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..
Learning the Pareto Front with Hypernetworks
Authors: Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a wide set of problems, from multi-task regression and classification to fairness. PHNs learn the entire Pareto front at roughly the same time as learning a single point on the front and at the same time reach a better solution set. |
| Researcher Affiliation | Collaboration | Aviv Navon Bar-Ilan University, Israel EMAIL Aviv Shamsian Bar-Ilan University, Israel EMAIL Ethan Fetaya Bar-Ilan University, Israel EMAIL Gal Chechik Bar-Ilan University, Israel NVIDIA, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 PHN while not converged do r Dir(α) θ(φ, r) = h(r; φ) Sample mini-batch (x1, y1), .., (x B, y B) if LS then i,j ri φℓi(xj, yj, θ(φ, r)) if EPO then β = EPO(θ(φ, r), ℓ, r) gφ 1 B P i,j βi φℓi(xj, yj, θ(φ, r)) φ φ ηgφ return φ |
| Open Source Code | Yes | We make our source code publicly available at: https://github.com/AvivNavon/pareto-hypernetworks. |
| Open Datasets | Yes | Multi-MNIST (Sabour et al., 2017); (2) Multi-Fashion, and (3) Multi-Fashion + MNIST. In each dataset, two instances are sampled uniformly at random from the MNIST (Le Cun et al., 1998) or Fashion-MNIST (Xiao et al., 2017) datasets. ... Adult (Dua & Graff, 2017), Default (Yeh & Lien, 2009) and Bank (Moro et al., 2014). ... NYUv2 dataset (Silberman et al., 2012). ... SARCOS dataset (Vijayakumar), a commonly used dataset for multitask regression (Zhang & Yang, 2017). |
| Dataset Splits | Yes | We allocate 10% of each training set for constructing validation sets. ... Each dataset is divided into train/validation/test sets of sizes 70%/10%/20% respectively. ... We use 40,036 training examples, 4,448 validation examples, and 4,449 test examples. |
| Hardware Specification | Yes | Run-time (min., Tesla V100) |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2015) optimizer' but does not specify version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For our hypernetwork, we use an MLP with 2 hidden layers and linear heads. We set the hidden dimension to 100 for the Multi-MNIST and NYU experiment, and 25 for the Fairness and SARCOS datasets. The Dirichlet parameter α in Alg. 1 is set to 0.2 for all experiments... We train all methods using an Adam optimizer with learning rate 1e-4 for 150 epochs and batch size 256. |