“‘Meta-Learning’ Tag”,2019-09-02 (dynamic evaluation (NN), inner monologue (AI), RL scaling; backlinks)⁠:

Bibliography for tag reinforcement-learning/meta-learning, most recent first: 6 related tags, 374 annotations, & 35 links (parent).

• See Also
• Gwern
  • “Free-Play Periods for RL Agents”, Gwern2023
  • “WBE and DRL: a Middle Way of Imitation Learning from the Human Brain”, Gwern2018
• Links
  • “State-Space Models Can Learn In-Context by Gradient Descent”, Sushma et al 2024
  • “Thinking LLMs: General Instruction Following With Thought Generation”, Wu et al 2024
  • “MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering”, Chan et al 2024
  • “Contextual Document Embeddings”, Morris & Rush2024
  • “Generating Diverse and Reliable Features for Few-Shot Learning”, Xu2024b
  • “When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models”, Chang et al 2024
  • “Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Zhao et al 2024
  • “Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models”, Denison et al 2024
  • “Discovering Preference Optimization Algorithms With and for Large Language Models”, Lu et al 2024
  • “State Soup: In-Context Skill Learning, Retrieval and Mixing”, Pióro et al 2024
  • “Attention As a Hypernetwork”, Schug et al 2024
  • “BERTs Are Generative In-Context Learners”, Samuel2024
  • “To Believe or Not to Believe Your LLM”, Yadkori et al 2024
  • “Learning to Grok: Emergence of In-Context Learning and Skill Composition in Modular Arithmetic Tasks”, He et al 2024
  • “Auto Evol-Instruct: Automatic Instruction Evolving for Large Language Models”, Zeng et al 2024
  • “A Theoretical Understanding of Self-Correction through In-Context Alignment”, Wang et al 2024
  • “MLPs Learn In-Context”, Tong & Pehlevan2024
  • “Zero-Shot Tokenizer Transfer”, Minixhofer et al 2024
  • “Position: Understanding LLMs Requires More Than Statistical Generalization”, Reizinger et al 2024
  • “SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-Trained Models”, Deng et al 2024
  • “Many-Shot In-Context Learning”, Agarwal et al 2024
  • “Foundational Challenges in Assuring Alignment and Safety of Large Language Models”, Anwar et al 2024
  • “Revisiting the Equivalence of In-Context Learning and Gradient Descent: The Impact of Data Distribution”, Mahdavi et al 2024
  • “Best Practices and Lessons Learned on Synthetic Data for Language Models”, Liu et al 2024
  • “From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples”, Vacareanu et al 2024
  • “Mixture-Of-Depths: Dynamically Allocating Compute in Transformer-Based Language Models”, Raposo et al 2024
  • “Evolutionary Optimization of Model Merging Recipes”, Akiba et al 2024
  • “How Well Can Transformers Emulate In-Context Newton’s Method?”, Giannou et al 2024
  • “Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models”, Rannen-Triki et al 2024
  • “Neural Network Parameter Diffusion”, Wang et al 2024
  • “The Matrix: A Bayesian Learning Model for LLMs”, Dalal & Misra2024
  • “Rephrasing the Web (WARP): A Recipe for Compute and Data-Efficient Language Modeling”, Maini et al 2024
  • “An Information-Theoretic Analysis of In-Context Learning”, Jeon et al 2024
  • “Deep De Finetti: Recovering Topic Distributions from Large Language Models”, Zhang et al 2023
  • “Generative Multimodal Models Are In-Context Learners”, Sun et al 2023
  • “VILA: On Pre-Training for Visual Language Models”, Lin et al 2023
  • “Evolving Reservoirs for Meta Reinforcement Learning”, Léger et al 2023
  • “The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning”, Lin et al 2023
  • “Learning Few-Shot Imitation As Cultural Transmission”, Bhoopchand et al 2023
  • “In-Context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering”, Liu et al 2023
  • “Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves”, Deng et al 2023
  • “ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-Like Language Models”, Luo et al 2023
  • “Self-AIXI: Self-Predictive Universal AI”, Catt et al 2023
  • “HyperFields: Towards Zero-Shot Generation of NeRFs from Text”, Babu et al 2023
  • “Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study With Linear Models”, Fu et al 2023
  • “Eureka: Human-Level Reward Design via Coding Large Language Models”, Ma et al 2023
  • “How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?”, Wu et al 2023
  • “Motif: Intrinsic Motivation from Artificial Intelligence Feedback”, Klissarov et al 2023
  • “ExpeL: LLM Agents Are Experiential Learners”, Zhao et al 2023
  • “Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Zahavy et al 2023
  • “RAVEN: In-Context Learning With Retrieval-Augmented Encoder-Decoder Language Models”, Huang et al 2023
  • “CausalLM Is Not Optimal for In-Context Learning”, Ding et al 2023
  • “MetaDiff: Meta-Learning With Conditional Diffusion for Few-Shot Learning”, Zhang & Yu2023
  • “Self Expanding Neural Networks”, Mitchell et al 2023
  • “Teaching Arithmetic to Small Transformers”, Lee et al 2023
  • “One Step of Gradient Descent Is Provably the Optimal In-Context Learner With One Layer of Linear Self-Attention”, Mahankali et al 2023
  • “Trainable Transformer in Transformer”, Panigrahi et al 2023
  • “Supervised Pretraining Can Learn In-Context Reinforcement Learning”, Lee et al 2023
  • “Pretraining Task Diversity and the Emergence of Non-Bayesian In-Context Learning for Regression”, Raventós et al 2023
  • “Language Models Are Weak Learners”, Manikandan et al 2023
  • “Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks”, Chevalier-Boisvert et al 2023
  • “Improving Long-Horizon Imitation Through Instruction Prediction”, Hejna et al 2023
  • “Schema-Learning and Rebinding As Mechanisms of In-Context Learning and Emergence”, Swaminathan et al 2023
  • “RGD: Stochastic Re-Weighted Gradient Descent via Distributionally Robust Optimization”, Kumar et al 2023
  • “Transformers Learn to Implement Preconditioned Gradient Descent for In-Context Learning”, Ahn et al 2023
  • “Learning Transformer Programs”, Friedman et al 2023
  • “Fundamental Limitations of Alignment in Large Language Models”, Wolf et al 2023
  • “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
  • “Larger Language Models Do In-Context Learning Differently”, Wei et al 2023
  • “BiLD: Big Little Transformer Decoder”, Kim et al 2023
  • “Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery”, Wen et al 2023
  • “Looped Transformers As Programmable Computers”, Giannou et al 2023
  • “A Survey of Meta-Reinforcement Learning”, Beck et al 2023
  • “Human-Like Systematic Generalization through a Meta-Learning Neural Network”, Lake & Baroni2023
  • “Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent As Meta-Optimizers”, Dai et al 2022
  • “Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Honovich et al 2022
  • “Rethinking the Role of Scale for In-Context Learning: An Interpretability-Based Case Study at 66 Billion Scale”, Bansal et al 2022
  • “Transformers Learn In-Context by Gradient Descent”, Oswald et al 2022
  • “FWL: Meta-Learning Fast Weight Language Models”, Clark et al 2022
  • “What Learning Algorithm Is In-Context Learning? Investigations With Linear Models”, Akyürek et al 2022
  • “Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models”, Henderson et al 2022
  • “VeLO: Training Versatile Learned Optimizers by Scaling Up”, Metz et al 2022
  • “Mysteries of Mode Collapse § Inescapable Wedding Parties”, Janus2022
  • “BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Muennighoff et al 2022
