README.md
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1 ---
2 license: apache-2.0
3 pipeline_tag: tabular-regression
4 ---
5
6 # Mitra Regressor
7
8 Mitra regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors.
9
10 ## Architecture
11
12 Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm.
13
14 ## Usage
15
16 To use Mitra regressor, install AutoGluon by running:
17
18 ```sh
19 pip install uv
20 uv pip install autogluon.tabular[mitra]
21 ```
22
23 A minimal example showing how to perform inference using the Mitra regressor:
24
25 ```python
26 import pandas as pd
27 from autogluon.tabular import TabularDataset, TabularPredictor
28 from sklearn.model_selection import train_test_split
29 from sklearn.datasets import fetch_california_housing
30
31 # Load datasets
32 housing_data = fetch_california_housing()
33 housing_df = pd.DataFrame(housing_data.data, columns=housing_data.feature_names)
34 housing_df['target'] = housing_data.target
35
36 print("Dataset shapes:")
37 print(f"California Housing: {housing_df.shape}")
38
39 # Create train/test splits (80/20)
40 housing_train, housing_test = train_test_split(housing_df, test_size=0.2, random_state=42)
41
42 print("Training set sizes:")
43 print(f"Housing: {len(housing_train)} samples")
44
45 # Convert to TabularDataset
46 housing_train_data = TabularDataset(housing_train)
47 housing_test_data = TabularDataset(housing_test)
48
49 # Create predictor with Mitra for regression
50 print("Training Mitra regressor on California Housing dataset...")
51 mitra_reg_predictor = TabularPredictor(
52 label='target',
53 path='./mitra_regressor_model',
54 problem_type='regression'
55 )
56 mitra_reg_predictor.fit(
57 housing_train_data.sample(1000), # sample 1000 rows
58 hyperparameters={
59 'MITRA': {'fine_tune': False}
60 },
61 )
62
63 # Evaluate regression performance
64 mitra_reg_predictor.leaderboard(housing_test_data)
65 ```
66
67 ## License
68
69 This project is licensed under the Apache-2.0 License.
70
71 ## Reference
72
73 ```
74 @article{zhang2025mitra,
75 title={Mitra: Mixed synthetic priors for enhancing tabular foundation models},
76 author={Zhang, Xiyuan and Maddix, Danielle C and Yin, Junming and Erickson, Nick and Ansari, Abdul Fatir and Han, Boran and Zhang, Shuai and Akoglu, Leman and Faloutsos, Christos and Mahoney, Michael W and others},
77 journal={arXiv preprint arXiv:2510.21204},
78 year={2025}
79 }
80 ```
81
82 Amazon Science blog: [Mitra: Mixed synthetic priors for enhancing tabular foundation models](https://www.amazon.science/blog/mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models?utm_campaign=mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models&utm_medium=organic-asw&utm_source=linkedin&utm_content=2025-7-22-mitra-mixed-synthetic-priors-for-enhancing-tabular-foundation-models&utm_term=2025-july)