Deep learning · optimization papers, 2023–2026

Deep learning area: optimization

Scope: NeurIPS, ICLR, ICML and CVPR, 2023–2026. 953 papers are tagged with this topic.

Key findings

Papers per year

Papers per venue

Counts by year and venue

Deep learning · optimization papers by year and venue
YearNeurIPSICLRICMLCVPRTotal
202314434708256
202419130721294
20256251446163
202610344867240

Representative papers

Spread across years and venues so every edition shows up

All Deep learning · optimization papers

500 papers, page 2 of 10

  1. NeurIPS 2026Locally-Additive Regret for Delayed Non-stationary Bandit Convex Optimization
  2. NeurIPS 2026LOTION: Smoothing the Optimization Landscape for Quantized Training
  3. NeurIPS 2026Masked Sobolev Training for Feasibility-Reliable Optimization Proxies
  4. NeurIPS 2026Median-of-Means under Structured Heavy-Tailed Noise: High-Probability Bounds for Clipped Stochastic Optimization
  5. NeurIPS 2026Minimax Optimization without Spurious Solutions in Optimal Transport Learning
  6. NeurIPS 2026Mirror Descent-Ascent for mean-field min-max problems
  7. NeurIPS 2026Nerve-Skeleton Message Passing for Federated Optimization with Overlapping Parameters
  8. NeurIPS 2026Never Go Full Batch: Stochastic TMLE for Large-Scale Debiased Inference
  9. NeurIPS 2026Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise
  10. NeurIPS 2026Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders
  11. NeurIPS 2026On the Last-Iterate Convergence of Clipped Gradient Methods
  12. NeurIPS 2026Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback
  13. NeurIPS 2026Online Min-Max Optimization: From Individual Regrets to Cumulative Saddle Points
  14. NeurIPS 2026Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms
  15. NeurIPS 2026Orthogonal Updates for the Win: Towards Accelerated Adaptive Minimax Optimization
  16. NeurIPS 2026PACE: Partial-state Amortized Constraint Editing for Neural Combinatorial Optimization
  17. NeurIPS 2026Pairwise AUC Optimization Needs Corrective Power: A Unified View
  18. NeurIPS 2026ParetoM$^3$: Learning on the Pareto Set under Preference Guidance via Min-Max-Min Optimization
  19. NeurIPS 2026Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs
  20. NeurIPS 2026POME: Post Optimization Model Edit via Muon-Style Projection
  21. NeurIPS 2026Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise
  22. NeurIPS 2026Randomized Kriging Believer For Parallel Bayesian Optimization With Regret Bounds
  23. NeurIPS 2026Regret-Based $(\epsilon,\delta)$-optimal Stopping Criteria for Bayesian Optimization
  24. NeurIPS 2026Regularization Paths for Continuous DAG Learning
  25. NeurIPS 2026Regularized Large Neighborhood Search
  26. NeurIPS 2026Rennala-NSGD: Asynchronous Stochastic Optimization Beyond Euclidean Geometry
  27. NeurIPS 2026Rethinking Language Model Scaling under Transferable Hypersphere Optimization
  28. NeurIPS 2026Revisiting Decentralized Online Convex Optimization with Compressed Communication
  29. NeurIPS 2026Riemannian Bilevel Optimization under the Polyak–Łojasiewicz Condition
  30. NeurIPS 2026Riemannian Lyapunov Framework: Optimization as Closed-Loop Control on Riemannian Manifolds
  31. NeurIPS 2026Robust Latent Space Bayesian Optimization with Marginalized Kernel
  32. NeurIPS 2026Route-Consistent Adaptation for Stable Quantization of Mixture-of-Experts Models with Theoretical Guarantees
  33. NeurIPS 2026Sample complexity of stochastic optimization with integer variables
  34. NeurIPS 2026SAS: Simple Attention Sparsification via End-to-End Optimization of Context Ranking
  35. NeurIPS 2026Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
  36. NeurIPS 2026Sharper Guarantees for Misspecified Kernelized Bandit Optimization
  37. NeurIPS 2026Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise
  38. NeurIPS 2026Skip the Hessian, Keep the Rates: Globalized Semismooth Newton with Lazy Hessian Updates
  39. NeurIPS 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
  40. NeurIPS 2026Spectral Annealing: Normalization-Path Exploration for Large-Scale Ising Optimization
  41. NeurIPS 2026Stochastic Approximation Approach for Decentralized Optimization on Time Varying Random Networks
  42. NeurIPS 2026Stochastic Dynamic Barrier Perturbed Gradient Methods for Nonconvex Simple Bilevel Optimization
  43. NeurIPS 2026Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States
  44. NeurIPS 2026Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise
  45. NeurIPS 2026Structure vs. Chaos: Asymmetric Entropic Optimization for Enforcing Instruction Hierarchy
  46. NeurIPS 2026Taming the Tails: Why Distributionally Robust Optimization Needs New Theory for Imbalanced Regression
  47. NeurIPS 2026The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning
  48. NeurIPS 2026The Optimization Prior: Instilling depth for shallow networks, detail for coarse networks
  49. NeurIPS 2026Towards Hyperparameter Transfer for Differentially Private Optimization
  50. NeurIPS 2026Towards Settling the Complexity of Non-Euclidean Parallel Convex Optimization

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FAQ

How many Deep learning · optimization papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?
953 in total: 256 in 2023, 294 in 2024, 163 in 2025, 240 in 2026.
How many Deep learning · optimization papers are at NeurIPS 2026?
NeurIPS 2026 has 103 Deep learning · optimization papers, versus 62 the year before.
How many Deep learning · optimization papers are at ICLR 2026?
ICLR 2026 has 44 Deep learning · optimization papers, versus 51 the year before.
How many Deep learning · optimization papers are at ICML 2026?
ICML 2026 has 86 Deep learning · optimization papers, versus 44 the year before.
How many Deep learning · optimization papers are at CVPR 2026?
CVPR 2026 has 7 Deep learning · optimization papers, versus 6 the year before.
Which conference publishes the most Deep learning · optimization papers?
NeurIPS, with 500 papers from 2023 to 2026.

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