Doc/glossary

CHANGELOG.md

@@ -4,6 +4,10 @@
 
 ### Added
 
+- Many improvements to the documentation: fixes, links, text, example gallery
+  and more. [PR #532](https://github.com/aai-institute/pyDVL/pull/532)
+- Glossary of data valuation terms in the docs.
+  [PR #537](https://github.com/aai-institute/pyDVL/pull/537
 - Implement new method: `NystroemSketchInfluence` 
   [PR #504](https://github.com/aai-institute/pyDVL/pull/504)
 - Add property `model_dtype` to instances of type `TorchInfluenceFunctionModel`
@@ -24,8 +28,6 @@
 
 ### Changed
 
-- Improvements to documentation: fixes, links, text, example gallery and more
-  [PR #532](https://github.com/aai-institute/pyDVL/pull/532)
 - Bump versions of CI actions to avoid warnings [PR #502](https://github.com/aai-institute/pyDVL/pull/502)
 - Add Python Version 3.11 to supported versions [PR #510](https://github.com/aai-institute/pyDVL/pull/510)
 - Documentation improvements and cleanup [PR #521](https://github.com/aai-institute/pyDVL/pull/521) [PR #522](https://github.com/aai-institute/pyDVL/pull/522)

docs/getting-started/glossary.md

@@ -0,0 +1,189 @@
+# Glossary
+
+This glossary is meant to provide only brief explanations of each term, helping
+to clarify the concepts and techniques used in the library. For more detailed
+information, please refer to the relevant literature or resources.
+
+!!! warning
+    This glossary is still a work in progress. Pull requests are welcome!
+
+Terms in data valuation and influence functions:
+
+### Class-wise Shapley
+
+Class-wise Shapley is a Shapley valuation method which introduces a utility
+function that balances in-class, and out-of-class accuracy, with the goal of
+favoring points that improve the model's performance on the class they belong
+to. It is estimated to be particularly useful in imbalanced datasets, but more
+research is needed to confirm this.
+Introduced by [@schoch_csshapley_2022].
+[Implementation][pydvl.value.shapley.classwise.compute_classwise_shapley_values].
+
+### Conjugate Gradient
+
+CG is an algorithm for solving linear systems with a symmetric and
+positive-definite coefficient matrix. For Influence Functions, it is used to
+approximate the [iHVP][inverse-hessian-vector-product].
+[Implementation (torch)][pydvl.influence.torch.influence_function_model.CgInfluence].
+
+
+### Data Utility Learning
+
+Data Utility Learning is a method that uses an ML model to learn the utility
+function. Essentially, it learns to predict the performance of a model when
+trained on a given set of indices from the dataset. The cost of training this
+model is quickly amortized by avoiding costly re-evaluations of the original
+utility.
+Introduced by [@wang_improving_2022].
+[Implementation][pydvl.utils.utility.DataUtilityLearning].
+
+### Eigenvalue-corrected Kronecker-Factored Approximate Curvature
+
+EKFAC builds on [K-FAC][kronecker-factored-approximate-curvature] by correcting
+for the approximation errors in the eigenvalues of the blocks of the
+Kronecker-factored approximate curvature matrix. This correction aims to refine
+the accuracy of natural gradient approximations, thus potentially offering
+better training efficiency and stability in neural networks.
+[Implementation (torch)][pydvl.influence.torch.influence_function_model.EkfacInfluence].
+
+### Group Testing
+
+Group Testing is a strategy for identifying characteristics within groups of
+items efficiently, by testing groups rather than individuals to quickly narrow
+down the search for items with specific properties.
+Introduced into data valuation by [@jia_efficient_2019a].
+[Implementation][pydvl.value.shapley.gt.group_testing_shapley].
+
+### Influence Function
+
+The Influence Function measures the impact of a single data point on a
+statistical estimator. In machine learning, it's used to understand how much a
+particular data point affects the model's prediction.
+Introduced into data valuation by [@koh_understanding_2017].
+[[influence-function|Documentation]].
+
+### Inverse Hessian-vector product
+
+iHVP is the operation of calculating the product of the inverse Hessian matrix
+of a function and a vector, without explicitly constructing nor inverting the
+full Hessian matrix first. This is essential for influence function computation.
+
+### Kronecker-Factored Approximate Curvature
+
+K-FAC is an optimization technique that approximates the Fisher Information
+matrix's inverse efficiently. It uses the Kronecker product to factor the
+matrix, significantly speeding up the computation of natural gradient updates
+and potentially improving training efficiency.
+
+### Least Core
+
+The Least Core is a solution concept in cooperative game theory, referring to
+the smallest set of payoffs to players that cannot be improved upon by any
+coalition, ensuring stability in the allocation of value. In data valuation,
+it implies solving a linear and a quadratic system whose constraints are
+determined by the evaluations of the utility function on every subset of the
