We construct, for every function class $\mathcal{F}\subseteq[0,1]^{\mathcal{X}}$ and every accuracy $0<α\le 1$, an agnostic sample compression scheme for the empirical squared loss:
for every finite sample $S\in(\mathcal{X}\times[0,1])^m$ with arbitrary (noisy) labels, the scheme stores at most $O(\mathrm{fat}(\mathcal{F},c'α)\cdot\log^3(2/α))$ original labeled examples and auxiliary bits, independent of the sample size $m$, and reconstructs a function $\hat f$ with $L_2(\hat f,S)\le\inf_{f\in\mathcal{F}}L_2(f,S)+α$.
This resolves, in the positive, the open problem of Attias, Hanneke, Kontorovich, and Sadigurschi (ICML 2024, Section 5), which asks for an agnostic $\ell_2$ compression scheme of size $\mathrm{fat}(\mathcal{F},cα)\cdot\mathrm{polylog}(c/α)$. All previously known bounded-size constructions, agnostic and even realizable, incur a multiplicative dual fat-shattering factor, which can be exponentially larger than the primal dimension; our scheme removes the dual factor entirely, including in the realizable case.
The dual factor in prior work enters solely through a sparsification step that forces uniform approximation on the sample. By targeting only a $(1-ε)$-fraction of sample points, which suffices for an average-loss guarantee over a bounded range, K'egl's boosting margin bound yields $O(\log(1/ε))$ rounds independent of $m$, and sparsification is never needed.
The booster's synthetic target labels (values of a near-optimal $f^*\in\mathcal{F}$) are transmitted through quantized side-information bits attached to stored original examples, and the cross term of the squared loss forces the weak-learning scale $Θ(α)$, matching the same-scale form of the open problem.