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 [1].
Learning with Fenchel-Young losses
Authors: Mathieu Blondel, André F.T. Martins, Vlad Niculae
JMLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 9. Experiments In this section, we demonstrate one of the key features of Fenchel-Young losses: their ability to induce sparse probability distributions. We focus on two tasks: label proportion estimation ( 9.1) and dependency parsing ( 9.2). [...] 9.1. Label proportion estimation experiments [...] Table 3: Dataset statistics [...] Table 4: Test-set performance of Tsallis losses [...] Figure 10: Jensen-Shannon divergence between predicted and true label proportions, when varying document length, of various losses generated by a Tsallis entropy. |
| Researcher Affiliation | Collaboration | Mathieu Blondel EMAIL NTT Communication Science Laboratories Kyoto, Japan Andre F. T. Martins EMAIL Unbabel & Instituto de Telecomunicações Lisbon, Portugal Vlad Niculae EMAIL Instituto de Telecomunicações Lisbon, Portugal |
| Pseudocode | Yes | Algorithm 1 Bisection for byΩ(θ) = Ω (θ) Input: θ Rd, Ω(p) = I d + P i g(pi) p(τ) := (g ) 1(max{θ τ, g (0)}) φ(τ) := p(τ), 1 1 τmin max(θ) g (1); τmax max(θ) g (1/d) τ (τmin + τmax)/2 while |φ(τ)| > ϵ if φ(τ) < 0 τmax τ else τmin τ τ (τmin + τmax)/2 Output: byΩ(θ) p(τ) |
| Open Source Code | No | The text is ambiguous or lacks a clear, affirmative statement of release. No specific link to a code repository or explicit statement about the release of their implementation code was found. |
| Open Datasets | Yes | We ran experiments on 7 standard multi-label benchmark datasets see Table 3 for dataset characteristics1. 1. The datasets can be downloaded from http://mulan.sourceforge.net/datasets-mlc.html and https: //www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | Yes | We use 1200 samples as training set, 200 samples as validation set and 1000 samples as test set. [...] Table 3: Dataset statistics Dataset Type Train Dev Test Features Classes Avg. labels Birds Audio 134 45 172 [...] Yeast Micro-array 1,125 375 917 |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found in the paper. |
| Software Dependencies | No | No specific software versions (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) for key components were provided. The paper mentions algorithms like L-BFGS and Adam, but not the specific software implementations or their versions. |
| Experiment Setup | Yes | We chose λ {10 4, 10 3, . . . , 104} and α {1, 1.1, . . . , 2} against the validation set. [...] Parameters are trained using Adam (Kingma and Ba, 2015), tuning the learning rate on the grid {.5, 1, 2, 4, 8} 10 3, expanded by a factor of 2 if the best model is at either end. |