+training data.
+Introduced as data valuation method by [@yan_if_2021].
+[Implementation][pydvl.value.least_core.compute_least_core_values].
+
+### Linear-time Stochastic Second-order Algorithm
+
+LiSSA is an efficient algorithm for approximating the inverse Hessian-vector
+product, enabling faster computations in large-scale machine learning
+problems, particularly for second-order optimization.
+Introduced by [@agarwal_secondorder_2017].
+[Implementation (torch)][pydvl.influence.torch.influence_function_model.LissaInfluence].
+
+### Leave-One-Out
+
+LOO in the context of data valuation refers to the process of evaluating the
+impact of removing individual data points on the model's performance. The
+value of a training point is defined as the marginal change in the model's
+performance when that point is removed from the training set.
+[Implementation][pydvl.value.loo.loo.compute_loo].
+
+### Monte Carlo Least Core
+
+MCLC is a variation of the Least Core that uses a reduced amount of
+constraints, sampled randomly from the powerset of the training data.
+Introduced by [@yan_if_2021].
+[Implementation][pydvl.value.least_core.compute_least_core_values].
+
+### Monte Carlo Shapley
+
+MCS estimates the Shapley Value using a Monte Carlo approximation to the sum
+over subsets of the training set. This reduces computation to polynomial time
+at the cost of accuracy, but this loss is typically irrelevant for downstream
+applications in ML.
+Introduced into data valuation by [@ghorbani_data_2019].
+[Implementation][pydvl.value.shapley.montecarlo].
+[[data-valuation|Documentation]].
+
+### Point removal task
+
+A task in data valuation where the quality of a valuation method is measured
+through the impact of incrementally removing data points on the model's
+performance, where the points are removed in order of their value. See
+[Benchmarking tasks][benchmarking-tasks].
+
+
+### Shapley Value
+
+Shapley Value is a concept from cooperative game theory that allocates payouts
+to players based on their contribution to the total payoff. In data valuation,
+players are data points. The method assigns a value to each data point based
+on a weighted average of its marginal contributions to the model's performance 
+when trained on each subset of the training set. This requires
+$\mathcal{O}(2^{n-1})$ re-trainings of the model, which is infeasible for even
+trivial data set sizes, so one resorts to approximations like TMCS.
+Introduced into data valuation by [@ghorbani_data_2019].
+[Implementation][pydvl.value.shapley.naive].
+[[data-valuation|Documentation]].
+
+### Truncated Monte Carlo Shapley
+
+TMCS is an efficient approach to estimating the Shapley Value using a
+truncated version of the Monte Carlo method, reducing computation time while
+maintaining accuracy in large datasets.
+Introduced by [@ghorbani_data_2019].
+[Implementation][pydvl.value.shapley.montecarlo.permutation_montecarlo_shapley].
+[[data-valuation|Documentation]].
+
+### Weighted Accuracy Drop
+
+WAD is a metric to evaluate the impact of sequentially removing data points on
+the performance of a machine learning model, weighted by their rank, i.e. by the
+time at which they were removed.
+Introduced by [@schoch_csshapley_2022].
+
+---
+
+## Other terms
+
+### Coefficient of Variation
+
+CV is a statistical measure of the dispersion of data points in a data series
+around the mean, expressed as a percentage. It's used to compare the degree of
+variation from one data series to another, even if the means are drastically
+different.
+
+
+### Constraint Satisfaction Problem
+
+A CSP involves finding values for variables within specified constraints or
+conditions, commonly used in scheduling, planning, and design problems where
+solutions must satisfy a set of restrictions.
+
+### Out-of-Bag
+
+OOB refers to data samples in an ensemble learning context (like random forests)
+that are not selected for training a specific model within the ensemble. These
+OOB samples are used as a validation set to estimate the model's accuracy,
+providing a convenient internal cross-validation mechanism.
+
+### Machine Learning Reproducibility Challenge
+
+The [MLRC](https://reproml.org/) is an initiative that encourages the
+verification and replication of machine learning research findings, promoting
+transparency and reliability in the field. Papers are published in
+[Transactions on Machine Learning Research](https://jmlr.org/tmlr/) (TMLR).

docs/value/the-core.md

@@ -15,8 +15,7 @@ The Core is another solution concept in cooperative game theory that attempts to
 ensure stability in the sense that it provides the set of feasible payoffs that
 cannot be improved upon by a sub-coalition. This can be interesting for some
 applications of data valuation because it yields values consistent with training
-on the whole dataset, avoiding the spurious selection of subsets. It was first
-introduced in this field by [@yan_if_2021].
+on the whole dataset, avoiding the spurious selection of subsets.
 
