Chandan Singh | interpretability

interpretability

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some interesting papers on interpretable machine learning, largely organized based on this interpretable ml review (murdoch et al. 2019) and notes from this interpretable ml book (molnar 2019)

reviews

definitions

The definition of interpretability I find most useful is that given in murdoch et al. 2019: basically that interpretability requires a pragmatic approach in order to be useful. As such, interpretability is only defined with respect to a specific audience + problem and an interpretation should be evaluated in terms of how well it benefits a specific context. It has been defined and studied more broadly in a variety of works:

overviews

  • Towards a Generic Framework for Black-box Explanation Methods (henin & metayer 2019)
    • sampling - selection of inputs to submit to the system to be explained
    • generation - analysis of links between selected inputs and corresponding outputs to generate explanations
      1. proxy - approximates model (ex. rule list, linear model)
      2. explanation generation - explains the proxy (ex. just give most important 2 features in rule list proxy, ex. LIME gives coefficients of linear model, Shap: sums of elements)
    • interaction (with the user)
    • this is a super useful way to think about explanations (especially local), but doesn’t work for SHAP / CD which are more about how much a variable contributes rather than a local approximation

evaluating interpretability

Evaluating interpretability can be very difficult (largely because it rarely makes sense to talk about interpretability outside of a specific context). The best possible evaluation of interpretability requires benchmarking it with respect to the relevant audience in a context. For example, if an interpretation claims to help understand radiology models, it should be tested based on how well it helps radiologists when actually making diagnoses. The papers here try to find more generic alternative ways to evaluate interp methods (or just define desiderata to do so).

criticisms

intrinsic interpretability (i.e. how can we fit simpler models)

For an implementation of many of these models, see the python imodels package.

decision rules

For more on rules, see logic notes.

  • 2 basic concepts for a rule
    • converage = support
    • accuracy = confidence = consistency
      • measures for rules: precision, info gain, correlation, m-estimate, Laplace estimate
  • these algorithms usually don’t support regression, but you can get regression by cutting the outcome into intervals

rule sets

Rule sets commonly look like a series of independent if-then rules. Unlike trees / lists, these rules can be overlapping and might not cover the whole space. Final predictions can be made via majority vote, using most accurate rule, or averaging predictions.

rule lists

  • oneR algorithm - select feature that carries most information about the outcome and then split multiple times on that feature
  • sequential covering - keep trying to cover more points sequentially
  • pre-mining frequent patterns (want them to apply to a large amount of data and not have too many conditions)
    • FP-Growth algorithm (borgelt 2005) is fast
    • Aprior + Eclat do the same thing, but with different speeds
  • interpretable classifiers using rules and bayesian analysis (letham et al. 2015)
    • start by pre-mining frequent patterns rules
      • current approach does not allow for negation (e.g. not diabetes) and must split continuous variables into categorical somehow (e.g. quartiles)
      • mines things that frequently occur together, but doesn’t look at outcomes in this step - okay (since this is all about finding rules with high support)
    • learn rules w/ prior for short rule conditions and short lists
      • start w/ random list
      • sample new lists by adding/removing/moving a rule
      • at the end, return the list that had the highest probability
    • scalable bayesian rule lists (yang et al. 2017) - faster algorithm for computing
  • learning certifiably optimal rules lists (angelino et al. 2017) - optimization for categorical feature space
    • can get upper / lower bounds for loss = risk + $\lambda$ * listLength
  • Expert-augmented machine learning (gennatas et al. 2019)
    • make rule lists, then compare the outcomes for each rule with what clinicians think should be outcome for each rule
    • look at rules with biggest disagreement and engineer/improve rules or penalize unreliable rules

trees

Trees suffer from the fact that they have to cover the entire decision space and often we end up with replicated subtrees.