  • “ProMoT: Preserving In-Context Learning Ability in Large Language Model Fine-Tuning”, Wang et al 2022
  • “In-Context Reinforcement Learning With Algorithm Distillation”, Laskin et al 2022
  • “SAP: Bidirectional Language Models Are Also Few-Shot Learners”, Patel et al 2022
  • “g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, Peebles et al 2022
  • “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model”, Soltan et al 2022
  • “Few-Shot Adaptation Works With UnpredicTable Data”, Chan et al 2022
  • “What Can Transformers Learn In-Context? A Case Study of Simple Function Classes”, Garg et al 2022
  • “Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling”, Nguyen & Grover2022
  • “TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data”, Hollmann et al 2022
  • “Offline RL Policies Should Be Trained to Be Adaptive”, Ghosh et al 2022
  • “Goal-Conditioned Generators of Deep Policies”, Faccio et al 2022
  • “Prompting Decision Transformer for Few-Shot Policy Generalization”, Xu et al 2022
  • “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Mindermann et al 2022
  • “NOAH: Neural Prompt Search”, Zhang et al 2022
  • “Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions”, Jiang et al 2022
  • “Towards Learning Universal Hyperparameter Optimizers With Transformers”, Chen et al 2022
  • “Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich et al 2022
  • “Gato: A Generalist Agent”, Reed et al 2022
  • “Unifying Language Learning Paradigms”, Tay et al 2022
  • “Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Chan et al 2022
  • “Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Wang et al 2022
  • “What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?”, Wang et al 2022
  • “Effective Mutation Rate Adaptation through Group Elite Selection”, Kumar et al 2022
  • “Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs”, Akin et al 2022
  • “Can Language Models Learn from Explanations in Context?”, Lampinen et al 2022
  • “Auto-Lambda: Disentangling Dynamic Task Relationships”, Liu et al 2022
  • “In-Context Learning and Induction Heads”, Olsson et al 2022
  • “HyperMixer: An MLP-Based Low Cost Alternative to Transformers”, Mai et al 2022
  • “LiteTransformerSearch: Training-Free Neural Architecture Search for Efficient Language Models”, Javaheripi et al 2022
  • “Evolving Curricula With Regret-Based Environment Design”, Parker-Holder et al 2022
  • “HyperPrompt: Prompt-Based Task-Conditioning of Transformers”, He et al 2022
  • “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”, Min et al 2022
  • “All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL”, Arulkumaran et al 2022
  • “NeuPL: Neural Population Learning”, Liu et al 2022
  • “Learning Synthetic Environments and Reward Networks for Reinforcement Learning”, Ferreira et al 2022
  • “From Data to Functa: Your Data Point Is a Function and You Should Treat It like One”, Dupont et al 2022
  • “Environment Generation for Zero-Shot Compositional Reinforcement Learning”, Gur et al 2022
  • “Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies”, Gklezakos & Rao2022
  • “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild”, Miki et al 2022
  • “Automated Reinforcement Learning (AutoRL): A Survey and Open Problems”, Parker-Holder et al 2022
  • “In Defense of the Unitary Scalarization for Deep Multi-Task Learning”, Kurin et al 2022
  • “HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning”, Zhmoginov et al 2022
  • “Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning”, Curry et al 2022
  • “The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence”, Miranda et al 2021
  • “A Mathematical Framework for Transformer Circuits”, Elhage et al 2021
  • “PFNs: Transformers Can Do Bayesian Inference”, Müller et al 2021
