What framework is used to implement "Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning"?

The primary implementation uses PyTorch Adam optimizer docs.

Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning

Q: How reproducible is "Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning"?

Estimated time to first reproduction: a few hours. Risk flags: No maintained paper-verified implementation is currently available. This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.

Charles Dickens, Changyu Gao, Connor Pryor, Stephen Wright, Lise Getoor

Published: Jan 17, 2024

No direct implementation yet

Evidence: Inferred

Domain fit: Niche / domain-specific

Verified repos: 0

No strong AI-core implementation/artifact signals were detected from current providers.

Framework: PyTorch Adam optimizer docs

Time to first repro: a few hours

1 risk flag

DOI Publisher

We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems. We demonstrate our framework with NeuPSL, a state-of-the-art NeSy architecture. To achieve this, we propose a smooth primal and dual formulation of NeuPSL inference and show learning gradients are functions of the optimal dual variables. Additionally, we develop a ...

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dual block coordinate descent algorithm for the new formulation that naturally exploits warm-starts. This leads to over 100x learning runtime improvements over the current best NeuPSL inference method. Finally, we provide extensive empirical evaluations across 8 datasets covering a range of tasks and demonstrate our learning framework achieves up to a 16% point prediction performance improvement over alternative learning methods.

Technical details

Canonical key: doi-10.48550_arxiv.2401.09651

Cache status: Stale (SWR served)

Generated at: Jun 18, 2026, 10:40 PM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: missing

Time to repro: a few hours

1 risk flag

PyTorch Adam optimizer docs

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems.

Implementation Evidence Summary

Confidence: low

This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.

Reproduction Risks

No maintained paper-verified implementation is currently available

Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 60/100, grounding 58/100, status medium.

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.
Start with framework-native implementations (e.g. PyTorch optimizer module, Optax, or Transformers training loops).
Replicate the paper ablation settings first, then compare against modern baselines.

Time to first repro: a few hours

Reproduction readiness

No Repo

Time to first repro: hours

Last checked: Jun 18, 2026

No verified implementation available

· No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.

Framework baselines

PyTorch Adam optimizer docs
Reference implementation of Adam in PyTorch.
Optax Adam optimizer docs
JAX/Flax baseline for Adam variants.
Keras Adam optimizer docs
TensorFlow/Keras baseline for Adam.

Hugging Face artifacts

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Continue with targeted Hugging Face searches derived from the paper title and method context:

Models

Neuro-Symbolic Bilevel optimization Bilevel optimization model

Datasets

Neuro-Symbolic dataset Inference dataset Bilevel optimization benchmark

Spaces

Neuro-Symbolic demo Inference demo Bilevel optimization gradio

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Research context

Citations

References

Tasks

Inference, Computer science, Exploit, Range (aeronautics), Regular polygon, Dual (grammatical number), Gradient descent, Deep learning

Methods

Bilevel optimization, Convex optimization, Mathematical optimization, Optimization problem, Algorithm

Domains

Artificial intelligence, Machine learning

Evaluation & Human Feedback Data

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Inference Computer science Exploit Range (aeronautics) Regular polygon Dual (grammatical number)