  • optimal classification trees methodology paper (bertsimas & dunn 2017) - globally optimal decision tree with expensive optimization
  • Building more accurate decision trees with the additive tree (luna et al. 2019)
    • present additive tree (AddTree), which builds a single decision tree, which is between a single CART tree and boosted decision stumps
    • cart can be seen as a boosting algorithm on stumps
      • can rewrite boosted stumps as a tree very easily
      • previous work: can grow tree based on Adaboost idea = AdaTree
    • Screen Shot 2020-03-11 at 11.10.13 PM
  • optimal sparse decision trees (hu et al. 2019) - optimal decision trees for binary variables
  • extremely randomized trees - randomness goes further, not only feature is selected randomly but also split has some randomness
  • issues: replicated subtree problem (Pagallo & Haussler, 1990)
  • Bayesian Treed Models (chipman et al. 2001) - impose priors on tree parameters
    • tree structure e.g. depth, splitting criteria
    • values in terminal nodes coditioned on tree structure
    • residual noise’s standard deviation

linear (+algebraic) models

supersparse models

gams (generalized additive models)

symbolic regression

example-based (e.g. prototypes)

  • prototypes II (chen et al. 2018)
    • can have prototypes smaller than original input size
    • l2 distance
    • require the filters to be identical to the latent representation of some training image patch
    • cluster image patches of a particular class around the prototypes of the same class, while separating image patches of different classes
    • maxpool class prototypes so spatial size doesn’t matter
    • also get heatmap of where prototype was activated (only max really matters)
    • train in 3 steps
      • train everything: classification + clustering around intraclass prototypes + separation between interclass prototypes (last layer fixed to 1s / -0.5s)
      • project prototypes to data patches
      • learn last layer
  • prototypes I (li et al. 2017)
    • uses encoder/decoder setup
    • encourage every prototype to be similar to at least one encoded input
    • learned prototypes in fact look like digits
    • correct class prototypes go to correct classes
    • loss: classification + reconstruction + distance to a training point

bayesian models

  • e.g. naive bayes

interpretable neural nets

misc models

programs

  • program synthesis - automatically find a program in an underlying programming language that satisfies some user intent
    • ex. program induction - given a dataset consisting of input/output pairs, generate a (simple?) program that produces the same pairs
  • probabilistic programming - specify graphical models via a programming language

posthoc interpretability (i.e. how can we interpret a fitted model)

model-agnostic

  • local surrogate (LIME) - fit a simple model locally to on point and interpret that
    • select data perturbations and get new predictions
      • for tabular data, this is just varying the values around the prediction
      • for images, this is turning superpixels on/off
      • superpixels determined in unsupervised way
    • weight the new samples based on their proximity
    • train a kernel-weighted, interpretable model on these points
    • LEMNA - like lime but uses lasso + small changes
  • anchors (ribeiro et al. 2018) - find biggest square region of input space that contains input and preserves same output (with high precision)
    1. does this search via iterative rules
  • What made you do this? Understanding black-box decisions with sufficient input subsets
    • want to find smallest subsets of features which can produce the prediction
      • other features are masked or imputed
  • VIN (hooker 04) - variable interaction networks - globel explanation based on detecting additive structure in a black-box, based on ANOVA
  • local-gradient (bahrens et al. 2010) - direction of highest slope towards a particular class / other class
  • golden eye (henelius et al. 2014) - randomize different groups of features and search for groups which interact
  • shapley value - average marginal contribution of a feature value across all possible sets of feature values
    • “how much does prediction change on average when this feature is added?”
    • tells us the difference between the actual prediction and the average prediction
    • estimating: all possible sets of feature values have to be evaluated with and without the j-th feature
      • this includes sets of different sizes
      • to evaluate, take expectation over all the other variables, fixing this variables value
    • shapley sampling value - sample instead of exactly computing
      • quantitative input influence is similar to this…
    • satisfies 3 properties
      • local accuracy - basically, explanation scores sum to original prediction
      • missingness - features with $x’_i=0$ have 0 impact
      • consistency - if a model changes so that some simplified input’s contribution increases or stays the same regardless of the other inputs, that input’s attribution should not decrease.
    • interpretation: Given the current set of feature values, the contribution of a feature value to the difference between the actual prediction and the mean prediction is the estimated Shapley value
    • recalculate via sampling other features in expectation
    • followup propagating shapley values (chen, lundberg, & lee 2019) - can work with stacks of different models
  • probes - check if a representation (e.g. BERT embeddings) learned a certain property (e.g. POS tagging) by seeing if we can predict this property (maybe linearly) directly from the representation
    • problem: if the post-hoc probe is a complex model (e.g. MLP), it can accurately predict a property even if that property isn’t really contained in the representation
    • potential solution: benchmark against control tasks, where we construct a new random task to predict given a representation, and see how well the post-hoc probe can do on that task
  • Explaining individual predictions when features are dependent: More accurate approximations to Shapley values (aas et al. 2019) - tries to more accurately compute conditional expectation
  • Feature relevance quantification in explainable AI: A causal problem (janzing et al. 2019) - argues we should just use unconditional expectation
  • quantitative input influence - similar to shap but more general
  • permutation importance - increase in the prediction error after we permuted the feature’s values
    • $\mathbb E[Y] - \mathbb E[Y\vert X_{\sim i}]$
    • If features are correlated, the permutation feature importance can be biased by unrealistic data instances (PDP problem)
    • not the same as model variance
    • Adding a correlated feature can decrease the importance of the associated feature
  • L2X: information-theoretical local approximation (chen et al. 2018) - locally assign feature importance based on mutual information with function
  • Learning Explainable Models Using Attribution Priors + Expected Gradients - like doing integrated gradients in many directions (e.g. by using other points in the training batch as the baseline)
    • can use this prior to help improve performance
  • Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
  • All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (Aaron, Rudin, & Dominici 2018)
  • Interpreting Black Box Models via Hypothesis Testing

