I want to talk about technical approaches to mitigating algorithmic bias. It’s 2019, and the majority of the ML community is finally publicly acknowledging the prevalence and consequences of bias in ML models. For years, dozens of reports by organizations such as ProPublica and the New York Times have been exposing the scale of algorithmic discrimination in criminal risk … [Read more...] about Algorithmic Solutions to Algorithmic Bias: A Technical Guide
Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning
One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn’t want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. Throwing a neural net at an RL problem will probably blow something up in front of your face — and it will blow up in a different way … [Read more...] about Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning
Topic Modeling with LSA, PLSA, LDA & lda2Vec
In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called … [Read more...] about Topic Modeling with LSA, PLSA, LDA & lda2Vec