Awesome Multi-task Learning

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Table of Contents

• Survey & Study
• Benchmarks & Code
• Papers
• Awesome Multi-domain Multi-task Learning
• Workshops
• Online Courses
• Related awesome list

Survey & Study

• Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types (TPAMI, 2022) [paper]

• Multi-Task Learning for Dense Prediction Tasks: A Survey (TPAMI, 2021) [paper] [code]

• A Survey on Multi-Task Learning (TKDE, 2021) [paper]

• Multi-Task Learning with Deep Neural Networks: A Survey (arXiv, 2020) [paper]

• Taskonomy: Disentangling Task Transfer Learning (CVPR, 2018, Best Paper) [paper] [dataset]

• A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks (IEEE Access, 2019) [paper]

• An Overview of Multi-Task Learning in Deep Neural Networks (arXiv, 2017) [paper]

Benchmarks & Code

Papers

2023

• Auxiliary Learning as an Asymmetric Bargaining Game (ICML, 2023) [paper] [code]

• Learning to Modulate pre-trained Models in RL (arXiv, 2023) [paper] [code]

• [InvPT++]: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding (arXiv, 2023) [paper] [code]

• FAMO: Fast Adaptive Multitask Optimization (arXiv, 2023) [paper] [code]

• Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks (arXiv, 2023) [paper]

• DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning (arXiv, 2023) [paper]

• Planning-oriented Autonomous Driving (CVPR, 2023, Best Paper) [paper] [code]

• MDL-NAS: A Joint Multi-domain Learning Framework for Vision Transformer (CVPR, 2023) [paper]

• Hierarchical Prompt Learning for Multi-Task Learning (CVPR, 2023) [paper]

• Independent Component Alignment for Multi-Task Learning (CVPR, 2023) [paper] [code]

• ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning (TMLR, 2023) [paper] [code]

• MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning (arXiv, 2023) [paper]

• ESSR: Evolving Sparse Sharing Representation for Multi-task Learning (arXiv, 2023) [paper]

• AutoTaskFormer: Searching Vision Transformers for Multi-task Learning (arXiv, 2023) [paper]

• AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations (arXiv, 2023) [paper]

• A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision (arXiv, 2023) [paper]

• Efficient Computation Sharing for Multi-Task Visual Scene Understanding (arXiv, 2023) [paper]

• Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners (CVPR, 2023) [paper] [code]

• Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives (CVPR, 2023) [paper] [code]

• Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach (ICLR, 2023) [paper]

• UNIVERSAL FEW-SHOT LEARNING OF DENSE PREDIC- TION TASKS WITH VISUAL TOKEN MATCHING (ICLR, 2023) [paper]

• TASKPROMPTER: SPATIAL-CHANNEL MULTI-TASK PROMPTING FOR DENSE SCENE UNDERSTANDING (ICLR, 2023) [paper] [code] [dataset]

• Contrastive Multi-Task Dense Prediction (AAAI 2023) [paper]

• Composite Learning for Robust and Effective Dense Predictions (WACV, 2023) [paper]

• Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search (WACV, 2023) [paper]

2022

• RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction (arXiv, 2022) [paper]

• LEARNING USEFUL REPRESENTATIONS FOR SHIFTING TASKS AND DISTRIBUTIONS (arXiv, 2022) [paper]

• Sub-Task Imputation via Self-Labelling to Train Image Moderation Models on Sparse Noisy Data (ACM CIKM, 2022) [paper]

• Multi-Task Meta Learning: learn how to adapt to unseen tasks (arXiv, 2022) [paper]

• M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design (NeurIPS, 2022) [paper] [code]

• AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning (NeurIPS, 2022) [paper] [code]

• Association Graph Learning for Multi-Task Classification with Category Shifts (NeurIPS, 2022) [paper] [code]

• Do Current Multi-Task Optimization Methods in Deep Learning Even Help? (NeurIPS, 2022) [paper]

• Task Discovery: Finding the Tasks that Neural Networks Generalize on (NeurIPS, 2022) [paper]

• [Auto-λ] Auto-λ: Disentangling Dynamic Task Relationships (TMLR, 2022) [paper] [code]

