“ICML 2019 Notes”, 2019-06 (; similar):
The 2019 ICML edition of David Abel’s famous conference notes: he goes to as many presentations and talks as possible, jotting down opinionated summaries & equations, with a particular focus on DRL. Topics covered:
Tutorial: PAC-Bayes Theory (Part II) · PAC-Bayes Theory · PAC-Bayes and Task Awareness · Tutorial: Meta-Learning · Two Ways to View Meta-Learning · Meta-Learning Algorithms · Meta-Reinforcement Learning · Challenges and Frontiers in Meta Learning · Tuesday June: Main Conference Best Paper Talk: Challenging Assumptions in Learning Disentangled Representations Contributed Talks: Deep RL · DQN and Time Discretization · Nonlinear Distributional Gradient TD Learning · Composing Entropic Policies using Divergence Correction · TibGM: A Graphical Model Approach for RL · Multi-Agent Adversarial IRL · Policy Consolidation for Continual RL · Off-Policy Evaluation Deep RL w/o Exploration · Random Expert Distillation · Revisiting the Softmax Bellman Operator · Contributed Talks: RL Theory · Distributional RL for Efficient Exploration · Optimistic Policy Optimization via Importance Sampling · Neural Logic RL · Learning to Collaborate in MDPs · Predictor-Corrector Policy Optimization · Learning a Prior over Intent via Meta IRL · DeepMDP: Learning Late Space Models for RL · Importance Sampling Policy Evaluation · Learning from a Learner · Separating Value Functions Across Time-Scales · Learning Action Representations in RL · Bayesian Counterfactual Risk Minimization · Per-Decision Option Counting · Problem Dependent Regret Bounds in RL · A Theory of Regularized MDPs · Discovering Options for Exploration by Minimizing Cover Time · Policy Certificates: Towards Accountable RL · Action Robust RL · The Value Function Polytope · Wednesday June: Main Conference Contributed Talks: Multitask and Lifelong Learning · Domain Agnostic Learning with Disentangled Representations · Composing Value Functions in RL · CAVIA: Fast Context Adaptation via Meta Learning · Gradient Based Meta-Learning · Towards Understanding Knowledge Distillation · Transferable Adversarial Training · Contributed Talks: RL Theory · Provably Efficient Imitation Learning from Observation Alone · Dead Ends and Secure Exploration · Statistics and Samples in Distributional RL · Hessian Aided Policy Gradient · Maximum Entropy Exploration · Combining Multiple Models for Off-Policy Evaluation · Sample-Optimal ParametricQ-Learning Using Linear Features · Transfer of Samples in Policy Search · Exploration Conscious RL Revisited · Kernel Based RL in Robust MDPs · Thursday June: Main Conference Contributed Talks: RL · Batch Policy learning under Constraints · Quantifying Generalization in RL · Learning Latent Dynamics for Planning from Pixels · Projections for Approximate Policy Iteration · Learning Structured Decision Problems with Unawareness · Calibrated Model-Based Deep RL · RL in Configurable Continuous Environments · Target-Based Temporal-Difference Learning · Linearized Control: Stable Algorithms and Complexity Guarantees · Contributed Talks: Deep Learning Theory · Why do Larger Models Generalize Better? · On the Spectral Bias of Neural Nets · Recursive Sketches for Modular Deep Learning · Zero-Shot Knowledge Distillation in Deep Networks · Convergence Theory for Deep Learning via Over-Parameterization · Best Paper Award: Rates of Convergence for Sparse Gaussian Process Regression · Friday June: Workshops Workshop: AI for Climate Change · John Platt on What ML can do to help Climate Change · Jack Kelly: Why It’s Hard to Mitigate Climate Change, and How to Do Better, Andrew Ng: Tackling Climate Change with AI through Collaboration · Workshop: RL for Real Life · Panel Discussion · Workshop: Real World Sequential Decision Making · Emma Brunskill on Efficient RL When Data is Costly · Miro Dudik: Doubly Robust Off-Policy Evaluation via Shrinkage
View PDF: