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

111 papers, page 2 of 3

51. NeurIPS 2023Learning to Modulate pre-trained Models in RL
52. NeurIPS 2023Learning to Taste: A Multimodal Wine Dataset
53. NeurIPS 2023Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
54. NeurIPS 2023Leveraging sparse and shared feature activations for disentangled representation learning
55. NeurIPS 2023Locality-Aware Generalizable Implicit Neural Representation
56. NeurIPS 2023Loss Decoupling for Task-Agnostic Continual Learning
57. NeurIPS 2023M5HisDoc: A Large-scale Multi-style Chinese Historical Document Analysis Benchmark
58. NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
59. NeurIPS 2023Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
60. NeurIPS 2023Meta-Learning Adversarial Bandit Algorithms
61. NeurIPS 2023Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
62. NeurIPS 2023MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing
63. NeurIPS 2023Multi-Agent Learning with Heterogeneous Linear Contextual Bandits
64. NeurIPS 2023Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
65. NeurIPS 2023Multi-Fidelity Multi-Armed Bandits Revisited
66. NeurIPS 2023Multi-Head Adapter Routing for Cross-Task Generalization
67. NeurIPS 2023Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
68. NeurIPS 2023Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
69. NeurIPS 2023Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
70. NeurIPS 2023Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
71. NeurIPS 2023Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
72. NeurIPS 2023Multi-Swap k-Means++
73. NeurIPS 2023Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum
74. NeurIPS 2023Multi-task learning with summary statistics
75. NeurIPS 2023Multi-task Representation Learning for Pure Exploration in Bilinear Bandits
76. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
77. NeurIPS 2023Mutual-Information Regularized Multi-Agent Policy Iteration
78. NeurIPS 2023Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
79. NeurIPS 2023Nonparametric Identifiability of Causal Representations from Unknown Interventions
80. NeurIPS 2023Online Pricing for Multi-User Multi-Item Markets
81. NeurIPS 2023Optimistic Rates for Multi-Task Representation Learning
82. NeurIPS 2023Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
83. NeurIPS 2023Pengi: An Audio Language Model for Audio Tasks
84. NeurIPS 2023Prediction and Control in Continual Reinforcement Learning
85. NeurIPS 2023ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
86. NeurIPS 2023RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
87. NeurIPS 2023Real-World Image Super-Resolution as Multi-Task Learning
88. NeurIPS 2023Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
89. NeurIPS 2023Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
90. NeurIPS 2023Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
91. NeurIPS 2023RoboHive: A Unified Framework for Robot Learning
92. NeurIPS 2023Robust Concept Erasure via Kernelized Rate-Distortion Maximization
93. NeurIPS 2023Scalarization for Multi-Task and Multi-Domain Learning at Scale
94. NeurIPS 2023Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells
95. NeurIPS 2023SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
96. NeurIPS 2023SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
97. NeurIPS 2023STXD: Structural and Temporal Cross-Modal Distillation for Multi-View 3D Object Detection
98. NeurIPS 2023Task-aware Distributed Source Coding under Dynamic Bandwidth
99. NeurIPS 2023Task-Robust Pre-Training for Worst-Case Downstream Adaptation
100. NeurIPS 2023TaskMet: Task-driven Metric Learning for Model Learning

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.