feature interactions

How interactions are defined and summarized is a very difficult thing to specify. For example, interactions can change based on monotonic transformations of features (e.g. $y= a \cdot b$, $\log y = \log a + \log b$). Nevertheless, when one has a specific question it can make sense to pursue finding and understanding interactions.

  • build-up = context-free, less faithful: score is contribution of only variable of interest ignoring other variables
  • break-down = occlusion = context-dependent, more faithful: score is contribution of variable of interest given all other variables (e.g. permutation test - randomize var of interest from right distr.)
  • H-statistic: 0 for no interaction, 1 for complete interaction
    • how much of the variance of the output of the joint partial dependence is explained by the interaction instead of the individuals
    • $H^2{jk} = \underbrace{\sum_i [\overbrace{PD(x_j^{(i)}, x_k^{(i)})}^{\text{interaction}} \overbrace{- PD(x_j^{(i)}) - PD(x_k^{(i)})}^{\text{individual}}]^2}{\text{sum over data points}} : / : \underbrace{\sum_i [PD(x_j^{(i)}, x_k^{(i)})}_{\text{normalization}}]^2$
    • alternatively, using ANOVA decomp: $H_{jk}^2 = \sum_i g_{ij}^2 / \sum_i (\mathbb E [Y \vert X_i, X_j])^2$
    • same assumptions as PDP: features need to be independent
  • alternatives
    • variable interaction networks (Hooker, 2004) - decompose pred into main effects + feature interactions
    • PDP-based feature interaction (greenwell et al. 2018)
  • feature-screening (feng ruan’s work)
    • want to find beta which is positive when a variable is important
    • idea: maximize difference between (distances for interclass) and (distances for intraclass)
    • using an L1 distance yields better gradients than an L2 distance
  • methods for finding frequent item sets

