On the Learning and Learnability of Quasimetrics
Authors: Tongzhou Wang, Phillip Isola
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on random graphs, social graphs, and offline Q-learning demonstrate its effectiveness over many common baselines. Project Page: ssnl.github.io/quasimetric. Code: github.com/Ssn L/poisson_quasimetric_embedding. |
| Researcher Affiliation | Academia | Tongzhou Wang MIT CSAIL Phillip Isola MIT CSAIL |
| Pseudocode | Yes | Algorithm 1 Random quasipartition of a bounded treewidth graph. Algorithm 2 of (Mémoli et al., 2018). |
| Open Source Code | Yes | Code: github.com/Ssn L/poisson_quasimetric_embedding. |
| Open Datasets | Yes | We choose the Berkeley-Stanford Web Graph (Leskovec & Krevl, 2014) as the real-wold social graph for evaluation. This graph consists of 685,230 pages as nodes, and 7,600,595 hyperlinks as directed edges. We use 128-dimensional node2vec features (Grover & Leskovec, 2016) and the landmark method (Rizi et al., 2018) to construct a training set of 2,500,000 pairs, and a test set of 150,000 pairs. |
| Dataset Splits | No | The paper primarily describes train/test splits, such as “randomly sample the training set, and use the rest as the test set” and “form a test set of 150,000”. It does not explicitly mention a separate validation set for hyperparameter tuning or model selection across all experiments. |
| Hardware Specification | Yes | All our experiments run on a single GPU and finish within 3 hours. GPUs we used include NVIDIA 1080, NVIDIA 2080 Ti, NVIDIA 3080 Ti, NVIDIA Titan Xp, NVIDIA Titan RTX, and NVIDIA Titan V. |
| Software Dependencies | No | The paper mentions software like “Sci Py (Virtanen et al., 2020)”, “CDFLIB (Burkardt, 2021; Brown et al., 1994)”, and “PyTorch (Paszke et al., 2019)”. However, it does not explicitly state specific version numbers for these software packages, which is required for full reproducibility. |
| Experiment Setup | Yes | We use 2048 batch size with the Adam optimizer (Kingma & Ba, 2014), with learning rate decaying according to the cosine schedule without restarting (Loshchilov & Hutter, 2016) starting from 10 4 to 0 over 3000 epochs. All models are optimized w.r.t. MSE on the γ-discounted distances, with γ = 0.9. When running with the triangle inequality regularizer, 683 2048/3 triplets are uniformly sampled at each iteration. |