• [Universal Representations] Universal Representations: A Unified Look at Multiple Task and Domain Learning (arXiv, 2022) [paper] [code]

• MTFormer: Multi-Task Learning via Transformer and Cross-Task Reasoning (ECCV, 2022) [paper]

• Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space (ECCV, 2022) [paper] [code]

• Factorizing Knowledge in Neural Networks (ECCV, 2022) [paper] [code]

• [InvPT] Inverted Pyramid Multi-task Transformer for Dense Scene Understanding (ECCV, 2022) [paper] [code]

• [MultiMAE] MultiMAE: Multi-modal Multi-task Masked Autoencoders (ECCV, 2022) [paper] [code]

• A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity (ICML, 2022) [paper]

• Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization (ICML, 2022) [paper]

• Active Multi-Task Representation Learning (ICML, 2022) [paper]

• Generative Modeling for Multi-task Visual Learning (ICML, 2022) [paper] [code]

• Multi-Task Learning as a Bargaining Game (ICML, 2022) [paper] [code]

• Multi-Task Learning with Multi-query Transformer for Dense Prediction (arXiv, 2022) [paper]

• [Gato] A Generalist Agent (arXiv, 2022) [paper]

• [MTPSL] Learning Multiple Dense Prediction Tasks from Partially Annotated Data (CVPR, 2022, Best Paper Finalist) [paper] [code]

• [TSA] Cross-domain Few-shot Learning with Task-specific Adapters (CVPR, 2022) [paper] [code]

• [OMNIVORE] OMNIVORE: A Single Model for Many Visual Modalities (CVPR, 2022) [paper] [code]

• Task Adaptive Parameter Sharing for Multi-Task Learning (CVPR, 2022) [paper]

• Controllable Dynamic Multi-Task Architectures (CVPR, 2022) [paper] [code]

• [SHIFT] SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation (CVPR, 2022) [paper] [code]

• DiSparse: Disentangled Sparsification for Multitask Model Compression (CVPR, 2022) [paper] [code]

• [MulT] MulT: An End-to-End Multitask Learning Transformer (CVPR, 2022) [paper] [code]

• Sound and Visual Representation Learning with Multiple Pretraining Tasks (CVPR, 2022) [paper]

• Medusa: Universal Feature Learning via Attentional Multitasking (CVPR Workshop, 2022) [paper]

• An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems (arXiv, 2022) [paper] [code]

• Combining Modular Skills in Multitask Learning (arXiv, 2022) [paper]

• Visual Representation Learning over Latent Domains (ICLR, 2022) [paper]

• ADARL: What, Where, and How to Adapt in Transfer Reinforcement Learning (ICLR, 2022) [paper] [code]

• Towards a Unified View of Parameter-Efficient Transfer Learning (ICLR, 2022) [paper] [code]

• [Rotograd] Rotograd: Dynamic Gradient Homogenization for Multi-Task Learning (ICLR, 2022) [paper] [code]

• Relational Multi-task Learning: Modeling Relations Between Data and Tasks (ICLR, 2022) [paper]

• Weighted Training for Cross-task Learning (ICLR, 2022) [paper] [code]

• Semi-supervised Multi-task Learning for Semantics and Depth (WACV, 2022) [paper]

• In Defense of the Unitary Scalarization for Deep Multi-Task Learning (arXiv, 2022) [paper]

2021

• Variational Multi-Task Learning with Gumbel-Softmax Priors (NeurIPS, 2021) [paper] [code]

• Efficiently Identifying Task Groupings for Multi-Task Learning (NeurIPS, 2021) [paper]

• [CAGrad] Conflict-Averse Gradient Descent for Multi-task Learning (NeurIPS, 2021) [paper] [code]

• A Closer Look at Loss Weighting in Multi-Task Learning (arXiv, 2021) [paper]

• Exploring Relational Context for Multi-Task Dense Prediction (ICCV, 2021) [paper] [code]

• Multi-Task Self-Training for Learning General Representations (ICCV, 2021) [paper]

• Task Switching Network for Multi-task Learning (ICCV, 2021) [paper] [code]

• Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (ICCV, 2021) [paper] [project]