vim (variable importance measure) framework

  • VIM
    1. a quantitative indicator that quantifies the change of model output value w.r.t. the change or permutation of one or a set of input variables
    2. an indicator that quantifies the contribution of the uncertainties of one or a set of input variables to the uncertainty of model output variable
    3. an indicator that quantifies the strength of dependence between the model output variable and one or a set of input variables.
  • difference-based - deriv=based methods, local importance measure, morris’ screening method
    • LIM (local importance measure) - like LIME
      • can normalize weights by values of x, y, or ratios of their standard deviations
      • can also decompose variance to get the covariances between different variables
      • can approximate derivative via adjoint method or smth else
    • morris’ screening method
      • take a grid of local derivs and look at the mean / std of these derivs
      • can’t distinguish between nonlinearity / interaction
    • using the squared derivative allows for a close connection w/ sobol’s total effect index
      • can extend this to taking derivs wrt different combinations of variables
  • parametric regression
    • correlation coefficient, linear reg coeffeicients
    • partial correlation coefficient (PCC) - wipe out correlations due to other variables
      • do a linear regression using the other variables (on both X and Y) and then look only at the residuals
    • rank regression coefficient - better at capturing nonlinearity
    • could also do polynomial regression
    • more techniques (e.g. relative importance analysis RIA)
      • nonparametric regression
        • use something like LOESS, GAM, projection pursuit
        • rank variables by doing greedy search (add one var at a time) and seeing which explains the most variance
  • hypothesis test
    • grid-based hypothesis tests: splitting the sample space (X, Y) into grids and then testing whether the patterns of sample distributions across different grid cells are random
      • ex. see if means vary
      • ex. look at entropy reduction
    • other hypothesis tests include the squared rank difference, 2D kolmogorov-smirnov test, and distance-based tests
  • variance-based vim (sobol’s indices)
    • ANOVA decomposition - decompose model into conditional expectations $Y = g_0 + \sum_i g_i (X_i) + \sum_i \sum_{j > i} g_{ij} (X_i, X_j) + \dots + g_{1,2,…, p}$
      • $g_0 = \mathbf E (Y)\ g_i = \mathbf E(Y \vert X_i) - g_0 \ g_{ij} = \mathbf E (Y \vert X_i, X_j) - g_i - g_j - g_0\…$
      • take variances of these terms
      • if there are correlations between variables some of these terms can misbehave
      • note: $V(Y) = \sum_i V (g_i) + \sum_i \sum_{j > i} V(g_{ij}) + … V(g_{1,2,…,p})$ - variances are orthogonal and all sum to total variance
      • anova decomposition basics - factor function into means, first-order terms, and interaction terms
    • $S_i$: Sobol’s main effect index: $=V(g_i)=V(E(Y \vert X_i))=V(Y)-E(V(Y \vert X_i))$
      • small value indicates $X_i$ is non-influential
      • usually used to select important variables
    • $S_{Ti}$: Sobol’s total effect index - include all terms (even interactions) involving a variable
    • equivalently, $V(Y) - V(E[Y \vert X_{\sim i}])$
      • usually used to screen unimportant variables
        • it is common to normalize these indices by the total variance $V(Y)$
      • three methods for computation - Fourire amplitude sensitivity test, meta-model, MCMC
      • when features are correlated, these can be strange (often inflating the main effects)
        • can consider $X_i^{\text{Correlated}} = E(X_i \vert X_{\sim i})$ and $X_i^{\text{Uncorrelated}} = X_i - X_i^{\text{Correlated}}$
    • this can help us understand the contributions that come from different features, as well as the correlations between features (e.g. $S_i^{\text{Uncorrelated}} = V(E[Y \vert X_i^{\text{Uncorrelated}}])/V(Y)$
  • moment-independent vim
    • want more than just the variance ot the output variables
    • e.g. delta index = average dist. between $f_Y(y)$ and $f_{Y \vert X_i}(y)$ when $X_i$ is fixed over its full distr.
      • $\delta_i = \frac 1 2 \mathbb E \int \vert f_Y(y) - f_{Y\vert X_i} (y) \vert dy = \frac 1 2 \int \int \vert f_{Y, X_i}(y, x_i) - f_Y(y) f_{X_i}(x_i) \vert dy \,dx_i$
      • moment-independent because it depends on the density, not just any moment (like measure of dependence between $y$ and $X_i$
    • can also look at KL, max dist..
  • graphic vim - like curves
    • e.g. scatter plot, meta-model plot, regional VIMs, parametric VIMs
    • CSM - relative change of model ouput mean when range of $X_i$ is reduced to any subregion
    • CSV - same thing for variance

importance curves

  • pdp plots - marginals (force value of plotted var to be what you want it to be)
  • separate into ice plots - marginals for instance
    • average of ice plots = pdp plot
    • sometimes these are centered, sometimes look at derivative
  • both pdp ice suffer from many points possibly not being real
  • possible solution: Marginal plots M-plots (bad name - uses conditional, not marginal)
    • only use points conditioned on certain variable
    • problem: this bakes things in (e.g. if two features are correlated and only one important, will say both are important)
  • ALE-plots - take points conditioned on value of interest, then look at differences in predictions around a window
    • this gives pure effect of that var and not the others
    • needs an order (i.e. might not work for caterogical)
    • doesn’t give you individual curves
    • recommended very highly by the book…
    • they integrate as you go…
  • summary: To summarize how each type of plot (PDP, M, ALE) calculates the effect of a feature at a certain grid value v:
    • Partial Dependence Plots: “Let me show you what the model predicts on average when each data instance has the value v for that feature. I ignore whether the value v makes sense for all data instances.”
  • M-Plots: “Let me show you what the model predicts on average for data instances that have values close to v for that feature. The effect could be due to that feature, but also due to correlated features.”
    • ALE plots: “Let me show you how the model predictions change in a small “window” of the feature around v for data instances in that window.”

