Core values¶
Shapley values define a fair way to distribute payoffs amongst all participants (training points) when they form a grand coalition, i.e. when the model is trained on the whole dataset. But they do not consider the question of stability: under which conditions do all participants in a game form the grand coalition? Are the payoffs distributed in such a way that prioritizes its formation?
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 subcoalition. 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 satisfies the following 2 properties:

Efficiency: The payoffs are distributed such that it is not possible to make any participant better off without making another one worse off. \(\sum_{i \in D} v(i) = u(D).\)

Coalitional rationality: The sum of payoffs to the agents in any coalition \(S\) is at least as large as 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 and Procaccia, 2021)^{1}, in the following form.
Least Core values¶
Unfortunately, for many cooperative games the Core may be empty. By relaxing the coalitional rationality property by a subsidy \(e \gt 0\), we are then able to find approximate payoffs:
The Least Core (LC) values \(\{v\}\) for utility \(u\) are computed by solving the following linear program:
Note that solving this program yields a set of solutions \(\{v_j:N \rightarrow \mathbb{R}\}\), whereas the Shapley value is a single function \(v\). In order to obtain a single valuation to use, one breaks ties by solving a quadratic program to select the \(v\) in the LC with the smallest \(\ell_2\) norm. This is called the egalitarian least core.
Exact Least Core¶
This first algorithm is just a verbatim implementation of the definition, in 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).
from pydvl.value import compute_least_core_values
values = compute_least_core_values(utility, mode="exact")
Monte Carlo Least Core¶
Because the number of subsets \(S \subseteq D \setminus \{i\}\) is \(2^{  D   1 }\), one typically must resort to approximations.
The simplest one consists in using a fraction of all subsets for the constraints. (Yan and Procaccia, 2021)^{1} show that a quantity of order \(\mathcal{O}((n  \log \Delta ) / \delta^2)\) is enough to obtain a socalled \(\delta\)approximate least core with high probability. I.e. the following property holds with probability \(1\Delta\) over the choice of subsets:
where \(e^{*}\) is the optimal least core subsidy. This approximation is also implemented in compute_least_core_values:
from pydvl.value import compute_least_core_values
values = compute_least_core_values(
utility, mode="montecarlo", n_iterations=n_iterations
)
Note
Although any number is supported, it is best to choose n_iterations
to be
at least equal to the number of data points.
Because computing the Least Core values requires the solution of a linear and a quadratic problem after computing all the utility values, we offer the possibility of splitting the latter from the former. This is useful when running multiple experiments: use mclc_prepare_problem to prepare a list of problems to solve, then solve them in parallel with lc_solve_problems.
from pydvl.value.least_core import mclc_prepare_problem, lc_solve_problems
n_experiments = 10
problems = [mclc_prepare_problem(utility, n_iterations=n_iterations)
for _ in range(n_experiments)]
values = lc_solve_problems(problems)
Method comparison¶
The TransferLab team reproduced the results of the original paper in a publication for the 2022 MLRC (Benmerzoug and Benito Delgado, 2023)^{2}.
Roughly speaking, MCLC performs better in identifying high value points, as measured by bestsample removal tasks. In all other aspects, it performs worse or similarly to TMCS at comparable sample budgets. But using an equal number of subsets is more computationally expensive because of the need to solve large linear and quadratic optimization problems.
For these reasons we recommend some variation of SV like TMCS for outlier detection, data cleaning and pruning, and perhaps MCLC for the selection of interesting points to be inspected for the improvement of data collection or model design.

Yan, T., Procaccia, A.D., 2021. If You Like Shapley Then You’ll Love the Core, in: Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. Presented at the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, pp. 5751–5759. https://doi.org/10.1609/aaai.v35i6.16721 ↩↩

Benmerzoug, A., Benito Delgado, M. de, 2023. [Re] If you like Shapley, then you’ll love the core. ReScience C 9. https://doi.org/10.5281/zenodo.8173733 ↩