#machine_learning

To Read:

• Explainable AI for Trees: From Local Explanations to Global Understanding

src: (Tommasi et al. ⊕2015Tommasi, Tatiana, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars. 2015. “A Deeper Look at Dataset Bias.” arXiv.org, May. http://arxiv.org/abs/1505.01257v1.)

Machine learning fundamentally operates by finding patterns in datasets.11 I always knew that datasets were biased (especially given the whole fairness problem), and this leads to various problems, but I didn’t realize this was a whole field of study. Fascinating. As such, the particulars of the dataset that you train on will affect what possible models can be learned.

Focusing on visual data for the moment, it is clear that, even while we are in the era of big data, most datasets cannot possibily capture every possible facet of visual information,22 which ties into the problem of self-driving cars whereby your dataset can’t possibly have every single possible circumstance, and thus it is these way off in the tail situations that cause the most headache, much like what people like Taleb always talk about. and so someone has to contend with the ways in which there are blind spots or biases as a result of the particular curation of data.

Causes:

• capture bias: thigs like POV, lighting condition, actual device used;
• label bias: this is mainly a problem with supervised learning, as you often have explicit labels (maybe with a bounding box), and there is no notion of ambiguity or subtlety in the labels;
• negative bias: which again is about supervised learning and simply noting all other possible categories not in the dataset.

So now we know that we have all these problems with the coverage of the dataset. Ultimately though, the actual thing we care about is generalization performance, or basically how well this does out-of-sample. After all, even if your model has all these issues, if it is intelligent enough to do well on out-of-sample things, then its sort of a moot point.

Key terms:

• domain shift: general term to describe the fact that the training data probability model \((X,Y)\) changes from train to test. In particular, it encompasses changes in \(P(Y \given X)\), \(P(X)\) or even \(P(Y,X)\).
• covariate shift: when the distribution of the covariates change
  • theory: source (\(S\)) and target (\(T\)) operate in the same space (\(X\) input, \(Y\) output). conditional distributions are the same: \(P_S(Y \given X) = P_T(Y \given X)\) (which essentially says that the model is intact); but marginal distribution has changed: \(P_S(X) = P_T(X)\).
• sample selection bias: same thing as covariate shift?? in my mind sample selection bias is more specific case of covariate shift, that happens as a result of improper selection, whereas covariate shift can be something natural.
• class imbalance: specific to classification, when you have poorly represented classes (so here we’re talking about the \(Y\)’s)

• references:
  • lil-log
  • distill
  • ebook-chapter on transformers (actually this ebook isn’t great, as it ends up being more about the implementation)
• it turns out that #attention is an earlier concept, which itself is motivated by the encoder-decoder sequence-to-sequence architecture
  • 
  • (oops) I didn’t really understand this diagram:
    • sequence to sequence models are essentially the third diagram, where basically the input sequence and output sequence are asynchronous
    • versus the normal rnn, which takes the output as the input
    • I really like the example of 1-to-many, image-to-caption, so your input is not a sequence, but your output is
  • seq2seq example is the translation program, where importantly the input and output sequence don’t have to be the same length (due to the way languages differ in their realizations of the same meaning)
  • the encoder is the rnn on the input sequence, and this culminates into the last hidden layer
    • this is essentially the context vector: the idea here is that this (fixed-length) vector captures all the information about the sentence
      • key: this acts like a sort of informational bottleneck, and actually is the impetus for the attention mechanic
  • key point (via this tutorial):
    • instead of having everything represented as the last hidden layer (fixed-length), why not just look at all the hidden layers (vectors representing each word)
    • but, that would be variable length, so instead just look at a linear combination of those hidden layers. this linear combination is learned, and is basically attention
• Transformers #todo
  • “prior art”
    • CNN: easy to parallelize, but aren’t recurrent (can’t capture sequential dependency)
    • RNN: reverse
  • goal of transformers/attention is achieve parallelization and recurrence
    • by appealing to “attention” to get the recurrence (?)

src: (Lipton and Steinhardt ⊕2019Lipton, Zachary, and Jacob Steinhardt. 2019. “Troubling Trends in Machine Learning Scholarship.” Queue, February.)

Let’s record the kinds of experimental data that we are interested in for [[project-interpolation]].

1. Replicating what’s been shown in the literature

• In the Zhang et al. (⊕2019Zhang, Chiyuan, Benjamin Recht, Samy Bengio, Moritz Hardt, and Oriol Vinyals. 2019. “Understanding deep learning requires rethinking generalization.” In 5th International Conference on Learning Representations, Iclr 2017 - Conference Track Proceedings. University of California, Berkeley, Berkeley, United States.) paper, they show that if you randomize the training data labels (in a classification problem), then you can still get zero training loss.
  • the point here is that the classical ideas of understanding the performance of machine learning methods (VC dimension) break down11 Though, I always get confused about this. Probably should reread one of the blog posts of Aroras..
• In some more recent work (Belkin et al. ⊕2019Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019. “Reconciling modern machine-learning practice and the classical biasvariance trade-off.” Proceedings of the National Academy of Sciences 116 (32): 15849–54.), it was cited that if you randomize some of the training labels, you can basically still get the same generalization performance.
  • unclear to me if these two results are the same.
  • I vaguely remember there was some place that talked about the relevance to #robustness or #adversarial_training.
• establish that for standard problems (like CIFAR/MNIST/*), if you perturb the data (some percentage, or be clever about picking the points), then you’re going to maintain test performance.

2. New Ideas

• We conjecture that what’s going on is that there
  • show that this