example-based explanations

  • influential instances - want to find important data points
  • deletion diagnostics - delete a point and see how much it changed
  • influence funcs (koh & liang, 2017): use Hessian ($\theta x \theta$) to give effect of upweighting a point
    • influence functions = inifinitesimal approach - upweight one person by infinitesimally small weight and see how much estimate changes (e.g. calculate first derivative)
    • influential instance - when data point removed, has a strong effect on the model (not necessarily same as an outlier)
    • requires access to gradient (e.g. nn, logistic regression)
    • take single step with Newton’s method after upweighting loss
    • yield change in parameters by removing one point
    • yield change in loss at one point by removing a different point (by multiplying above by cahin rule)
    • yield change in parameters by modifying one point

tree ensembles

  • MDI = Gini importance
  • Breiman proposes permutation tests: Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1). Springer: 5–32
  • Explainable AI for Trees: From Local Explanations to Global Understanding (lundberg et al. 2019)
    • shap-interaction scores - distribute among pairwise interactions + local effects
    • plot lots of local interactions together - helps detect trends
    • propose doing shap directly on loss function (identify how features contribute to loss instead of prediction)
    • can run supervised clustering (where SHAP score is the label) to get meaningful clusters
      • alternatively, could do smth like CCA on the model output
  • conditional variable importance for random forests (strobl et al. 2008)
    • propose permuting conditioned on the values of variables not being permuted
      • to find region in which to permute, define the grid within which the values of $X_j$ are permuted for each tree by means of the partition of the feature space induced by that tree
    • many scores (such as MDI, MDA) measure marginal importance, not conditional importance
      • as a result, correlated variables get importances which are too high
  • treeshap (lundberg, erion & lee, 2019): prediction-level
    • individual feature attribution: want to decompose prediction into sum of attributions for each feature
      • each thing can depend on all features
    • Saabas method: basic thing for tree
      • you get a pred at end
      • count up change in value at each split for each variable
    • three properties
      • local acc - decomposition is exact
      • missingness - features that are already missing are attributed no importance
        • for missing feature, just (weighted) average nodes from each split
      • consistency - if F(X) relies more on a certain feature j, $F_j(x)$ should
        • however Sabaas method doesn’t change $F_j(X)$ for $F’(x) = F(x) + x_j$
    • these 3 things iply we want shap values
    • average increase in func value when selecting i (given all subsets of other features)
    • for binary features with totally random splits, same as Saabas
    • can cluster based on explanation similarity (fig 4)
      • can quantitatively evaluate based on clustering of explanations
    • their fig 8 - qualitatively can see how different features alter outpu
    • gini importance is like weighting all of the orderings
  • understanding variable importances in forests of randomized trees (louppe et al. 2013)
    • consider fully randomized trees
      • assume all categorical
      • randomly pick feature at each depth, split on all possibilities
      • also studied by biau 2012
      • extreme case of random forest w/ binary vars?
    • real trees are harder: correlated vars and stuff mask results of other vars lower down
    • asymptotically, randomized trees might actually be better
  • Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles (lucic et al. 2019)

neural nets (dnns)

