Bilevel Optimization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 143 papers with code (423 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Adaptive Personalized Federated Learning
  30 Mar 2020 11 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.
• OptNet: Differentiable Optimization as a Layer in Neural Networks
  1 Mar 2017 8 repositories listed Syntology ran 0 of 16 samples · 16 unverified
  This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks.
• Nystrom Method for Accurate and Scalable Implicit Differentiation
  20 Feb 2023 4 repositories listed
  The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network parameters.
• Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
  17 Jan 2024 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems.
• Second-Order Sensitivity Analysis for Bilevel Optimization
  4 May 2022 3 repositories listed
  Many existing approaches to bilevel optimization employ first-order sensitivity analysis, based on the implicit function theorem (IFT), for the lower problem to derive a gradient of the lower problem solution with…
• Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
  7 Mar 2019 3 repositories listed
  Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems.
• LDC-MTL: Balancing Multi-Task Learning through Scalable Loss Discrepancy Control
  12 Feb 2025 2 repositories listed
  Our method incorporates three key components: (i) a coarse loss pre-normalization, (ii) a bilevel formulation for fine-grained loss discrepancy control, and (iii) a scalable first-order bilevel algorithm that requires…
• Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium
  21 Oct 2024 2 repositories listed Syntology ran 6 of 20 samples · 14 unverified · 3 pointer-only (licence)
  The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks.
• Embarassingly Simple Dataset Distillation
  13 Nov 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Re-examining the foundational back-propagation through time method, we study the pronounced variance in the gradients, computational burden, and long-term dependencies.
• Self-Supervised Dataset Distillation for Transfer Learning
  10 Oct 2023 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  To achieve this, we also introduce the MSE between representations of the inner model and the self-supervised target model on the original full dataset for outer optimization.
• Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
  22 Jun 2023 2 repositories listed Syntology ran 1 of 6 samples · 5 unverified · 6 pointer-only (licence)
  The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with…
• Single-level Adversarial Data Synthesis based on Neural Tangent Kernels
  8 Apr 2022 2 repositories listed
  In this paper, we propose a new generative model called the generative adversarial NTK (GA-NTK) that has a single-level objective.
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection
  30 Mar 2022 2 repositories listed
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection.
• Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start
  7 Feb 2022 2 repositories listed Syntology ran 8 of 8 samples · 0 unverified
  We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction…
• BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
  17 Aug 2021 2 repositories listed Syntology ran 10 of 10 samples · 0 unverified · 1 pointer-only (licence)
  Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker.
• Bilevel Optimization: Convergence Analysis and Enhanced Design
  15 Oct 2020 2 repositories listed Syntology ran 9 of 18 samples · 9 unverified
  For the AID-based method, we orderwisely improve the previous convergence rate analysis due to a more practical parameter selection as well as a warm start strategy, and for the ITD-based method we establish the first…
• Adversarial Immunization for Certifiable Robustness on Graphs
  19 Jul 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
• Learning Data Augmentation with Online Bilevel Optimization for Image Classification
  25 Jun 2020 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Data augmentation is a key practice in machine learning for improving generalization performance.
• MetaPoison: Practical General-purpose Clean-label Data Poisoning
  1 Apr 2020 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Existing attacks for data poisoning neural networks have relied on hand-crafted heuristics, because solving the poisoning problem directly via bilevel optimization is generally thought of as intractable for deep models.
• Penalty Method for Inversion-Free Deep Bilevel Optimization
  8 Nov 2019 2 repositories listed
  We present results on data denoising, few-shot learning, and training-data poisoning problems in a large-scale setting.
• Truncated Back-propagation for Bilevel Optimization
  25 Oct 2018 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Bilevel optimization has been recently revisited for designing and analyzing algorithms in hyperparameter tuning and meta learning tasks.
• Spectral Inference Networks: Unifying Deep and Spectral Learning
  6 Jun 2018 2 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
• GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
  17 Jun 2025 1 repository listed
  Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs.
• MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
  3 Jun 2025 1 repository listed
  To address this gap, we propose MISLEADER (enseMbles of dIStiLled modEls Against moDel ExtRaction), a novel defense strategy that does not rely on OOD assumptions.
• Learning Where to Learn: Training Distribution Selection for Provable OOD Performance
  27 May 2025 1 repository listed
  Out-of-distribution (OOD) generalization remains a fundamental challenge in machine learning.
• GCAL: Adapting Graph Models to Evolving Domain Shifts
  22 May 2025 1 repository listed Syntology ran 7 of 10 samples · 3 unverified · 10 pointer-only (licence)
  This paper addresses the challenge of graph domain adaptation on evolving, multiple out-of-distribution (OOD) graphs.
• HBO: Hierarchical Balancing Optimization for Fine-Tuning Large Language Models
  18 May 2025 1 repository listed
  We introduce Hierarchical Balancing Optimization (HBO), a novel method that enables LLMs to autonomously adjust data allocation during fine-tuning both across datasets (globally) and within each individual dataset…
• Optimizing Decomposition for Optimal Claim Verification
  19 Mar 2025 1 repository listed
  Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlooking their interactions and potential…
• PRDP: Progressively Refined Differentiable Physics
  26 Feb 2025 1 repository listed Syntology ran 8 of 9 samples · 1 unverified
  The physics solvers employed for neural network training are primarily iterative, and hence, differentiating through them introduces a severe computational burden as iterations grow large.
• Adversarial Training for Defense Against Label Poisoning Attacks
  24 Feb 2025 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  These results underscore the potential of FLORAL to enhance the resilience of machine learning models against label poisoning threats, thereby ensuring robust classification in adversarial settings.

Syntology lines on 18 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.