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

  • The Complexity of Agreement [link]
  • Superintelligence: Paths, Dangers, Strategies [link]

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

  • Image Synthesis with a Single (Robust) Classifier [link]
  • Agreeing to Disagree [link]

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]