Multi-Task Learning papers, 2023–2026

Key findings

• Multi-Task Learning went from 288 papers in 2023 to 402 in 2026, roughly 1.4x in three years.
• NeurIPS publishes the most Multi-Task Learning work: 399 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 6 in 2023 to 16 in 2026.
• Among the 5 topics under Meta-Learning and Transfer, it ranks #2 by 2026 paper count; the fastest grower in the group, Foundation Model Adaptation, is up 3.8x versus 1.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Mahalanobis Margin \texorpdfstring{$\gamma_{\min}$}{gamma\_min} Bound on Task Confusion in Pretrained Class-Incremental Learning: From Infeasibility to Exponential Attenuation
• ICML 2026A Systematic Study of Behavioral Cloning for Scientific Data Annotation
• ICLR 2026Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?
• CVPR 20263D-Aware Multi-Task Learning with Cross-View Correlations for Dense Scene Understanding
• NeurIPS 2025A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning
• ICML 2025ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
• ICLR 2025CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMs
• CVPR 2025ACAttack: Adaptive Cross Attacking RGB-T Tracker via Multi-Modal Response Decoupling
• NeurIPS 2024CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
• ICML 2024Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
• ICLR 2024Class Incremental Learning via Likelihood Ratio Based Task Prediction
• CVPR 2024Differentiable Information Bottleneck for Deterministic Multi-view Clustering
• NeurIPS 2023A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
• ICML 2023A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
• ICLR 2023An Extensible Multi-modal Multi-task Object Dataset with Materials
• CVPR 2023AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
• NeurIPS 2026A Scalable Multi-Task Model for Virtual Sensors
• ICML 2026Alleviating Observation Bias via Causal-Invariant Meta-Learning for Unbalanced Incomplete Multi-view Clustering
• ICLR 2026Beyond Hearing: Learning Task-agnostic ExG Representations from Earphones via Physiology-informed Tokenization
• CVPR 2026Any Resolution Any Geometry From Multi-View To Multi-Patch

All Multi-Task Learning papers

61 papers, page 1 of 2

1. NeurIPS 2024$C^2M^3$: Cycle-Consistent Multi-Model Merging
2. NeurIPS 2024$SE(3)$ Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
3. NeurIPS 2024A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning
4. NeurIPS 2024A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm
5. NeurIPS 2024Adversarially Robust Multi-task Representation Learning
6. NeurIPS 2024AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
7. NeurIPS 2024Autonomous Agents for Collaborative Task under Information Asymmetry
8. NeurIPS 2024BAKU: An Efficient Transformer for Multi-Task Policy Learning
9. NeurIPS 2024Beyond task diversity: provable representation transfer for sequential multitask linear bandits
10. NeurIPS 2024CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding
11. NeurIPS 2024CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
12. NeurIPS 2024Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
13. NeurIPS 2024DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning
14. NeurIPS 2024Efficiency for Free: Ideal Data Are Transportable Representations
15. NeurIPS 2024Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters
16. NeurIPS 2024Efficient Multi-task Reinforcement Learning with Cross-Task Policy Guidance
17. NeurIPS 2024EMR-Merging: Tuning-Free High-Performance Model Merging
18. NeurIPS 2024Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering
19. NeurIPS 2024Exploring the Precise Dynamics of Single-Layer GAN Models: Leveraging Multi-Feature Discriminators for High-Dimensional Subspace Learning
20. NeurIPS 2024Extending Multi-modal Contrastive Representations
21. NeurIPS 2024Flexible task abstractions emerge in linear networks with fast and bounded units
22. NeurIPS 2024GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance
23. NeurIPS 2024Generative Retrieval Meets Multi-Graded Relevance
24. NeurIPS 2024Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
25. NeurIPS 2024GO4Align: Group Optimization for Multi-Task Alignment
26. NeurIPS 2024Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning
27. NeurIPS 2024GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
28. NeurIPS 2024Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures
29. NeurIPS 2024Improved Bayes Regret Bounds for Multi-Task Hierarchical Bayesian Bandit Algorithms
30. NeurIPS 2024Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection
31. NeurIPS 2024In-Context Learning with Representations: Contextual Generalization of Trained Transformers
32. NeurIPS 2024Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
33. NeurIPS 2024Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline
34. NeurIPS 2024Instruction Embedding: Latent Representations of Instructions Towards Task Identification
35. NeurIPS 2024Kuro Siwo: 33 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping
36. NeurIPS 2024MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset
37. NeurIPS 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
38. NeurIPS 2024Multi-language Diversity Benefits Autoformalization
39. NeurIPS 2024Multi-Scale Representation Learning for Protein Fitness Prediction
40. NeurIPS 2024NanoBaseLib: A Multi-Task Benchmark Dataset for Nanopore Sequencing
41. NeurIPS 2024Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
42. NeurIPS 2024Neural Persistence Dynamics
43. NeurIPS 2024Pre-trained Large Language Models Use Fourier Features to Compute Addition
44. NeurIPS 2024Propensity Score Alignment of Unpaired Multimodal Data
45. NeurIPS 2024Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
46. NeurIPS 2024Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RL
47. NeurIPS 2024Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
48. NeurIPS 2024SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark
49. NeurIPS 2024SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding
50. NeurIPS 2024SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation

FAQ

How many Multi-Task Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,181 in total: 288 in 2023, 227 in 2024, 264 in 2025, 402 in 2026.

How many Multi-Task Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 126 Multi-Task Learning papers, versus 101 the year before.

How many Multi-Task Learning papers are at ICLR 2026?

ICLR 2026 has 57 Multi-Task Learning papers, versus 40 the year before.

How many Multi-Task Learning papers are at ICML 2026?

ICML 2026 has 91 Multi-Task Learning papers, versus 47 the year before.

How many Multi-Task Learning papers are at CVPR 2026?

CVPR 2026 has 128 Multi-Task Learning papers, versus 76 the year before.

Which conference publishes the most Multi-Task Learning papers?

NeurIPS, with 399 papers from 2023 to 2026.