dnn visualization

  • good summary on distill
  • visualize intermediate features
    1. visualize filters by layer - doesn’t really work past layer 1
    2. decoded filter - rafegas & vanrell 2016 - project filter weights into the image space - pooling layers make this harder
    3. deep visualization - yosinski 15
    4. Understanding Deep Image Representations by Inverting Them (mahendran & vedaldi 2014) - generate image given representation
  • penalizing activations
    • interpretable cnns (zhang et al. 2018) - penalize activations to make filters slightly more intepretable
      • could also just use specific filters for specific classes…
    • teaching compositionality to cnns - mask features by objects
  • approaches based on maximal activation
    • images that maximally activate a feature
      • deconv nets - Zeiler & Fergus (2014) use deconvnets (zeiler et al. 2011) to map features back to pixel space
        • given one image, get the activations (e.g. maxpool indices) and use these to get back to pixel space
        • everything else does not depend on the original image
        • might want to use optimization to generate image that makes optimal feature instead of picking from training set - before this, erhan et al. did this for unsupervised features - dosovitskiy et al 16 - train generative deconv net to create images from neuron activations - aubry & russel 15 do similar thing
      • deep dream - reconstruct image from feature map
      • could use natural image prior
      • could train deconvolutional NN
      • also called deep neuronal tuning - GD to find image that optimally excites filters
    • neuron feature - weighted average version of a set of maximum activation images that capture essential properties - rafegas_17
      • can also define color selectivity index - angle between first PC of color distribution of NF and intensity axis of opponent color space
      • class selectivity index - derived from classes of images that make NF
    • saliency maps for each image / class
      • simonyan et al 2014
    • Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics - sample deep dream images generated by gan
  • posthoc prototypes
    • counterfactual explanations - like adversarial, counterfactual explanation describes smallest change to feature vals that changes the prediction to a predefined output
      • maybe change fewest number of variables not their values
      • counterfactual should be reasonable (have likely feature values)
      • human-friendly
      • usually multiple possible counterfactuals (Rashomon effect)
      • can use optimization to generate counterfactual
      • anchors - opposite of counterfactuals, once we have these other things won’t change the prediction
    • prototypes (assumed to be data instances)
      • prototype = data instance that is representative of lots of points
      • criticism = data instances that is not well represented by the set of prototypes
      • examples: k-medoids or MMD-critic
        • selects prototypes that minimize the discrepancy between the data + prototype distributions
  • Explaining Deep Learning Models with Constrained Adversarial Examples
  • Understanding Deep Architectures by Visual Summaries
  • Semantics for Global and Local Interpretation of Deep Neural Networks
  • Iterative augmentation of visual evidence for weakly-supervised lesion localization in deep interpretability frameworks
  • explaining image classifiers by counterfactual generation
    • generate changes (e.g. with GAN in-filling) and see if pred actually changes
    • can search for smallest sufficient region and smallest destructive region
    • Screen Shot 2020-01-20 at 9.14.44 PM

dnn concept-based explanations

  • concept activation vectors
    • Given: a user-defined set of examples for a concept (e.g., ‘striped’), and random examples, labeled training-data examples for the studied class (zebras)
      • given trained network
      • TCAV can quantify the model’s sensitivity to the concept for that class. CAVs are learned by training a linear classifier to distinguish between the activations produced by a concept’s examples and examples in any layer
      • CAV - vector orthogonal to the classification boundary
      • TCAV uses the derivative of the CAV direction wrt input
    • automated concept activation vectors - Given a set of concept discovery images, each image is segmented with different resolutions to find concepts that are captured best at different sizes. (b) After removing duplicate segments, each segment is resized tothe original input size resulting in a pool of resized segments of the discovery images. (c) Resized segments are mapped to a model’s activation space at a bottleneck layer. To discover the concepts associated with the target class, clustering with outlier removal is performed. (d) The output of our method is a set of discovered concepts for each class, sorted by their importance in prediction
  • Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning - specify some interpretable features and learn a causal graph of how the classifier uses these features
  • On Completeness-aware Concept-Based Explanations in Deep Neural Networks

dnn feature importance

textual explanations

model summarization / distillation

connecting dnns with tree-models

  • Distilling a Neural Network Into a Soft Decision Tree (frosst & hinton 2017) - distills DNN into DNN-like tree which uses sigmoid neuron decides which path to follow
    • training on distilled DNN predictions outperforms training on original labels
    • to make the decision closer to a hard cut, can multiply by a large scalar before applying sigmoid
    • parameters updated with backprop
    • regularization to ensure that all paths are taken equally likely
  • Neural Random Forests (biau et al. 2018) - convert DNN to RF
    • first layer learns a node for each split
    • second layer learns a node for each leaf (by only connecting to nodes on leaves in the path)
    • finally map each leaf to a value
    • relax + retrain
  • Deep Neural Decision Forests (2015)
    • dnn learns small intermediate representation, which outputs all possible splits in a tree
    • these splits are forced into a tree-structure and optimized via SGD
    • neurons use sigmoid function
  • Gradient Boosted Decision Tree Neural Network - build DNN based on decision tree ensemble - basically the same but with gradient-boosted trees
  • Neural Decision Trees - treat each neural net like a node in a tree

