I want this to be a helpful resource for newcomers to the field of Bayesian machine learning. The objective here is to collect relevant literature that brings insights into modern inference methods. Of course, this requires me to extract insights myself to be sure that the papers I put on are meaningful. Therefore, this post remains a living document.
I will post commentary, when I can, in terms of what to expect when reading the
material. Often, however, I will only put materials in list to be considered
as the recommended reading order. A recommendation for the overall sequence
in which topics should be considered is harder to be prescribed.
however, suggest that this not be your first excursion into machine learning.
I now encourage that this perspective be your first foray into machine learning.
When diving deep into a topic, we often find ourselves too close to the action. It is important to start with and keep the bigger picture in mind. I recommend the following to get a feel for the fundamental thesis around being Bayesian. It is not a silver bullet, but a set of common-sense principles to abide by.
Less so now, but often arguments around the subjectivity of the prior is brought into question. This is unfortunately a misdirected argument because without subjectivity, "learning" cannot happen and is in general an ill-defined problem to tackle. Although, subjective priors is not the only thing that being Bayesian brings to the table.
Many people, including seasonsed researchers, have the wrong idea of what it means to be Bayesian. Putting prior assumptions does not make one a Bayesian. In that sense, everyone is a Bayesian because they build algorithms starting with priors, whether they know it or not. I die a little when people compare Bayesian methods to simply regularlizing with the prior. That is an effect often misconstrued. For instance, take a look at this fun post by Dan Simpson, "The king must die" on why simply assuming a Laplace prior does not imply sparse solutions unlike its popular maximum a-posteriori variant known as the Lasso.
When explaining the data using a model, we usually have many competing hypothesis available, naturally leading to the model selection problem. Occam's razor principle advocates that we must choose the simplest possible explanation. Bayesian inference shines here as well by automatically embodying this "principle of parsimony".
Bayesian model averaging (BMA) is another perk enjoyed by Bayesians, which allows for soft model selection. See Bayesian Model Averaging: A Tutorial for a classic reference. Andrew G. Wilson clarifies the value it adds in a technical report titled The Case for Bayesian Deep Learning. Unfortunately, BMA is often misconstrued as model combination. Minka dispells any misunderstandings in this regard, in his technical note Bayesian model averaging is not model combination.
The Frequentist-vs-Bayesian debate has unfortunately occupied more minds than it should have. Any new entrant to the field will undoubtably still come across this debate and be forced to take a stand (make sure you don't fall for the trap). Christian Robert's answer on Cross Validated is the best technical introduction to start with. Then, I highly recommend this talk by a dominant figure in the field, Michael Jordan, titled Bayesian or Frequentist, Which Are You? (Part I, Part II). Having read and listened to all this, one should keep this excellent exposition by Robert E. Kass Statistical Inference: The Big Picture on their reading list always. Everytime someone starts this debate again, ask them to read this first.
Gelman and Yao describe Holes in Bayesian Statistics which may be a worthwhile reader at a later stage.
On a concluding note, I would refrain from labelling anyone or any algorithm as an exclusive Bayesian. In one is still hell-bent on being labeled, remember keeping an open mind is the hallmark of a true Bayesian.
References so that one doesn't have to always remember those tricky identities but come up commonly.
Gaussian Process (GP) research interestingly started as a consequence of the popularity and early success of neural networks.
The non-parametric nature is slightly at odds with scalability of Gaussian Processes, but we've made some considerable progress through first principles in this regard as well.
Covariance functions are the way we describe our inductive biases in a Gaussian Process model and hence deserve a separate section altogether.
Monte Carlo algorithms are used for exact inference in scenarios when closed-form inference is not possible.
The simple Monte Carlo algorithms rely on independent samples from a target distribution to be useful. Relaxing the independence assumption leads to correlated samples via Markov Chain Monte Carlo (MCMC) family of algorithms.
The following readings are only worth after one has played more closely with MCMC algorithms.
PRML Chapter 10 1 shows the zero-forcing behavior of the KL term involved in variational inference, as a result underestimating the uncertainty when unimodal approximations are used for multimodal true distributions. This, however, should not be considered a law of the universe, but only a thumb rule as clarified by Turner et. al. Counterexamples to variational free energy compactness folk theorems. Rainforth et. al show that tighter variational bounds are not necessarily better.
Cutting-edge research is a good way to sense where the field is headed. Here are a few venues that I occassionally sift through.
Think Bayes by Allen B. Downey is an excellent book for beginners.
MacKay, D. (2004). Information Theory, Inference, and Learning Algorithms. IEEE Transactions on Information Theory, 50, 2544-2545. ↩
MacKay, D. (1998). Introduction to Gaussian processes. ↩
Schölkopf, B., & Smola, A. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Journal of the American Statistical Association, 98, 489-489. ↩