Readings
List of things I’ve been reading. Only adding things that I’ve read thoroughly and understand beyond a superficial level.
11/4/22
- Reinforcement Learning by Reward-Weighted Regression for Operational Space Control [link]
10/22/2022
- Precis of The Limits of Morality [link]
10/20/2022
- CHOMP: Covariant Hamiltonian optimization for motion planning [link]
10/19/2022
- Probabilistic Plan Recognition using off-the-shelf Classical Planners [link]
10/18/2022
- Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning [link]
- Modeling Strong and Human-Like Gameplay with KL-Regularized Search [link]
10/17/2022
- Human-Level Performance in No-Press Diplomacy via Equilibrium Search [link]
- No Press Diplomacy: Modeling Multi-Agent Gameplay [link]
10/11/2022
- Policy Gradient Bayesian Robust Optimization for Imitation Learning [link]
10/10/2022
- Bayesian Inverse Reinforcement Learning [link]
9/19/2022
- Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market [link]
9/18/2022
- An Extensible Interactive Interface for Reward Design [link]
9/17/2022
- Social Simulacra: Creating Populated Prototypes for Social Computing Systems [link]
9/13/2022
- Retrospective on the 2021 BASALT Competition on Learning From Human Feedback [link]
9/12/2022
- Learning with Not Enough Data Part 3: Data Generation [link]
9/10/2022
- Simulators on Lesswrong [link]
9/5/2022
- Trust Region Policy Optimization [link]
8/29/2022
- Reward-rational (implicit) choice: A unifying formalism for reward learning [link]
- The MineRL BASALT Competition on Learning from Human Feedback [link]
8/28/2022
- Active Preference-Based Learning of Reward Functions [link]
8/24/2022
- Deep Reinforcement Learning from Human Preferences [link]
8/16/2022
- Too many cooks: Bayesian inference for coordinating multi-agent collaboration [link]
8/15/2022
- Open Problems in Cooperative AI [link]
8/11/2022
- Distribution-Free Predictive Inference for Regression [link]
- Aaditya Ramdas’ Tutorial Talk on Conformal Prediction [link]
8/10/2022
- Improving Reproducibility in Machine Learning Research [link]
8/9/2022
- A tutorial on conformal prediction [link]
8/6/2022
- Beyond Bayesian and Frequentists [link]
5/20/2022
- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision [link]
5/19/2022
- Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding [link]
5/18/2022
- Curiosity-driven Exploration by Self-supervised Prediction [link]
- VIME: Variational Information Maximizing Exploration [link]
5/17/2022
- Adaptive Computation Time for Recurrent Neural Networks [link]
5/16/2022
- Embodied Question Answering [link]
5/13/2022
- Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot [link]
5/11/2022
- Convolutional Dynamic Alignment Networks for Interpretable Classifications [link]
- Optimizing for Interpretability in Deep Neural Networks with Tree Regularization [link]
- Explainable Models with Consistent Interpretations [link]
4/27/2022
4/25/2022
- Geometric Deep Learning - Grids, Groups, Graphs, Geodesics, and Gauges: Chapters 4.1, 5.3, 5.4 [link]
4/24/2022
- Adversarial Examples Are Not Bugs, They Are Features [link]
4/23/2022
4/18/2022
- Identifying Statistical Bias in Dataset Replication [link]
4/16/2022
- What Can Neural Networks Reason About? [link]
4/15/2022
- Cooperative Inverse Reinforcement Learning [link]
- Editing a classifier by rewriting its prediction rules [link]
4/11/2022
- Geometric Deep Learning - Grids, Groups, Graphs, Geodesics, and Gauges, Chapters 1-3 [link]
4/9/2022
- The Old Man and the Sea by Ernest Hemingway
3/28/2022
- Convergence Analysis of Two-layer Neural Networks with ReLU Activation [link]
3/7/2022
- A Gentle Introduction to Graph Neural Networks [link]
- A General Formula on the Conjugate of the Difference of Functions [link]
- Generalized Convexity and Fractional Programming with Economic Applications: On Strongly Convex and Paraconvex Dualities [link]
3/5/2022
- Group Equivariant Convolutional Networks [link]
3/2/2022
- Model Inversion Networks for Model-Based Optimization [link]
- Conservative Objective Models for Effective Offline Model-Based Optimization [link]
3/1/2022
- Consequences of Misaligned AI [link]
- Future of Life Institute AI Alignment Podcast: Inverse Reinforcement Learning and Inferring Human Preferences with Dylan Hadfield-Menell [link]
2/21/2022
- A unified framework for Hamiltonian deep neural networks [link]
- Stable Architectures for Deep Neural Networks [link]
2/17/2022
- Rationality AI to Zombies: How to Actually Change Your Mind [link]
- Generally Intelligent #10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI [link]
2/15/2022
- Algorithms for Inverse Reinforcement Learning [link]
2/14/2022
- Training language models to follow instructions with human feedback [link]
- Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [link]
- Towards a Rigorous Science of Interpretable Machine Learning [link]
2/13/2022
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [link]
- Concept Whitening for Interpretable Image Recognition [link]
- NBDT: Neural-Backed Decision Trees [link]
2/12/2022
- Rationality AI to Zombies: Map and Territory [link]
2/9/2022
- Poincaré Embeddings for Learning Hierarchical Representations [link]
2/4/2022
- This Looks Like That: Deep Learning for Interpretable Image Recognition [link]
- Towards Robust Interpretability with Self-Explaining Neural Networks [link]
1/31/2022
- A Unified Approach to Interpreting Model Predictions [link]
1/10/2022
- Concept Bottleneck Models [link]
1/3/2022
- Why Should I Trust You? Explaining the Predictions of Any Classifier [link]
- How to Explain Individual Classification Decisions [link]
- How to Explain the Prediction of a Machine Learning Model [link]
12/28/2021
- Learning Structured Output Representation using Deep Conditional Generative Models [link]
12/27/2021
- Genesis 1-7
12/25/2021
- Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions [link]
12/23/2021
- Tensor Algebra and Tensor Analysis for Engineers, Chapter 1: Vectors and Tensors in a Finite-Dimensional Space [link]
12/22/2021
- Dual Space Preconditioning for Gradient Descent [link]
12/21/2021
- Generally Intelligent #12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement [link]
12/20/2021
- Monte Carlo Statistical Methods, Chapter 6: Markov Chains [link]