Deep learning · optimization papers, 2023–2026

Key findings

• Deep learning · optimization fell from 256 papers in 2023 to 240 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · optimization work: 500 papers from 2023 to 2026, 52% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 5 in 2023 to 23 in 2026.
• Among the 11 topics under Deep Learning, it ranks #6 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Achieving Directional-Stationarity from a Single Random Direction Step
• ICML 2026Bottleneck Communication Delay Minimization for Communication-Efficient Decentralized Learning
• ICLR 2026From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces
• CVPR 2026Coverage Optimization for Camera View Selection
• NeurIPS 2025High-Performance Arithmetic Circuit Optimization via Differentiable Architecture Search
• ICML 2025Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
• ICLR 2025Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems
• CVPR 2025HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition
• NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
• ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
• ICLR 2024Bayesian Coreset Optimization for Personalized Federated Learning
• CVPR 2024Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
• NeurIPS 2023$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
• ICML 2023Buying Information for Stochastic Optimization
• ICLR 2023Alternating Differentiation for Optimization Layers
• CVPR 2023Visual-Language Prompt Tuning With Knowledge-Guided Context Optimization
• NeurIPS 2026Adaptive Conditional Gradient Sliding: Projection-Free and Line-Search-Free Acceleration
• ICML 2026Riemannian Optimization for Fair Spectral Clustering
• ICLR 2026Gradient-Based Diversity Optimization with Differentiable Top-$k$ Objective
• CVPR 2026Its Never Too Late Noise Optimization for Collapse Recovery in

All Deep learning · optimization papers

953 papers, page 7 of 20

301. ICML 2025Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
302. ICML 2025Attention-Only Transformers via Unrolled Subspace Denoising
303. ICML 2025BackSlash: Rate Constrained Optimized Training of Large Language Models
304. ICML 2025Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
305. ICML 2025BSO: Binary Spiking Online Optimization Algorithm
306. ICML 2025Decoupled SGDA for Games with Intermittent Strategy Communication
307. ICML 2025DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
308. ICML 2025Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
309. ICML 2025Enabling Optimal Decisions in Rehearsal Learning under CARE Condition
310. ICML 2025Enhancing Parallelism in Decentralized Stochastic Convex Optimization
311. ICML 2025Ensemble Learned Bloom Filters: Two Oracles are Better than One
312. ICML 2025Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
313. ICML 2025Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions
314. ICML 2025FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
315. ICML 2025Generalized additive models via direct optimization of regularized decision stump forests
316. ICML 2025Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level
317. ICML 2025Global curvature for second-order optimization of neural networks
318. ICML 2025Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
319. ICML 2025Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems
320. ICML 2025Inverse Optimization via Learning Feasible Regions
321. ICML 2025Joker: Joint Optimization Framework for Lightweight Kernel Machines
322. ICML 2025Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms
323. ICML 2025Meta-Black-Box-Optimization through Offline Q-function Learning
324. ICML 2025Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization
325. ICML 2025Neural Solver Selection for Combinatorial Optimization
326. ICML 2025Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness
327. ICML 2025Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization
328. ICML 2025Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
329. ICML 2025PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
330. ICML 2025Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning
331. ICML 2025Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent
332. ICML 2025Radio: Rate–Distortion Optimization for Large Language Model Compression
333. ICML 2025Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
334. ICML 2025Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
335. ICML 2025Scalable Meta-Learning via Mixed-Mode Differentiation
336. ICML 2025SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization
337. ICML 2025Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization
338. ICML 2025Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
339. ICML 2025Subspace Optimization for Large Language Models with Convergence Guarantees
340. ICML 2025The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
341. ICML 2025TSP: A Two-Sided Smoothed Primal-Dual Method for Nonconvex Bilevel Optimization
342. NeurIPS 2025A Beyond-Worst-Case Analysis of Greedy k-means++
343. NeurIPS 2025A Near-Optimal Algorithm for Decentralized Convex-Concave Finite-Sum Minimax Optimization
344. NeurIPS 2025A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models
345. NeurIPS 2025An Ellipsoid Algorithm for Online Convex Optimization
346. NeurIPS 2025Auto-Connect: Connectivity-Preserving RigFormer with Direct Preference Optimization
347. NeurIPS 2025Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
348. NeurIPS 2025Beyond Value Functions: Single-Loop Bilevel Optimization under Flatness Conditions
349. NeurIPS 2025Boosting Resilience of Large Language Models through Causality-Driven Robust Optimization
350. NeurIPS 2025Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining

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.