  • “How to Learn and Represent Abstractions: An Investigation Using Symbolic Alchemy”, AlKhamissi et al 2021
  • “Noether Networks: Meta-Learning Useful Conserved Quantities”, Alet et al 2021
  • “A Rational Reinterpretation of Dual-Process Theories”, Milli et al 2021
  • “A General Language Assistant As a Laboratory for Alignment”, Askell et al 2021
  • “A Modern Self-Referential Weight Matrix That Learns to Modify Itself”, Irie et al 2021
  • “A Survey of Generalization in Deep Reinforcement Learning”, Kirk et al 2021
  • “Gradients Are Not All You Need”, Metz et al 2021
  • “An Explanation of In-Context Learning As Implicit Bayesian Inference”, Xie et al 2021
  • “Procedural Generalization by Planning With Self-Supervised World Models”, Anand et al 2021
  • “MetaICL: Learning to Learn In Context”, Min et al 2021
  • “Logical Activation Functions: Logit-Space Equivalents of Probabilistic Boolean Operators”, Lowe et al 2021
  • “Shaking the Foundations: Delusions in Sequence Models for Interaction and Control”, Ortega et al 2021
  • “Meta-Learning, Social Cognition and Consciousness in Brains and Machines”, Langdon et al 2021
  • “T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Sanh et al 2021
  • “Replay-Guided Adversarial Environment Design”, Jiang et al 2021
  • “Embodied Intelligence via Learning and Evolution”, Gupta et al 2021
  • “Transformers Are Meta-Reinforcement Learners”, Anonymous2021
  • “Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Fickinger et al 2021
  • “Dropout’s Dream Land: Generalization from Learned Simulators to Reality”, Wellmer & Kwok2021
  • “Is Curiosity All You Need? On the Utility of Emergent Behaviors from Curious Exploration”, Groth et al 2021
  • “Bootstrapped Meta-Learning”, Flennerhag et al 2021
  • “The Sensory Neuron As a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning”, Tang & Ha2021
  • “FLAN: Finetuned Language Models Are Zero-Shot Learners”, Wei et al 2021
  • “The AI Economist: Optimal Economic Policy Design via Two-Level Deep Reinforcement Learning”, Zheng et al 2021
  • “Open-Ended Learning Leads to Generally Capable Agents”, Team et al 2021
  • “Dataset Distillation With Infinitely Wide Convolutional Networks”, Nguyen et al 2021
  • “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability”, Ghosh et al 2021
  • “PonderNet: Learning to Ponder”, Banino et al 2021
  • “Multimodal Few-Shot Learning With Frozen Language Models”, Tsimpoukelli et al 2021
  • “LHOPT: A Generalizable Approach to Learning Optimizers”, Almeida et al 2021
  • “Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents”, Lampinen et al 2021
  • “A Full-Stack Accelerator Search Technique for Vision Applications”, Zhang et al 2021
  • “Reward Is Enough”, Silver et al 2021
  • “Bayesian Optimization Is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020”, Turner et al 2021
  • “CrossFit: A Few-Shot Learning Challenge for Cross-Task Generalization in NLP”, Ye et al 2021
  • “Podracer Architectures for Scalable Reinforcement Learning”, Hessel et al 2021
  • “BLUR: Meta-Learning Bidirectional Update Rules”, Sandler et al 2021
  • “Asymmetric Self-Play for Automatic Goal Discovery in Robotic Manipulation”, OpenAI et al 2021
  • “OmniNet: Omnidirectional Representations from Transformers”, Tay et al 2021
  • “Linear Transformers Are Secretly Fast Weight Programmers”, Schlag et al 2021
  • “Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm”, Reynolds & McDonell2021
  • “ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution”, Song et al 2021
  • “Training Learned Optimizers With Randomly Initialized Learned Optimizers”, Metz et al 2021
  • “Evolving Reinforcement Learning Algorithms”, Co-Reyes et al 2021
  • “Meta Pseudo Labels”, Pham et al 2021
