shuo-zhou · Oct 16, 2026 · Sep 10, 2026 · Sep 17, 2026 · Sep 17, 2026 · Sep 17, 2026
diff --git a/.github/changelog-config.json b/.github/changelog-config.json
@@ -2,31 +2,53 @@
   "template": "#{{CHANGELOG}}\n\n#### Other Changes\n\n#{{UNCATEGORIZED}}",
   "pr_template": "- #{{TITLE}} ([##{{NUMBER}}](#{{URL}}))",
   "empty_template": "- No pull requests were merged since the last release.",
-  "ignore_labels": ["dependabot", "bot", "ci"],
+  "ignore_labels": [
+    "dependabot",
+    "bot",
+    "ci"
+  ],
   "categories": [
     {
       "title": "#### New Features",
-      "labels": ["feature", "enhancement"],
+      "labels": [
+        "feature",
+        "enhancement"
+      ],
       "consume": true
     },
     {
       "title": "#### Bug Fixes",
-      "labels": ["bug", "bugfix"],
+      "labels": [
+        "bug",
+        "bugfix"
+      ],
       "consume": true
     },
     {
       "title": "#### Code Improvements",
-      "labels": ["improvements", "refactor", "refactoring"],
+      "labels": [
+        "improvements",
+        "refactor",
+        "refactoring"
+      ],
       "consume": true
     },
     {
       "title": "#### Documentation",
-      "labels": ["documentation", "docs", "doc"],
+      "labels": [
+        "documentation",
+        "docs",
+        "doc"
+      ],
       "consume": true
     },
     {
       "title": "#### Tests",
-      "labels": ["tests", "testing", "test"],
+      "labels": [
+        "tests",
+        "testing",
+        "test"
+      ],
       "consume": true
     }
   ]

diff --git a/README.md b/README.md
@@ -19,6 +19,7 @@ The package is part of the [PyKale](https://github.com/pykale/pykale) ecosystem
   - Dimension reduction for multiview tensor data:
     - Multilinear Principal Component Analysis (`MPCA`) [[1](#references)]
   - Transferable / generalizable feature extraction across domains or groups:
+    - Correlation Alignment (`CORAL`) [[13](#references)]
     - Transfer Component Analysis (`TCA`) [[2](#references)]
     - Joint Distribution Adaptation (`JDA`) [[3](#references)]
     - Balanced Distribution Adaptation (`BDA`) [[4](#references)]
@@ -97,14 +98,15 @@ See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed contribution guidelines.
 ### Public API
 
 ```python
-from kalelinear.transformer import BDA, JDA, MIDA, MPCA, TCA
+from kalelinear.transformer import BDA, CORAL, JDA, MIDA, MPCA, TCA
 from kalelinear.estimator import ARRLS, ARSVM, CoIRLS, CoIRSVM, GSDA, LapRLS, LapSVM
 ```
 
 Worked examples for the main transformers and estimators are collected in
 [Tutorials](TUTORIALS.md):
 
 - Learn a domain-invariant embedding with TCA
+- Align source and target features with CORAL
 - Use MIDA with categorical covariates
 - Extract common and individual features across groups with CIFE or AJIVE
 - Train a domain adaptation classifier (ARSVM, ARRLS)
@@ -136,6 +138,8 @@ Worked examples for the main transformers and estimators are collected in
 
 [12] Feng, Q., Jiang, M., Hannig, J. and Marron, J.S., 2018. [Angle-based joint and individual variation explained](https://www.sciencedirect.com/science/article/pii/S0047259X1730204X). _Journal of Multivariate Analysis_, 166, pp.241-265.
 
+[13] Sun, B., Feng, J. and Saenko, K., 2016. [Return of frustratingly easy domain adaptation](https://ojs.aaai.org/index.php/AAAI/article/view/10306). In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 30, No. 1, pp. 2058-2065).
+
 ## Other open domain adaptation repositories
 
 - [POT: Python Optimal Transport](https://github.com/rflamary/POT)

diff --git a/TUTORIALS.md b/TUTORIALS.md
@@ -37,6 +37,39 @@ TCA, JDA, and BDA take domain labels through `covariates`. They do not accept
 separate source and target arrays; stack samples into one array and use
 `target_covariate` to identify the target domain.
 