• Robustness via Cross-Domain Ensembles (ICCV, 2021) [paper] [code]

• Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (ICCV, 2021) [paper] [code]

• [URL] Universal Representation Learning from Multiple Domains for Few-shot Classification (ICCV, 2021) [paper] [code]

• [tri-M] A Multi-Mode Modulator for Multi-Domain Few-Shot Classification (ICCV, 2021) [paper] [code]

• MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach (ICCV Workshop, 2021) [paper]

• See Yourself in Others: Attending Multiple Tasks for Own Failure Detection (arXiv, 2021) [paper]

• A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty Estimation in 3D Cardiac MRI Image Segmentation (CinC, 2021) [paper] [code]

• Multi-Task Reinforcement Learning with Context-based Representations (ICML, 2021) [paper]

• [FLUTE] Learning a Universal Template for Few-shot Dataset Generalization (ICML, 2021) [paper] [code]

• Towards a Unified View of Parameter-Efficient Transfer Learning (arXiv, 2021) [paper]

• UniT: Multimodal Multitask Learning with a Unified Transformer (arXiv, 2021) [paper]

• Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation (CVPR, 2021) [paper] [code]

• CompositeTasking: Understanding Images by Spatial Composition of Tasks (CVPR, 2021) [paper] [code]

• Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (CVPR, 2021) [paper]

• Taskology: Utilizing Task Relations at Scale (CVPR, 2021) [paper]

• Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation (CVPR, 2021) [paper] [code]

• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation (arXiv, 2021) [paper] [code]

• Counter-Interference Adapter for Multilingual Machine Translation (Findings of EMNLP, 2021) [paper]

• Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (ICLR) [paper] [code]

• [Gradient Vaccine] Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models (ICLR, 2021) [paper]

• [IMTL] Towards Impartial Multi-task Learning (ICLR, 2021) [paper]

• Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach (ICLR, 2021) [paper]

• [URT] A Universal Representation Transformer Layer for Few-Shot Image Classification (ICLR, 2021) [paper] [code]

• Flexible Multi-task Networks by Learning Parameter Allocation (ICLR Workshop, 2021) [paper]

• Multi-Loss Weighting with Coefficient of Variations (WACV, 2021) [paper] [code]

2020

• Multi-Task Reinforcement Learning with Soft Modularization (NeurIPS, 2020) [paper] [code]

• AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS, 2020) [paper] [code]

• [GradDrop] Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout (NeurIPS, 2020) [paper] [code]

• [PCGrad] Gradient Surgery for Multi-Task Learning (NeurIPS, 2020) [paper] [tensorflow] [pytorch]

• On the Theory of Transfer Learning: The Importance of Task Diversity (NeurIPS, 2020) [paper]

• A Study of Residual Adapters for Multi-Domain Neural Machine Translation (WMT, 2020) [paper]

• Multi-Task Adversarial Attack (arXiv, 2020) [paper]

• Automated Search for Resource-Efficient Branched Multi-Task Networks (BMVC, 2020) [paper] [code]

• Branched Multi-Task Networks: Deciding What Layers To Share (BMVC, 2020) [paper]

• MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning (ECCV, 2020) [paper] [code]

• Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference (ECCV, 2020) [paper] [code]

• Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification (ECCV, 2020) [paper] [code]

• Multitask Learning Strengthens Adversarial Robustness (ECCV 2020) [paper] [code]

• Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning (ECCV, 2020) [paper] [code]

• [KD4MTL] Knowledge Distillation for Multi-task Learning (ECCV Workshop) [paper] [code]

• MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning (CVPR, 2020) [paper] [code]

• Robust Learning Through Cross-Task Consistency (CVPR, 2020) [paper] [code]

• 12-in-1: Multi-Task Vision and Language Representation Learning (CVPR, 2020) paper [code]

• A Multi-task Mean Teacher for Semi-supervised Shadow Detection (CVPR, 2020) [paper] [code]

• MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer (EMNLP, 2020) [paper]

• Masking as an Efficient Alternative to Finetuning for Pretrained Language Models (EMNLP, 2020) [paper] [code]

