Results 2026-10-23 59fae4ed912c

benchmark-info/benchmark-protocol.md

@@ -1,4 +1,4 @@
-# Benchmark protocol
+# Benchmark methodology
 
 Here we explain how we built this benchmark: which tasks we
 compare, on which data, at which privacy levels, and how we rank algorithms.
@@ -64,16 +64,16 @@ each task, every problem instance also has a nonprivate estimate. We measure err
 - **Linear regression:** the metric is the Mahalanobis norm between the private and OLS coefficients:
 
     $$
-    \frac{1}{\sqrt{n}} \bigl\lVert X(\hat\beta - \hat\beta_{\mathrm{OLS}}) \bigr\rVert_2 .
+    \frac{1}{\sqrt{n}} \bigl\lVert X(\hat\beta - \hat\beta_{\rm OLS}) \bigr\rVert_2 .
     $$
 
     The sampling error baseline is
 
     $$
-    \sqrt{\frac{s^2 d}{n}}, \qquad s^2 = \frac{\lVert y - X\hat\beta_{\mathrm{OLS}} \rVert_2^2}{n - d},
+    \sqrt{\frac{s^2 d}{n}}, \qquad s^2 = \frac{\lVert y - X\hat\beta_{\rm OLS} \rVert_2^2}{n - d},
     $$
 
-    which, under typical homoskedastic linear-model assumptions, estimates the OLS sampling error $\frac{1}{\sqrt{n}} \lVert X(\hat\beta_{\mathrm{OLS}} - \beta^\star) \rVert_2$. This baseline is used in classical joint F-tests for simple hypothesis tests in linear regression.
+    which, under typical homoskedastic linear-model assumptions, estimates the OLS sampling error $\frac{1}{\sqrt{n}} \lVert X(\hat\beta_{\rm OLS} - \beta^\star) \rVert_2$. This baseline is used in classical joint F-tests for simple hypothesis tests in linear regression.
 
 - **Mean estimation:** the Mahalanobis error between the private estimate
     and the sample mean, using the sample covariance $S$ with divisor $n-1$:

benchmark-info/provenance/algorithms/sce-component-comparisons.md

@@ -9,6 +9,6 @@ Each family pairs a canonical arm with a whitening arm:
 | Canonical | `ToLinearRegression → LR ApproxBounds → LR Standardize` |
 | Whitening | `SCE ApproxBounds → SCE Standardize → DPWhitening → ToLinearRegression → LR Standardize` |
 
-Both arms use each family's untuned base parameters; AMP keeps `gradient_tolerance=0.01` in both loss families. `ApproxBounds` uses `bounds_share=0.1`, `scale=1.0` and `num_bins=64`, and whitening uses `preparation_share=0.25`, at every privacy budget. Each stage splits its incoming budget by the library's typed budget split. `ApproxBounds` skips private estimation on publicly bounded coordinates and preserves intercept and one-hot blocks; there is no re-bounding after whitening. For an intercept target, SCE standardization scales without centering.
+Both arms use each family's untuned base parameters; AMP keeps its default `gradient_tolerance="inverse_n"` in both loss families. `ApproxBounds` uses `bounds_share=0.1`, `scale=1.0` and `num_bins=64`, and whitening uses `preparation_share=0.25`, at every privacy budget. Each stage splits its incoming budget by the library's typed budget split. `ApproxBounds` skips private estimation on publicly bounded coordinates and preserves intercept and one-hot blocks; there is no re-bounding after whitening. For an intercept target, SCE standardization scales without centering.
 
 Bounds placement, scaling, privacy spending and the transported domain all differ between the arms, so each comparison measures the whole pipeline. It does not isolate a cost-free matrix operation, and it does not claim that whitening improves every core.

linear_regression/algorithm_registries/benchmark.json

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single_coefficient_estimation/benchmark-info/algorithm-declarations.csv

@@ -7,7 +7,6 @@ a3-3A55XWHW4SQEM4SI,House SCE component comparison with fixed core parameters an
 a3-3CAKERO2A37AAPTU,Tuned on this benchmark's data; selected at eps=3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-3FHVHLYLAHLB6QMM,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-3MLTQMIBYAQAUJZG,Tuned on this benchmark's data; selected at eps=10.,benchmark-info/provenance/algorithms/retune-campaign-2.md
-a3-3QVA4HGPLBNKNG3B,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-57HAU6YLAR6WQ4HV,Tuned on this benchmark's data; selected at eps=3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-5QBIQL3NORULXU5F,"Appears as Algorithm 1 in Slavkovic and Molinari (2021), Perturbed M-Estimation.",benchmark-info/provenance/algorithms/pme.md
 a3-5VAY2DR4PPP6CG7X,"Appears as the Appendix H linear regression of Kifer, Smith, and Thakurta (2012).",benchmark-info/provenance/algorithms/kst-squared-loss.md
@@ -32,6 +31,7 @@ a3-CNVVOCMJ3JM5QUOL,Tuned on this benchmark's data; selected at eps=10.,benchmar
 a3-D7NKBIUOVQZDCQMU,Tuned on this benchmark's data; selected at eps=0.3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-DHSC7LBFDTKNBL5L,Tuned on this benchmark's data; selected at eps=0.3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-DIAB5GAVN7DJVUWX,Tuned on this benchmark's data; selected at eps=3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
+a3-DLINLG6ICOXJKOGP,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-DPEG37FAJHGYPSXO,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-DTDZW7SM5Z4RFNHB,Tuned on this benchmark's data; selected at eps=1.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-E3CEPM6GEV6P3R7C,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
@@ -65,7 +65,6 @@ a3-KDDZ65LE42PVQOVP,Tuned on this benchmark's data; selected at eps=1.,benchmark
 a3-KE742HCGCFHGQJSE,Tuned on this benchmark's data; selected at eps=1.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-KZFUEHL7DBM3CF3S,Tuned on this benchmark's data; selected at eps=1.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-L7PTG3RAD4OBEQ2L,Tuned on this benchmark's data; selected at eps=10.,benchmark-info/provenance/algorithms/retune-campaign-2.md
-a3-LCVHECH2CEXXL3OJ,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-LJVGPIPNGC5KRSLV,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-LK3XEO4XUMLFTOO4,Tuned on this benchmark's data; selected at eps=1.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-LOXXUID575SFS467,"Appears as K-BAS in Section 4.2 of Dick, Gillenwater, and Joseph (2023), Better Private Linear Regression Through Better Private Feature Selection.",benchmark-info/provenance/algorithms/dgj-feature-selection.md
@@ -92,6 +91,7 @@ a3-R7SVIHC64O3NOPBQ,House SCE component comparison with fixed core parameters an
 a3-RDXYAGPNO6WM6PYJ,Tuned on this benchmark's data; selected at eps=0.3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-RDZJGHY3S5WMUQV7,Tuned on this benchmark's data; selected at eps=10.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-RIVYIIKGSKUJLUOQ,Tuned on this benchmark's data; selected at eps=0.3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
+a3-S7WPJGEFCQSKCZ56,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md
 a3-SDN457YJ4NAX5GID,Tuned on this benchmark's data; selected at eps=3.,benchmark-info/provenance/algorithms/retune-campaign-2.md
 a3-SS4OCEYEKKJXPPFE,"Appears as Algorithm 1 in Slavkovic and Molinari (2021), Perturbed M-Estimation.",benchmark-info/provenance/algorithms/pme.md
 a3-SYDNYZOR6RYKVGCQ,House SCE component comparison with fixed core parameters and preparation; not selected for a tuning gain.,benchmark-info/provenance/algorithms/sce-component-comparisons.md

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