.nips.cc/paper/6767-counterfactual-gaussian-processes-for-reliable-decision-making-and-what-if-reasoning.pdf
Bayesian/Probabilistic • Need to quantify uncertainty, but typically computationally difficult. https://bayesianbiologist.com/2012/10/18/introduction-to-bayesian-methods-guest-lecture/
Variational Inference • Analytic approximations to posteriors • Current approaches are model specific • Capturing the covariance properly and generally is hard
Variational Inference • Analytic approximations to posteriors • Current approaches are model specific • Capturing the covariance properly and generally is hard
Adversarial Examples Can we attain some provable level of robustness against attack? Important for mission critical applications (self driving cars, medicine)
Learning from very few examples • Sample Generation • It’s all about dem GANs • K-Shot Learning • Transfer knowledge to subdomains • Both RL and supervised examples
Other Interesting Things • Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database. https://arxiv.org/abs/1703.07771 https://github.com/YerevaNN/mimic3-benchmarks • Fei-Fie Li’s group is moving into the healthcare domain. Focusing now on operations using privacy preserving depth sensing computer vision. • Detecting hand washing • Mobility in ICU • Meta-Learning http://papers.nips.cc/paper/7266-a-meta-learning- perspective-on-cold-start-recommendations-for-items.pdf
Other Interesting Things • More hospital DS teams are coming online. Notable example: MGH, BWH, Harvard: https://clindatsci.com/ (~6months old) • Jeff Dean says ML all the things! Including the things that do the ML (http://learningsys.org/nips17/assets/slides/dean-nips17.pdf) • Lols
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