+### Align Source and Target Features with CORAL
+
+CORAL is an asymmetric domain alignment method: it whitens the source
+covariance and recolors it with the target covariance. As with TCA, pass all
+samples in one array with binary domain labels. `fit_transform` centers each
+domain by its own mean, aligns the source samples, and leaves the target
+samples in the same centered space.
+
+```python
+import numpy as np
+from kalelinear.transformer import CORAL
+
+X = np.array(
+    [
+        [-2.0, -1.8],
+        [-1.8, -2.1],
+        [1.9, 1.7],
+        [2.1, 2.0],
+        [-1.4, -1.2],
+        [-1.2, -1.1],
+        [1.2, 1.1],
+        [1.4, 1.3],
+    ]
+)
+domain_labels = np.array([0, 0, 0, 0, 1, 1, 1, 1])
+
+transformer = CORAL()
+z = transformer.fit_transform(X, covariates=domain_labels, target_covariate=1)
+
+z_source = z[domain_labels == 0]
+z_target = z[domain_labels == 1]
+```
+
 ### Use MIDA with Categorical Domain Covariates
 
 ```python

diff --git a/docs/source/api_transformers.rst b/docs/source/api_transformers.rst
@@ -26,6 +26,11 @@ The transformer classes are also available through the PyKale-compatible alias
    :undoc-members:
    :show-inheritance:
 
+.. autoclass:: CORAL
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 .. autoclass:: MIDA
    :members:
    :undoc-members:

diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
@@ -14,8 +14,8 @@ covariates, side information, or unlabeled target samples.
 Main Features
 -------------
 
-* Transformer models for learning feature embeddings: MPCA, TCA, JDA, BDA,
-  MIDA, CIFE, and AJIVE.
+* Transformer models for learning feature embeddings: CORAL, MPCA, TCA, JDA,
+  BDA, MIDA, CIFE, and AJIVE.
 * Estimator models for classification and adaptation: LapSVM, LapRLS, ARSVM,
   ARRLS, CoIRSVM, CoIRLS, and GSDA.
 * NumPy-compatible inputs and outputs.

diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
@@ -33,6 +33,40 @@ label is the target domain.
    z_source = z[domain_labels == 0]
    z_target = z[domain_labels == 1]
 
+Align Source and Target Features with CORAL
+-------------------------------------------
+
+CORAL is an asymmetric domain alignment method: it whitens the source
+covariance and recolors it with the target covariance. As with TCA, pass all
+samples in one array with binary domain labels. ``fit_transform`` centers each
+domain by its own mean, aligns the source samples, and leaves the target
+samples in the same centered space.
+
+.. code-block:: python
+
+   import numpy as np
+   from kalelinear.transformer import CORAL
+
+   X = np.array(
+       [
+           [-2.0, -1.8],
+           [-1.8, -2.1],
+           [1.9, 1.7],
+           [2.1, 2.0],
+           [-1.4, -1.2],
+           [-1.2, -1.1],
+           [1.2, 1.1],
+           [1.4, 1.3],
+       ]
+   )
+   domain_labels = np.array([0, 0, 0, 0, 1, 1, 1, 1])
+
+   transformer = CORAL()
+   z = transformer.fit_transform(X, covariates=domain_labels, target_covariate=1)
+
+   z_source = z[domain_labels == 0]
+   z_target = z[domain_labels == 1]
+
 Use MIDA with Categorical Covariates
 ------------------------------------
 

diff --git a/docs/source/usage.rst b/docs/source/usage.rst
@@ -6,9 +6,10 @@ Usage
 Domain Adaptation Transformers
 ------------------------------
 
-TCA, JDA, and BDA take all samples in a single input array and receive domain
-labels through ``covariates``. Use ``target_covariate`` to identify which domain
-label is the target domain.
+TCA, JDA, BDA, and CORAL take all samples in a single input array and receive
+domain labels through ``covariates``. Use ``target_covariate`` to identify
+which domain label is the target domain. CORAL is asymmetric: after fitting, it
+aligns source samples to the target covariance and centers target samples.
 
 .. code-block:: python
 

diff --git a/kalelinear/embed.py b/kalelinear/embed.py
@@ -1,5 +1,5 @@
 """Embedding models exposed with a PyKale-style API."""
 
-from kalelinear.transformer import AJIVE, BDA, CIFE, JDA, MIDA, MPCA, TCA
+from kalelinear.transformer import AJIVE, BDA, CIFE, CORAL, JDA, MIDA, MPCA, TCA
 
-__all__ = ["TCA", "JDA", "BDA", "MIDA", "MPCA", "CIFE", "AJIVE"]
+__all__ = ["TCA", "JDA", "BDA", "CORAL", "MIDA", "MPCA", "CIFE", "AJIVE"]
diff --git a/kalelinear/transformer/__init__.py b/kalelinear/transformer/__init__.py
@@ -1,5 +1,6 @@
 from kalelinear.transformer._ajive import AJIVE
 from kalelinear.transformer._cife import CIFE
+from kalelinear.transformer._coral import CORAL
 from kalelinear.transformer._jda import BDA, JDA
 from kalelinear.transformer._mida import MIDA
 from kalelinear.transformer._mpca import MPCA
@@ -9,6 +10,7 @@
     "TCA",
     "JDA",
     "BDA",
+    "CORAL",
     "MIDA",
     "MPCA",
     "CIFE",