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Title: Leveraging Simple Model Predictions for Enhancing its Performance

Paper content:
There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constrained settings as well as environmental considerations.
In this paper, we propose a novel method SRatio that can utilize information from high performing complex models (viz. deep neural networks, boosted trees, random forests) to reweight a training dataset for a potentially low performing simple model such as a decision tree or a shallow network enhancing its performance.
Our method also leverages the per sample hardness estimate of the simple model which is not the case with the prior works which primarily consider the complex model's confidences/predictions and is thus conceptually novel.
Moreover, we generalize and formalize the concept of attaching probes to intermediate layers of a neural network, which was one of the main ideas in previous work \citep{profweight}, to other commonly used classifiers and incorporate this into our method.
The benefit of these contributions is witnessed in the experiments where on 6 UCI datasets and CIFAR-10 we outperform competitors in a majority (16 out of 27) of the cases and tie for best performance in the remaining cases.
In fact, in a couple of cases, we even approach the complex model's performance.
We also conduct further experiments to validate assertions and intuitively understand why our method works.
Theoretically, we motivate our approach by showing that the weighted loss minimized by simple models using our weighting upper bounds the loss of the complex model.
Simple models such as decision trees or rule lists or shallow neural networks still find use in multiple settings where
a) (global) interpretability is needed,
b) small data sizes are available, or
c) memory/computational constraints are prevalent (Dhurandhar et al., 2018b) .
In such settings compact or understandable models are often preferred over high performing complex models, where the combination of a human with an interpretable model can have better on-field performance than simply using the best performing black box model (Varshney et al., 2018) .
For example, a manufacturing engineer with an interpretable model may be able to obtain precise knowledge of how an out-of-spec product was produced and can potentially go back to fix the process as opposed to having little-to-no knowledge of how the decision was reached.
Posthoc local explainability methods (Ribeiro et al., 2016; Bach et al., 2015; Dhurandhar et al., 2018a) can help delve into the local behavior of black box models, however, besides the explanations being only local, there is no guarantee that they are in fact true (Rudin, 2018) .
There is also a growing concern of the carbon footprint left behind in training complex deep models (Strubell et al., 2019) , which for some popular architectures is more than that left behind by a car over its entire lifetime.
In this paper, we propose a method, SRatio, which reweights the training set to improve simple models given access to a highly accurate complex model such as a deep neural network, boosted trees, or some other predictive model.
Given the applications we are interested in, such as interpretability or deployment of models in resource limited settings, we assume the complexity of the simple models to be predetermined or fixed (viz. decision tree of height ≤ 5).
We cannot grow arbitrary size ensembles such as in boosting or bagging (Freund & Schapire, 1997) .
Our method applies potentially to any complex-simple model combination which is not the case for some state-of-the-art methods in this space such as Knowledge Distillation (Geoffrey Hinton, 2015) or Profweight (Dhurandhar et al., 2018b) , where the complex model is assumed to be a deep neural network.
In addition, we generalize and formalize the concept of probes presented in (Dhurandhar et al., 2018b) and provide examples of what they would correspond to for classifiers other than neural networks.
Our method also uses the a priori low performing simple model's confidences to enhance its performance.
We believe this to be conceptually novel compared to existing methods which seem to only leverage the complex model (viz. its predictions/confidences).
The benefit is seen in experiments where we outperform other competitors in a majority of the cases and are tied with one or more methods for best performance in the remaining cases.
In fact, in a couple of cases we even approach the complex model's performance, i.e. a single tree is made to be as accurate as 100 boosted trees.
Moreover, we motivate our approach by contrasting it with covariate shift and show that our weighting scheme where we now minimize the weighted loss of the simple model is equivalent to minimizing an upper bound on the loss of the complex model.
Our approach and results outline an interesting strategy, where even in cases that one might want a simple model, it might be beneficial to build an accurate complex model first and use it to enhance the desired simple model.
Such is exactly the situation for the manufacturing engineer described in the introduction that has experience with simple interpretable models that provide him with knowledge that a complex model with better performance cannot offer.
Although our method may appear to be simplistic, we believe it to be a conceptual jump.
Our method takes into account the difficulty of a sample not just based on the complex model, but also the simple model which a priori is not obvious and hence possibly ignored by previous methods that may or may not be weighting-based.
Moreover, we have empirically shown that our method either outperforms or matches the best solutions across a wide array of datasets for different complex model (viz. boosted trees, random forests and ResNets) and simple model (viz. single decision trees, linear SVM and small ResNets) combinations.
In fact, in a couple of cases, a single tree approached the performance of a 100 boosted trees using our method.
In addition, we also formalized and generalized the idea behind probes presented in previous work (Dhurandhar et al., 2018b) to classifiers beyond deep neural networks and gave examples of practical instantiations.
In the future, we would like to uncover more such methods and study their theoretical underpinnings.
Table 7 : Probes at various units and their accuracies on the training set 2 for the CIFAR-10 experiment.
This is used in the ProfWeight algorithm to choose the unit above which confidence scores needs to be averaged.
training steps, 0.001 between 60k − 80k training steps and 0.0001 for > 80k training steps.
This is the standard schedule followed in the code by the Tensorflow authors 2 .
We keep the learning rate schedule invariant across all our results.

assistant: text: Method to improve simple models performance given a (accurate) complex model.