Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Shadow Cones: A Generalized Framework for Partial Order Embeddings
Authors: Tao Yu, Toni J.B. Liu, Albert Tseng, Christopher De Sa
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on datasets of various sizes and hierarchical structures show that shadow cones consistently and significantly outperform existing entailment cone constructions. and Achieve state-of-the-art results on a wide range of graph embedding tasks, lending both empirical and theoretical support to the advantages of all four embedding schemes. and 5 EXPERIMENTS This section showcases shadow cones ability to represent and infer hierarchical relations on four datasets (detailed statistics in Appendix G): MCG (Wang et al., 2015; Wu et al., 2012; Li et al., 2017), Hearst patterns (Hearst, 1992; Le et al., 2019), Word Net Noun (Christiane, 1998), and its Mammal sub-graph. |
| Researcher Affiliation | Academia | Tao Yu , Toni J.B. Liu , Albert Tseng, and Christopher De Sa Cornell University EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available on github https://github.com/ydtydr/Shadow Cones |
| Open Datasets | Yes | This section showcases shadow cones ability to represent and infer hierarchical relations on four datasets (detailed statistics in Appendix G): MCG (Wang et al., 2015; Wu et al., 2012; Li et al., 2017), Hearst patterns (Hearst, 1992; Le et al., 2019), Word Net Noun (Christiane, 1998), and its Mammal sub-graph. |
| Dataset Splits | Yes | The remaining 10% of non-basic edges are evenly divided between the validation and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments. |
| Software Dependencies | No | The paper mentions using "HTorch (Yu et al., 2023) for optimization in various models of hyperbolic space. We use Riemannian SGD for Poincaré half-space model, and Riemannian Adam for Poincaré ball model". However, no specific version numbers are provided for HTorch or the optimizers, which are necessary for reproducibility. |
| Experiment Setup | Yes | For the margin parameters in shadow loss, we use γ2 = 0 consistently for all experiments. We tune γ1 and the learning rate in {0.01, 0.001, 0.0001}. For umbral cones, we tune the source radius r in {0.01, 0.05, 0.1, 0.2, 0.3}, empirically r = 0.05 during training gives the optimal performance when evaluated under a slightly larger radius r = 0.1. For penumbral-half-space cones, we tune the exponentiated height kh in {2, 5, 10, 20}... We set γ3 = 0.0001 consistently for all shadow cones. We use Riemannian SGD for Poincaré half-space model, and Riemannian Adam for Poincaré ball model. and A training batchsize of 16 is used for all datasets and models. |