It’s officially been a year since my last blog. There have been so many exciting new things going on that it’s been hard to take time out for some nice big picture blog posts. Here are a few areas that I have the best of intentions for getting to.
- Fair representation learning using information theory (2018 NIPS paper)
- Ryan Gallagher’s nice work on anchored topic models.
- Finding coordinated activity in collective sensing games and other spatiotemporal data using structured latent factor discovery. (This is mostly for showing off cool movies.)
- Echo noise. This is a new approach for bounding the information capacity in learning, and allows us to do many cool things like information bottleneck, VAEs, and sparse regression.
- A blessing of dimensionality and how we can use it for better causal inference.
- I’ve still never blogged about our work on neuroimaging or Alzheimer’s disease
- Sahil Garg’s paper for AAAI 2019 on biomedical relation extraction
- Neal Lawton’s work at UAI 2018 and ongoing mission to improve inference in hidden variable models
- Automating data science! (preview here)
Source: Apparent Horizons