  • “Meta Learning Backpropagation And Improving It”, Kirsch & Schmidhuber2020
  • “Emergent Complexity and Zero-Shot Transfer via Unsupervised Environment Design”, Dennis et al 2020
  • “Scaling down Deep Learning”, Greydanus2020
  • “Reverse Engineering Learned Optimizers Reveals Known and Novel Mechanisms”, Maheswaranathan et al 2020
  • “Dataset Meta-Learning from Kernel Ridge-Regression”, Nguyen et al 2020
  • “MELD: Meta-Reinforcement Learning from Images via Latent State Models”, Zhao et al 2020
  • “Meta-Trained Agents Implement Bayes-Optimal Agents”, Mikulik et al 2020
  • “Learning Not to Learn: Nature versus Nurture in Silico”, Lange & Sprekeler2020
  • “Prioritized Level Replay”, Jiang et al 2020
  • “Tasks, Stability, Architecture, and Compute: Training More Effective Learned Optimizers, and Using Them to Train Themselves”, Metz et al 2020
  • “Hidden Incentives for Auto-Induced Distributional Shift”, Krueger et al 2020
  • “Grounded Language Learning Fast and Slow”, Hill et al 2020
  • “Matt Botvinick on the Spontaneous Emergence of Learning Algorithms”, Scholl2020
  • “Discovering Reinforcement Learning Algorithms”, Oh et al 2020
  • “Deep Reinforcement Learning and Its Neuroscientific Implications”, Botvinick2020
  • “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions”, Chang et al 2020
  • “Rapid Task-Solving in Novel Environments”, Ritter et al 2020
  • “FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining”, Dai et al 2020
  • “GPT-3: Language Models Are Few-Shot Learners”, Brown et al 2020
  • “Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search”, Rawal et al 2020
  • “Automatic Discovery of Interpretable Planning Strategies”, Skirzyński et al 2020
  • “Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks”, Schoettler et al 2020
  • “A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation”, Fernández-Loría et al 2020
  • “Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020
  • “Meta-Learning in Neural Networks: A Survey”, Hospedales et al 2020
  • “Agent57: Outperforming the Atari Human Benchmark”, Badia et al 2020
  • “Designing Network Design Spaces”, Radosavovic et al 2020
  • “Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions”, Wang et al 2020
  • “Accelerating and Improving AlphaZero Using Population Based Training”, Wu et al 2020
  • “Meta-Learning Curiosity Algorithms”, Alet et al 2020
  • “AutoML-Zero: Evolving Machine Learning Algorithms From Scratch”, Real et al 2020
  • “AutoML-Zero: Open Source Code for the Paper: “AutoML-Zero: Evolving Machine Learning Algorithms From Scratch””, Real et al 2020
  • “Effective Diversity in Population Based Reinforcement Learning”, Parker-Holder et al 2020
  • “AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch”, Knight2020
  • “Smooth Markets: A Basic Mechanism for Organizing Gradient-Based Learners”, Balduzzi et al 2020
  • “AutoML-Zero: Evolving Code That Learns”, Real & Liang2020
  • “Learning Neural Activations”, Minhas & Asif2019
  • “Meta-Learning without Memorization”, Yin et al 2019
  • “MetaFun: Meta-Learning With Iterative Functional Updates”, Xu et al 2019
  • “Leveraging Procedural Generation to Benchmark Reinforcement Learning”, Cobbe et al 2019
  • “Procgen Benchmark: We’re Releasing Procgen Benchmark, 16 Simple-To-Use Procedurally-Generated Environments Which Provide a Direct Measure of How Quickly a Reinforcement Learning Agent Learns Generalizable Skills”, Cobbe et al 2019
  • “Increasing Generality in Machine Learning through Procedural Content Generation”, Risi & Togelius2019
  • “SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning”, Wang et al 2019
  • “Optimizing Millions of Hyperparameters by Implicit Differentiation”, Lorraine et al 2019