• Effcient Continuous Pareto Exploration in Multi-Task Learning (ICML, 2020) [paper] [code]

• Which Tasks Should Be Learned Together in Multi-task Learning? (ICML, 2020) [paper] [code]

• Learning to Branch for Multi-Task Learning (ICML, 2020) [paper]

• Partly Supervised Multitask Learning (ICMLA, 2020) paper

• Understanding and Improving Information Transfer in Multi-Task Learning (ICLR, 2020) [paper]

• Measuring and Harnessing Transference in Multi-Task Learning (arXiv, 2020) [paper]

• Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition (arXiv, 2020) [paper]

• Learning Sparse Sharing Architectures for Multiple Tasks (AAAI, 2020) [paper]

• AdapterFusion: Non-Destructive Task Composition for Transfer Learning (arXiv, 2020) [paper]

2019

• Adaptive Auxiliary Task Weighting for Reinforcement Learning (NeurIPS, 2019) [paper]

• Pareto Multi-Task Learning (NeurIPS, 2019) [paper] [code]

• Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains (NeurIPS, 2019) [paper]

• Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (NeurIPS, 2019) [paper] [code]

• [Orthogonal] Regularizing Deep Multi-Task Networks using Orthogonal Gradients (arXiv, 2019) [paper]

• Many Task Learning With Task Routing (ICCV, 2019) [paper] [code]

• Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels (ICCV, 2019) [paper]

• Deep Elastic Networks with Model Selection for Multi-Task Learning (ICCV, 2019) [paper] [code]

• Feature Partitioning for Efficient Multi-Task Architectures (arXiv, 2019) [paper] [code]

• Task Selection Policies for Multitask Learning (arXiv, 2019) [paper]

• BAM! Born-Again Multi-Task Networks for Natural Language Understanding (ACL, 2019) [paper] [code]

• OmniNet: A unified architecture for multi-modal multi-task learning (arXiv, 2019) [paper]

• NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction (CVPR, 2019) [paper] [code]

• [MTAN + DWA] End-to-End Multi-Task Learning with Attention (CVPR, 2019) [paper] [code]

• Attentive Single-Tasking of Multiple Tasks (CVPR, 2019) [paper] [code]

• Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation (CVPR, 2019) [paper]

• Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning (CVPR, 2019) [paper] [code]

• [Geometric Loss Strategy (GLS)] MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning (CVPR Workshop, 2019) [paper]

• Parameter-Efficient Transfer Learning for NLP (ICML, 2019) [paper]

• BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ICML, 2019) [paper] [code]

• Tasks Without Borders: A New Approach to Online Multi-Task Learning (ICML Workshop, 2019) [paper]

• AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning (NACCL, 2019) [paper] [code]

• Multi-Task Deep Reinforcement Learning with PopArt (AAAI, 2019) [paper]

• SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning (AAAI, 2019) [paper]

• Latent Multi-task Architecture Learning (AAAI, 2019) [paper] [[code](https://github.com/ sebastianruder/sluice-networks)]

• Multi-Task Deep Neural Networks for Natural Language Understanding (ACL, 2019) [paper]

2018

• Learning to Multitask (NeurIPS, 2018) [paper]

• [MGDA] Multi-Task Learning as Multi-Objective Optimization (NeurIPS, 2018) [paper] [code]

• Adapting Auxiliary Losses Using Gradient Similarity (arXiv, 2018) [paper] [code]

• Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV, 2018) [paper] [code]

• Dynamic Task Prioritization for Multitask Learning (ECCV, 2018) [paper]

• A Modulation Module for Multi-task Learning with Applications in Image Retrieval (ECCV, 2018) [paper]

• Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (KDD, 2018) [paper]

• Unifying and Merging Well-trained Deep Neural Networks for Inference Stage (IJCAI, 2018) [paper] [code]

• Efficient Parametrization of Multi-domain Deep Neural Networks (CVPR, 2018) [paper] [code]

• PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing (CVPR, 2018) [paper]

• NestedNet: Learning Nested Sparse Structures in Deep Neural Networks (CVPR, 2018) [paper]

• PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR, 2018) [paper] [code]

• [Uncertainty] Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (CVPR, 2018) [paper]

