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new 53176703 update site_head.html with newer MathJax
53176703 is described below
commit 531767033952e7abbbda265d54d7c8c6339fbe0f
Author: Lee Rhodes <[email protected]>
AuthorDate: Sun Jan 25 12:28:31 2026 -0800
update site_head.html with newer MathJax
---
_includes/site_head.html | 16 ++++++++++++++--
docs/Density/DensitySketch.md | 8 ++++----
2 files changed, 18 insertions(+), 6 deletions(-)
diff --git a/_includes/site_head.html b/_includes/site_head.html
index 0f597d38..969f4bf8 100644
--- a/_includes/site_head.html
+++ b/_includes/site_head.html
@@ -21,8 +21,20 @@
<link rel="stylesheet" href="/css/syntax.css">
<link rel="stylesheet" href="/css/docs.css">
-<script type="text/x-mathjax-config">
- MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'],
['\\(','\\)']]},showMathMenu:false,showMathMenuMSIE:false,showProcessingMessages:false});
+<script>
+ window.MathJax = {
+ tex: {
+ inlineMath: [['$', '$'], ['\\(', '\\)']],
+ displayMath: [['$$', '$$'], ['\\[', '\\]']]
+ },
+ options: {
+ enableMenu: false // Optional: hides the right-click context menu
+ }
+ };
+</script>
+
+<script id="MathJax-script" async
+ src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js";>
</script>
<!-- original source:
https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMX_HTML-full
-->
diff --git a/docs/Density/DensitySketch.md b/docs/Density/DensitySketch.md
index 8c547438..a5fc7c17 100644
--- a/docs/Density/DensitySketch.md
+++ b/docs/Density/DensitySketch.md
@@ -33,7 +33,7 @@ layout: doc_page
**Quick summary:** This sketch builds a coreset from the given set of input
points as multi-dimensional vectors. Provides density estimate at a given point.
<a id="paper"></a>
-### Our implementation is based on the following paper:
+### Our implementation was based on the following paper:
* Zohar Karnin, Edo Liberty "Discrepancy, Coresets, and Sketches in Machine
Learning"
https://proceedings.mlr.press/v99/karnin19a/karnin19a.pdf
@@ -44,12 +44,12 @@ https://proceedings.mlr.press/v99/karnin19a/karnin19a.pdf
#### Key Highlights:
* **New Complexity Measure:** The authors define "class discrepancy" as a way
to characterize the coreset complexity of different function families, similar
to how Rademacher complexity is used for generalization.
-* **Improved Coreset Sizes:** They prove the existence of
ε-approximation coresets of size *O(√d/ε)* for several
common machine learning problems, including:
+* **Improved Coreset Sizes:** They prove the existence of
ε-approximation coresets of size $O(\sqrt{d}/\epsilon)$ for several
common machine learning problems, including:
* Logistic regression
* Sigmoid activation loss
* Matrix covariance
* Kernel density estimation
-* **Gaussian Kernel Resolution:** The paper resolves a long-standing open
problem by matching the lower bound for the coreset complexity of Gaussian
kernel density estimation at *O(√d/ε)*.
+* **Gaussian Kernel Resolution:** The paper resolves a long-standing open
problem by matching the lower bound for the coreset complexity of Gaussian
kernel density estimation at $O(\sqrt{d}/\epsilon)$.
* **Streaming Algorithms:** It introduces an exponential improvement to the
"merge-and-reduce" trick, leading to better streaming sketches for any problem
with low discrepancy.
* **Deterministic Algorithm:** The authors provide a simple, deterministic
algorithm for finding low-discrepancy sequences and coresets for any positive
semi-definite kernel.
@@ -58,7 +58,7 @@ https://proceedings.mlr.press/v99/karnin19a/karnin19a.pdf
The findings allow for significantly faster optimization in large-scale
machine learning. By reducing a massive dataset into a much smaller coreset,
researchers can perform complex calculations (like training a logistic
regression model) with a fraction of the computational cost while maintaining a
high level of accuracy.
<a id="inspiration"></a>
-### Our implementations was inspired by the following code, example, and tests
by Edo Liberty:
+### Our implementation was inspired by the following code, example, and tests
by Edo Liberty:
* **Code:**
https://github.com/edoliberty/streaming-quantiles/blob/f688c8161a25582457b0a09deb4630a81406293b/gde.py
* **Example**
https://github.com/edoliberty/streaming-quantiles/blob/f688c8161a25582457b0a09deb4630a81406293b/gde_example_usage.ipynb
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