  • “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction”, Freeman et al 2019
  • “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction [Blog]”, Freeman et al 2019
  • “Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”, Yu et al 2019
  • “Solving Rubik’s Cube With a Robot Hand”, OpenAI et al 2019
  • “Solving Rubik’s Cube With a Robot Hand [Blog]”, OpenAI 2019
  • “Gradient Descent: The Ultimate Optimizer”, Chandra et al 2019
  • “Data Valuation Using Reinforcement Learning”, Yoon et al 2019
  • “Multiplicative Interactions and Where to Find Them”, Jayakumar et al 2019
  • “ANIL: Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML”, Raghu et al 2019
  • “Emergent Tool Use From Multi-Agent Autocurricula”, Baker et al 2019
  • “Meta-Learning With Implicit Gradients”, Rajeswaran et al 2019
  • “A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains”, Zador2019
  • “AutoML: A Survey of the State-Of-The-Art”, He et al 2019
  • “Metalearned Neural Memory”, Munkhdalai et al 2019
  • “Algorithms for Hyper-Parameter Optimization”, Bergstra et al 2019
  • “Evolving the Hearthstone Meta”, Silva et al 2019
  • “Meta Reinforcement Learning”, Weng2019
  • “One Epoch Is All You Need”, Komatsuzaki2019
  • “Compositional Generalization through Meta Sequence-To-Sequence Learning”, Lake2019
  • “Risks from Learned Optimization in Advanced Machine Learning Systems”, Hubinger et al 2019
  • “ICML 2019 Notes”, Abel2019
  • “SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers”, Fedorov et al 2019
  • “AI-GAs: AI-Generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence”, Clune2019
  • “Alpha MAML: Adaptive Model-Agnostic Meta-Learning”, Behl et al 2019
  • “Reinforcement Learning, Fast and Slow”, Botvinick et al 2019
  • “Meta Reinforcement Learning As Task Inference”, Humplik et al 2019
  • “Learning Loss for Active Learning”, Yoo & Kweon2019
  • “Meta-Learning of Sequential Strategies”, Ortega et al 2019
  • “Searching for MobileNetV3”, Howard et al 2019
  • “Meta-Learners’ Learning Dynamics Are unlike Learners’”, Rabinowitz2019
  • “Ray Interference: a Source of Plateaus in Deep Reinforcement Learning”, Schaul et al 2019
  • “AlphaX: EXploring Neural Architectures With Deep Neural Networks and Monte Carlo Tree Search”, Wang et al 2019
  • “Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables”, Rakelly et al 2019
  • “Task2Vec: Task Embedding for Meta-Learning”, Achille et al 2019
  • “The Omniglot Challenge: a 3-Year Progress Report”, Lake et al 2019
  • “FIGR: Few-Shot Image Generation With Reptile”, Clouâtre & Demers2019
  • “Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions”, Wang et al 2019
  • “Meta-Learning Neural Bloom Filters”, Rae2019
  • “Malthusian Reinforcement Learning”, Leibo et al 2018
  • “Quantifying Generalization in Reinforcement Learning”, Cobbe et al 2018
  • “An Introduction to Deep Reinforcement Learning”, Francois-Lavet et al 2018
  • “Meta-Learning: Learning to Learn Fast”, Weng2018
  • “Evolving Space-Time Neural Architectures for Videos”, Piergiovanni et al 2018
  • “Understanding and Correcting Pathologies in the Training of Learned Optimizers”, Metz et al 2018
  • “BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning”, Chevalier-Boisvert et al 2018
  • “Deep Reinforcement Learning”, Li2018
  • “Searching for Efficient Multi-Scale Architectures for Dense Image Prediction”, Chen et al 2018
  • “Backprop Evolution”, Alber et al 2018
  • “Learning Dexterous In-Hand Manipulation”, OpenAI et al 2018
  • “LEO: Meta-Learning With Latent Embedding Optimization”, Rusu et al 2018
  • “Automatically Composing Representation Transformations As a Means for Generalization”, Chang et al 2018
  • “Human-Level Performance in First-Person Multiplayer Games With Population-Based Deep Reinforcement Learning”, Jaderberg et al 2018