• Deep Asymmetric Multi-task Feature Learning (ICML, 2018) [paper]

• [GradNorm] GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ICML, 2018) [paper]

• Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back (ICML, 2018) [paper]

• Gradient Adversarial Training of Neural Networks (arXiv, 2018) [paper]

• Auxiliary Tasks in Multi-task Learning (arXiv, 2018) [paper]

• Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning (ICLR, 2018) [paper] [code

• Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering (ICLR, 2018) [paper]

2017

• Learning multiple visual domains with residual adapters (NeurIPS, 2017) [paper] [code]

• Learning Multiple Tasks with Multilinear Relationship Networks (NeurIPS, 2017) [paper] [code]

• Federated Multi-Task Learning (NeurIPS, 2017) [paper] [code]

• Multi-task Self-Supervised Visual Learning (ICCV, 2017) [paper]

• Adversarial Multi-task Learning for Text Classification (ACL, 2017) [paper]

• UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (CVPR, 2017) [paper]

• Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification (CVPR, 2017) [paper]

• Modular Multitask Reinforcement Learning with Policy Sketches (ICML, 2017) [paper] [code]

• SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (ICML, 2017) [paper] [code]

• One Model To Learn Them All (arXiv, 2017) [paper] [code]

• [AdaLoss] Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing (arXiv, 2017) [paper]

• Deep Multi-task Representation Learning: A Tensor Factorisation Approach (ICLR, 2017) [paper] [code]

• Trace Norm Regularised Deep Multi-Task Learning (ICLR Workshop, 2017) [paper] [code]

• When is multitask learning effective? Semantic sequence prediction under varying data conditions (EACL, 2017) [paper] [code]

• Identifying beneficial task relations for multi-task learning in deep neural networks (EACL, 2017) [paper]

• PathNet: Evolution Channels Gradient Descent in Super Neural Networks (arXiv, 2017) [paper] [code]

• Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification (AAAI, 2017) [paper]

2016 and earlier

• Learning values across many orders of magnitude (NeurIPS, 2016) [paper]

• Integrated Perception with Recurrent Multi-Task Neural Networks (NeurIPS, 2016) [paper]

• Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives (arXiv, 2016) [paper]

• Progressive Neural Networks (arXiv, 2016) [paper]

• Deep multi-task learning with low level tasks supervised at lower layers (ACL, 2016) [paper]

• [Cross-Stitch] Cross-Stitch Networks for Multi-task Learning (CVPR,2016) [paper] [code]

• Asymmetric Multi-task Learning based on Task Relatedness and Confidence (ICML, 2016) [paper]

• MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving (arXiv, 2016) [paper] [code]

• A Unified Perspective on Multi-Domain and Multi-Task Learning (ICLR, 2015) [paper]

• Facial Landmark Detection by Deep Multi-task Learning (ECCV, 2014) [paper] [code]

• Learning Task Grouping and Overlap in Multi-task Learning (ICML, 2012) [paper]

• Learning with Whom to Share in Multi-task Feature Learning (ICML, 2011) [paper]

• Semi-Supervised Multi-Task Learning with Task Regularizations (ICDM, 2009) [paper]

• Semi-Supervised Multitask Learning (NeurIPS, 2008) [paper]

• Multitask Learning (1997) [paper]

Awesome Multi-domain Multi-task Learning

Workshops

• Universal Representations for Computer Vision Workshop at BMVC 2022

• Workshop on Multi-Task Learning in Computer Vision (DeepMTL) at ICCV 2021

• Adaptive and Multitask Learning: Algorithms & Systems Workshop (AMTL) at ICML 2019

• Workshop on Multi-Task and Lifelong Reinforcement Learning at ICML 2015

• Transfer and Multi-Task Learning: Trends and New Perspectives at NeurIPS 2015

• Second Workshop on Transfer and Multi-task Learning at NeurIPS 2014

• New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks Workshop at NeurIPS 2013

Online Courses

• CS 330: Deep Multi-Task and Meta Learning

Related awesome list

• https://github.com/SimonVandenhende/Awesome-Multi-Task-Learning

• https://github.com/Manchery/awesome-multi-task-learning