Merge remote-tracking branch 'origin/main' into multi-task-visualization

docs/curriculum/concepts/multitask/multitask.md

@@ -44,3 +44,5 @@ Check out our multitask tutorial where we apply multitask optimization to the jo
 Swersky K, Snoek J, Adams RP. Multi-task bayesian optimization. Advances in neural information processing systems. 2013. [🔗](https://proceedings.neurips.cc/paper/2013/hash/f33ba15effa5c10e873bf3842afb46a6-Abstract.html)
 
 Bonilla EV, Chai K, Williams C. Multi-task Gaussian process prediction. Advances in neural information processing systems. 2007. [🔗](https://proceedings.neurips.cc/paper_files/paper/2007/hash/66368270ffd51418ec58bd793f2d9b1b-Abstract.html)
+
+Gaussian Process Summer School: https://youtu.be/VAQRguYCoDI

src/honegumi/ax/main.py.jinja

@@ -135,6 +135,8 @@ X_train = pd.DataFrame([
 # Define y_train (normally the values would be supplied directly instead of calculating here)
 y_train = [{{ objective_function }}(row["x1"], row["x2"]{% if composition_constraint %}, row["x3"]{% endif %}{% if categorical %}, row["c1"]{% endif %}) for _, row in X_train.iterrows()]
 
+# See https://youtu.be/4tnaL9ts6CQ for simple human-in-the-loop BO instructions
+
 # Define the number of training examples
 n_train = len(X_train)
 {%- endif %}