  • “Guided Evolutionary Strategies: Augmenting Random Search With Surrogate Gradients”, Maheswaranathan et al 2018
  • “RUDDER: Return Decomposition for Delayed Rewards”, Arjona-Medina et al 2018
  • “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Pang et al 2018
  • “Fingerprint Policy Optimization for Robust Reinforcement Learning”, Paul et al 2018
  • “AutoAugment: Learning Augmentation Policies from Data”, Cubuk et al 2018
  • “Meta-Gradient Reinforcement Learning”, Xu et al 2018
  • “Continuous Learning in a Hierarchical Multiscale Neural Network”, Wolf et al 2018
  • “Prefrontal Cortex As a Meta-Reinforcement Learning System”, Wang et al 2018
  • “Meta-Learning Update Rules for Unsupervised Representation Learning”, Metz et al 2018
  • “Reviving and Improving Recurrent Back-Propagation”, Liao et al 2018
  • “Kickstarting Deep Reinforcement Learning”, Schmitt et al 2018
  • “Reptile: On First-Order Meta-Learning Algorithms”, Nichol et al 2018
  • “Some Considerations on Learning to Explore via Meta-Reinforcement Learning”, Stadie et al 2018
  • “One Big Net For Everything”, Schmidhuber2018
  • “Machine Theory of Mind”, Rabinowitz et al 2018
  • “Evolved Policy Gradients”, Houthooft et al 2018
  • “One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning”, Yu et al 2018
  • “Rover Descent: Learning to Optimize by Learning to Navigate on Prototypical Loss Surfaces”, Faury & Vasile2018
  • “ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, Kim & Choi2018
  • “Population Based Training of Neural Networks”, Jaderberg et al 2017
  • “BlockDrop: Dynamic Inference Paths in Residual Networks”, Wu et al 2017
  • “Learning to Select Computations”, Callaway et al 2017
  • “Learning to Generalize: Meta-Learning for Domain Generalization”, Li et al 2017
  • “Efficient K-Shot Learning With Regularized Deep Networks”, Yoo et al 2017
  • “Online Learning of a Memory for Learning Rates”, Meier et al 2017
  • “One-Shot Visual Imitation Learning via Meta-Learning”, Finn et al 2017
  • “Supervising Unsupervised Learning”, Garg & Kalai2017
  • “Learning With Opponent-Learning Awareness”, Foerster et al 2017
  • “SMASH: One-Shot Model Architecture Search through HyperNetworks”, Brock et al 2017
  • “Stochastic Optimization With Bandit Sampling”, Salehi et al 2017
  • “A Simple Neural Attentive Meta-Learner”, Mishra et al 2017
  • “Reinforcement Learning for Learning Rate Control”, Xu et al 2017
  • “Metacontrol for Adaptive Imagination-Based Optimization”, Hamrick et al 2017
  • “Deciding How to Decide: Dynamic Routing in Artificial Neural Networks”, McGill & Perona2017
  • “Prototypical Networks for Few-Shot Learning”, Snell et al 2017
  • “Learned Optimizers That Scale and Generalize”, Wichrowska et al 2017
  • “MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”, Finn et al 2017
  • “Learning to Optimize Neural Nets”, Li & Malik2017
  • “Understanding Synthetic Gradients and Decoupled Neural Interfaces”, Czarnecki et al 2017
  • “Optimization As a Model for Few-Shot Learning”, Ravi & Larochelle2017
  • “Learning to Superoptimize Programs”, Bunel et al 2017
  • “Discovering Objects and Their Relations from Entangled Scene Representations”, Raposo et al 2017
  • “Google Vizier: A Service for Black-Box Optimization”, Golovin2017
  • “An Actor-Critic Algorithm for Learning Rate Learning”, Xu et al 2016
  • “A Bird’s Eye View of Synthetic Gradients”, Greydanus2016
  • “Learning to Reinforcement Learn”, Wang et al 2016
  • “Learning to Learn without Gradient Descent by Gradient Descent”, Chen et al 2016
  • “RL²: Fast Reinforcement Learning via Slow Reinforcement Learning”, Duan et al 2016
  • “Designing Neural Network Architectures Using Reinforcement Learning”, Baker et al 2016
