Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- anomaly-detection
+- tabular-data
+- in-context-learning
+- one-class-classification
+- unsupervised-learning
+- semi-supervised-learning
+---
+
+# ICLAD Pretrained Checkpoints
+
+**ICLAD: In-Context Learning for Unified Tabular Anomaly Detection**
+
+This repository contains pretrained model checkpoints for ICLAD, an in-context learning framework for tabular anomaly detection that supports one-class, unsupervised, and semi-supervised settings.
+
+📄 **Paper:** [ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes](https://arxiv.org/abs/2603.19497)
+
+## Model Variants
+
+### 1. [ICLAD]  `iclad_mixedprior_unified.pth` (Default)
+
+General use across all anomaly detection scenarios
+
+- Trained on **mixed prior** (structural causal models (SCMs) and perturbation noises)
+- Supports all three settings: **one-class**, **unsupervised**, and **semi-supervised**
+- **Recommended as the default choice** for most use cases
+
+---
+
+### 2. [ICLAD_OC]  `iclad_mixedprior_oneclass.pth`
+
+Used for ablation studies.
+
+- Trained on **mixed prior** optimized for **one-class setting only**
+- **Note:** Use `iclad_mixedprior_unified.pth` for general one-class applications
+
+---
+
+### 3. [ICLAD_UNSUP]  `iclad_mixedprior_unsup.pth`
+
+Used for ablation studies.
+
+- Trained on **mixed prior** optimized for **unsupervised setting only**
+- **Note:** Use `iclad_mixedprior_unified.pth` for general unsupervised applications
+
+---
+
+### 4. [ICLAD_SCM]  `iclad_scm_unified.pth`
+
+Used for ablation studies.
+
+- Trained on **SCM-only prior** (no perturbation-based noise)
+- Supports all three settings: **one-class**, **unsupervised**, and **semi-supervised**
+- **Note:** Use `iclad_mixedprior_unified.pth` for general applications
+
+---
+
+## Usage
+
+### ⭐ Recommended: Use the Unified Model
+
+**For most applications, use `iclad_mixedprior_unified.pth`** (the default). The other variants are provided for research and paper reproduction purposes.
+
+### Option 1: Load from Hugging Face Hub
+
+```python
+from iclad import ICLAD
+
+# Load default unified model (works for all settings)
+model = ICLAD.from_checkpoint("jyiwei/iclad-checkpoints/iclad_mixedprior_unified.pth")
+```
+
+### Option 2: Load from Local Checkpoints
+
+If you have the checkpoint files in `src/iclad/checkpoints/`, use the built-in names:
+
+```python
+from iclad import ICLAD
+
+# ⭐ RECOMMENDED: Load default unified model
+model = ICLAD()  # Uses iclad_mixedprior_unified by default
+model = ICLAD(model_name="ICLAD")
+
+# --- Paper Reproduction Only (below) ---
+
+# Load one-class variant
+model = ICLAD(model_name="ICLAD_OC")
+
+# Load unsupervised variant
+model = ICLAD(model_name="ICLAD_UNSUP")
+
+# Load SCM-only variant
+model = ICLAD(model_name="ICLAD_SCM")
+```
+
+### Example: Anomaly Detection on Tabular Data
+
+```python
+import numpy as np
+from iclad import ICLAD
+
+# Initialize model
+model = ICLAD(model_name="ICLAD")  # Default unified model
+
+# Prepare training and test data
+X_train = np.random.randn(100, 10)  # 100 samples, 10 features
+X_test = np.random.randn(50, 10)    # Test data
+
+# Fit on training data (for unsupervised setting, no labels needed)
+model.fit(X_train)
+
+# Get anomaly scores
+scores = model.predict_score(X_test)
+```
+
+### One-class Example
+
+```python
+# Prepare training and test data
+X_train = np.random.randn(100, 10)  # 100 samples, 10 features
+Y_train = np.zeros(X_train.shape[0]) # All normal
+X_test = np.random.randn(50, 10)    # Test data
+
+# Fit on training data (for unsupervised setting, no labels needed)
+model.fit(X_train, Y_train)
+
+# Get anomaly scores
+scores = model.predict_score(X_test)
+```
+
+### Semi-Supervised Example
+
+```python
+# y_train: 1 for anomaly, -1 for unknown (no support for known normals yet)
+model.fit(X_train, Y_train)
+scores = model.predict_score(X_test)
+```
+
+## Citation
+
+If you use these pretrained models in your research, please cite:
+
+```bibtex
+@misc{wei2026icladincontextlearningunified,
+  title={ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes},
+  author={Jack Yi Wei and Narges Armanfard},
+  year={2026},
+  eprint={2603.19497},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2603.19497},
+}
+```
+
+## License
+
+The model code is licensed under the **Apache License 2.0**.
+
+## Repository
+
+For issues, discussions, and more information, please visit the main ICLAD repository.
+
+## Contact
+
+Jack Wei: yi.wei4@mail.mcgill.ca