 It satisfies the following 2 properties:
 
@@ -30,6 +29,9 @@ It satisfies the following 2 properties:
   the amount that these agents could earn by forming a coalition on their own.
   $\sum_{i \in S} v(i) \geq u(S), \forall S \subset D.$
 
+The Core was first introduced into data valuation by [@yan_if_2021], in the
+following form.
+
 ## Least Core values
 
 Unfortunately, for many cooperative games the Core may be empty. By relaxing the
@@ -59,9 +61,12 @@ _egalitarian least core_.
 
 ## Exact Least Core
 
-This first algorithm is just a verbatim implementation of the definition.
-As such it returns as exact a value as the utility function allows
-(see what this means in [Problems of Data Values][problems-of-data-values]).
+This first algorithm is just a verbatim implementation of the definition, in
+[compute_least_core_values][pydvl.value.least_core.compute_least_core_values].
+It computes all constraints for the linear problem by evaluating the utility on
+every subset of the training data, and returns as exact a value as the utility
+function allows (see what this means in [Problems of Data
+Values][problems-of-data-values]).
 
 ```python
 from pydvl.value import compute_least_core_values
@@ -74,18 +79,20 @@ values = compute_least_core_values(utility, mode="exact")
 Because the number of subsets $S \subseteq D \setminus \{i\}$ is
 $2^{ | D | - 1 }$, one typically must resort to approximations.
 
-The simplest approximation consists in using a fraction of all subsets for the
-constraints. [@yan_if_2021] show that a quantity of order
-$\mathcal{O}((n - \log \Delta ) / \delta^2)$ is enough to obtain a so-called
-$\delta$-*approximate least core* with high probability. I.e. the following
-property holds with probability $1-\Delta$ over the choice of subsets:
+The simplest one consists in using a fraction of all subsets for the constraints.
+[@yan_if_2021] show that a quantity of order $\mathcal{O}((n - \log \Delta ) /
+\delta^2)$ is enough to obtain a so-called $\delta$-*approximate least core*
+with high probability. I.e. the following property holds with probability
+$1-\Delta$ over the choice of subsets:
 
 $$
 \mathbb{P}_{S\sim D}\left[\sum_{i\in S} v(i) + e^{*} \geq u(S)\right]
 \geq 1 - \delta,
 $$
 
-where $e^{*}$ is the optimal least core subsidy.
+where $e^{*}$ is the optimal least core subsidy. This approximation is
+also implemented in
+[compute_least_core_values][pydvl.value.least_core.compute_least_core_values]:
 
 ```python
 from pydvl.value import compute_least_core_values

docs_includes/abbreviations.md

@@ -1,25 +1,25 @@
+*[CG]: Conjugate Gradient
 *[CSP]: Constraint Satisfaction Problem
 *[CV]: Coefficient of Variation
 *[CWS]: Class-wise Shapley
 *[DUL]: Data Utility Learning
+*[EKFAC]: Eigenvalue-corrected Kronecker-Factored Approximate Curvature
 *[GT]: Group Testing
 *[IF]: Influence Function
 *[iHVP]: inverse Hessian-vector product
+*[K-FAC]: Kronecker-Factored Approximate Curvature
 *[LC]: Least Core
-*[LiSSA]: Linear-time Stochastic Second-order Algorithm
 *[LOO]: Leave-One-Out
+*[LiSSA]: Linear-time Stochastic Second-order Algorithm
 *[MCLC]: Monte Carlo Least Core
 *[MCS]: Monte Carlo Shapley
-*[ML]: Machine Learning
 *[MLP]: Multi-Layer Perceptron
 *[MLRC]: Machine Learning Reproducibility Challenge
+*[ML]: Machine Learning
 *[MSE]: Mean Squared Error
+*[OOB]: Out-of-Bag
 *[PCA]: Principal Component Analysis
 *[ROC]: Receiver Operating Characteristic
 *[SV]: Shapley Value
 *[TMCS]: Truncated Monte Carlo Shapley
 *[WAD]: Weighted Accuracy Drop
-*[OOB]: Out-of-Bag
-*[CG]: Conjugate Gradient
-*[K-FAC]: Kronecker-Factored Approximate Curvature
-*[EKFAC]: Eigenvalue-corrected Kronecker-Factored Approximate Curvature

mkdocs.yml

@@ -14,6 +14,7 @@ nav:
     - Applications: getting-started/applications.md
     - Benchmarking: getting-started/benchmarking.md
     - Methods: getting-started/methods.md
+    - Glossary: getting-started/glossary.md
   - Data Valuation:
     - value/index.md
     - Shapley values: value/shapley.md