  • “Using Fast Weights to Attend to the Recent Past”, Ba et al 2016
  • “HyperNetworks”, Ha et al 2016
  • “Decoupled Neural Interfaces Using Synthetic Gradients”, Jaderberg et al 2016
  • “Learning to Learn by Gradient Descent by Gradient Descent”, Andrychowicz et al 2016
  • “Matching Networks for One Shot Learning”, Vinyals et al 2016
  • “Learning to Optimize”, Li & Malik2016
  • “One-Shot Learning With Memory-Augmented Neural Networks”, Santoro et al 2016
  • “Adaptive Computation Time for Recurrent Neural Networks”, Graves2016
  • “On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, Schmidhuber2015
  • “Gradient-Based Hyperparameter Optimization through Reversible Learning”, Maclaurin et al 2015
  • “Machine Teaching: an Inverse Problem to Machine Learning and an Approach Toward Optimal Education”, Zhu2015b
  • “Human-Level Concept Learning through Probabilistic Program Induction”, Lake2015
  • “Robots That Can Adapt like Animals”, Cully et al 2014
  • “Deep Learning in Neural Networks: An Overview”, Schmidhuber2014
  • “Practical Bayesian Optimization of Machine Learning Algorithms”, Snoek et al 2012
  • “Optimal Ordered Problem Solver (OOPS)”, Schmidhuber2002
  • “Learning to Learn Using Gradient Descent”, Hochreiter et al 2001
  • “On the Optimization of a Synaptic Learning Rule”, Bengio et al 1997
  • “Interactions between Learning and Evolution”, Ackley & Littman1992
  • “Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks”, Schmidhuber1992
  • “Learning a Synaptic Learning Rule”, Bengio et al 1991
  • “Reinforcement Learning: An Introduction § Designing Reward Signals”, Sutton & Barto2024 (page 491)
  • “Exploring Hyperparameter Meta-Loss Landscapes With Jax”
  • “Metalearning”
  • “Universal Search § OOPS and Other Incremental Variations”
  • “Extrapolating to Unnatural Language Processing With GPT-3’s In-Context Learning: The Good, the Bad, and the Mysterious”
  • “How Does In-Context Learning Work? A Framework for Understanding the Differences from Traditional Supervised Learning”
  • “Rapid Motor Adaptation for Legged Robots”
  • “Collaborating With Humans Requires Understanding Them”
  • “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability [Blog]”
  • “Hypernetworks [Blog]”, Ha2024
  • “Action and Perception As Divergence Minimization”
  • “AlphaStar: Mastering the Real-Time Strategy Game StarCraft II”
  • “Prefrontal Cortex As a Meta-Reinforcement Learning System [Blog]”
  • “The Lie Comes First, the Worlds to Accommodate It”
  • “Sgdstore/experiments/omniglot at Master”
  • “Curriculum For Reinforcement Learning”
  • “Neural Architecture Search”
  • “MetaGenRL: Improving Generalization in Meta Reinforcement Learning”
  • “2022: 25-Year Anniversary: LSTM (1997_27ya), All Computable Metaverses, Hierarchical Q-Learning, Adversarial Intrinsic Reinforcement Learning, Low-Complexity NNs, Low-Complexity Art, Meta-RL, Soccer Learning”
  • “Metalearning or Learning to Learn Since 1987”
  • “The Future of Artificial Intelligence Is Self-Organizing and Self-Assembling”
  • “Domain-Adaptive Meta-Learning”
  • “How to Fix Reinforcement Learning”
  • “Introducing Adept”
  • “Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes”
  • “Risks from Learned Optimization: Introduction”
  • “How Good Are LLMs at Doing ML on an Unknown Dataset?”
  • “Early Situational Awareness and Its Implications, a Story”
  • “AI Is Learning How to Create Itself”
  • “Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind”
  • “SMASH: One-Shot Model Architecture Search through HyperNetworks [Video]”
  • “Solving Rubik’s Cube With a Robot Hand: Perturbations”
  • “WELM”
  • Wikipedia
• Miscellaneous
• Bibliography

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