different problems / perspectives

improving models

recourse

  • actionable recourse in linear classification (ustun et al. 2019)
    • want model to provide actionable inputs (e.g. income) rather than immutable variables (e.g. age, marital status)
      • drastic changes in actionable inputs are basically immutable
    • recourse - can person obtain desired prediction from fixed mode by changing actionable input variables (not just standard explainability)

interp for rl

  • heatmaps
  • visualize most interesting states / rollouts
  • language explanations
  • interpretable intermediate representations (e.g. bounding boxes for autonomous driving)
  • policy extraction - distill a simple model from a bigger model (e.g. neural net -> tree)

interpretation over sets / perturbations

These papers don’t quite connect to prediction, but are generally about finding stable interpretations across a set of models / choices.

  • All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (fisher, rudin, & dominici, 2019) - also had title Model class reliance: Variable importance measures for any machine learning model class, from the “Rashomon” perspective
    • model reliance = MR - like permutation importance, measures how much a model relies on covariates of interest for its accuracy
      • defined (for a feature) as the ratio of expected loss after permuting (with all possible permutation pairs) to before permuting
        • could also be defined as a difference or using predictions rather than loss
      • connects to U-statistics - can shows unbiased etc.
      • related to Algorithm Reliance (AR) - fitting with/without a feature and measuring the difference in loss (see gevrey et al. 03)
    • model-class reliance = MCR = highest/lowest degree of MR within a class of well-performing models
      • with some assumptions on model class complexity (in the form of a covering number), can create uniform bounds on estimation error
      • MCR can be efficiently computed for (regularized) linear / kernel linear models
    • Rashomon set = class of well-performing models
      • “Rashomon” effect of statistics - many prediction models may fit the data almost equally well (breiman 01)
      • “This set can be thought of as representing models that might be arrived at due to differences in data measurement, processing, filtering, model parameterization, covariate selection, or other analysis choices”
      • can study these tools for describing rank of risk predictions, variance of predictions, e.g. confidence intervals
      • Screen Shot 2020-09-27 at 8.20.35 AM
    • confidence intervals - can get finite-sample interval for anything, not just loss (e.g. norm of coefficients, prediction for a specific point)
    • connections to causality
      • when function is conditional expectation, then MR is similar to many things studies in causal literature
      • conditional importance measures a different notion (takes away things attributed to spurious variables)
        • can be hard to do conditional permutation well when some feature pairs are rare so can use weighting, matching, or imputation
    • here, application is to see on COMPAS dataset whether one can build an accurate model which doesn’t rely on race / sex (in order to audit black-box COMPAS models)
  • A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (coker, rudin, & king, 2018)
    • Inference = process of using facts we know to learn about facts we do not know
    • hacking intervals - the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data
      • prescriptively constrained hacking intervals - explicitly define reasonable analysis perturbations
        • ex. hyperparameters (e.g. k in kNN), matching algorithm, adding a new feature
      • tethered hacking intervals - take any model with small enough loss on the data
        • rather than choosing $\alpha$, we choose error tolerance
        • for MLE, equivalent to profile likelihood confidence intervals
        • ex. SVM distance from point to boundary, Kernel regression prediction for a specific new point, feature selection
        • ex. linear regression ATE, individual treatment effect
      • PCS intervals could be seen as slightly broder, including data cleaning and problem translations
    • different theories of inference have different counterfactual worlds
      • p-values - data from a superpopulation
      • Fisher’s exact p-values - fix the data and randomize counterfactual treatment assignments
      • Causal sensitivity analysis - unmeasured confounders from a defined set
      • bayesian credible intervals - redrawing the data from the same data generating process, given the observed data and assumed prior and likelihood model
      • hacking intervals - counterfactual researchers making counterfactual analysis choices
    • 2 approaches to replication
      • replicating studies - generally replication is very low
      • p-curve approach: look at distr. of p-values, check if lots of things are near 0.05
  • A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning (semenova, rudin, & parr, 2020)
    • rashomon ratio - ratio of the volume of the set of accurate models to the volume of the hypothesis space
      • can use this to perform model selection over different hypothesis spaces using empirical risk v. rashomon ratio (rashomon curve)

misc new papers