Learning conditional distributions on continuous spaces
Authors: Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical findings demonstrate that, with a suitably designed structure, the neural network has the ability to adapt to a suitable level of Lipschitz continuity locally. For reproducibility, our code is available at https://github.com/zcheng-a/LCD_kNN. ...In Section 3.2, we evaluate the performance of the trained P θ, denoted by P Θ, using three sets of synthetic data in 1D and 3D spaces. |
| Researcher Affiliation | Academia | Cyril B en ezet EMAIL Universit e Paris-Saclay, CNRS, Univ Evry, ens IIE Laboratoire de Math ematiques et Mod elisation d Evry, 91037, Evry-Courcouronnes, France Ziteng Cheng EMAIL Financial Technology Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, 511400, China Sebastian Jaimungal EMAIL Department of Statistical Sciences University of Toronto Toronto, ON M5G 1Z5, Canada |
| Pseudocode | Yes | Algorithm 1 Deep learning conditional distribution in conjunction with k-NN estimator ...Algorithm 2 Power iteration with momentum for updating W 2 estimate, applied to all convex potential layers simultaneously at every epoch during training |
| Open Source Code | Yes | For reproducibility, our code is available at https://github.com/zcheng-a/LCD_kNN. |
| Open Datasets | No | We consider data simulated from three different models. ...We generate 10^4 samples for Models 1 and 2. ...For Model 3, we generate 10^6 samples and select k = 300. |
| Dataset Splits | No | The paper generates data from three different models and does not explicitly define traditional training/test/validation splits on a fixed dataset. Instead, it mentions generating data and using randomly selected query points for training objectives, as seen in Algorithm 1: 'generate a query point Xn Uniform(X)'. |
| Hardware Specification | Yes | Table 1: This table compares the execution times for 500 runs of exact NNS versus ANNS-RBSP, both utilizing parallel computing, facilitated by Py Torch, with an NVIDIA L40 GPU. ...Table 5: All times were obtained from a machine equipped with an Nvidia L40 GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" in Table 1 and "Adam optimizer Kingma and Ba (2017)" in Section 3.2 and Table 4. However, it does not provide specific version numbers for PyTorch, Adam, or any other software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | Table 4 Hyper-parameters Hyper-parameters Configuration Note Sample size 1e4 for Model 1 & 2, 1e6 for Model 3 k 100 for Model 1 & 2, 300 for Model 3 See Definition 9 Network stucture Std Net: Layer-wise residual connection He et al. (2016), batch normalization (Ioffe and Szegedy (2015)) after affine transformation ...Number of episodes 5e3 for Model 1 & 2, 1e4 for Model 3 |