Inspired by Adrian Colyer and Denny Britz.

This contains my notes for research papers that I've read. Papers are arranged according to three broad categories and then further numbered on a (1) to (5) scale where a (1) means I have only barely skimmed it, while a (5) means I feel confident that I understand almost everything about the paper. Within a single year, these papers should be organized according to publication date. The links here go to my paper summaries if I have them, otherwise those papers are on my TODO list.

Contents:

• Reinforcement and Imitation Learning
  • 2019 Papers
  • 2018 Papers
  • 2017 Papers
  • 2016 Papers
  • 2015 Papers
  • 2014 and Earlier
• Deep Learning
  • 2019 Papers
  • 2018 Papers
  • 2017 Papers
  • 2016 Papers
  • 2015 Papers
  • 2014 and Earlier
• Miscellaneous
  • 2019 Papers
  • 2018 Papers
  • 2017 Papers
  • 2016 Papers
  • 2015 Papers
  • 2014 Papers
  • 2013 and Earlier

• Extending Deep MPC with Safety Augmented Value Estimation from Demonstrations, arXiv 2019 (3)
• Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction, arXiv 2019 (1)
• SQIL: Imitation Learning via Regularized Behavioral Cloning, arXiv 2019 (1)
• Towards Characterizing Divergence in Deep Q-Learning, arXiv 2019 (1)
• Skew-Fit: State-Covering Self-Supervised Reinforcement Learning, arXiv 2019 (1)
• Visual Hindsight Experience Replay, arXiv 2019 (1)
• Diagnosing Bottlenecks in Deep Q-Learning Algorithms, ICML 2019 (1)
• Efficient Off-Policy Meta-Reinforcement learning via Probabilistic Context Variables, ICML 2019 (1)
• Off-Policy Deep Reinforcement Learning Without Exploration ICML 2019 (5)

Early-year

• Model-Based Reinforcement Learning for Atari, arXiv 2019 (1)
• Reinforcement Learning from Imperfect Demonstrations, arXiv 2019 (1)
• Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control, arXiv (4)
• Residual Reinforcement Learning for Robot Control, ICRA 2019 (3)
• Memory Efficient Experience Replay for Streaming Learning, ICRA 2019 (1)
• Learning Curriculum Policies for Reinforcement Learning, AAMAS 2019 (1)
• Variational Discriminator Bottleneck: Improving IL, IRL, and GANs by Constraining Information Flow, ICLR 2019 (1)
• Recurrent Experience Replay in Distributed Reinforcement Learning, ICLR 2019 (1)
• InfoBot: Transfer and Exploration via the Information Bottleneck, ICLR 2019 (1)
• Large-Scale Study of Curiosity-Driven Learning, ICLR 2019 (4)
• Exploration by Random Network Distillation, ICLR 2019 (4)
• Automatically Composing Representation Transformations as a Means for Generalization, ICLR 2019 (2)
• Diversity is All You Need: Learning Skills without a Reward Function, ICLR 2019 (4)
• Distilling Policy Distillation, AISTATS 2019 (1)
• Multi-task Deep Reinforcement Learning with PopArt, AAAI 2019 (4)

Late-year

• Simple Random Search Provides a Competitive Approach to Reinforcement Learning, NeurIPS 2018 (5)
• Learning to Play with Intrinsically-Motivated Self-Aware Agents NeurIPS 2018 (2)
• Reward Learning from Human Preferences and Demonstrations in Atari, NeurIPS 2018 (3)
• Improving Exploration in ES for DeepRL via a Population of Novelty-Seeking Agents, NeurIPS 2018 (3)
• Visual Reinforcement Learning with Imagined Goals, NeurIPS 2018 (1)
• Probabilistic Model-Agnostic Meta-Learning, NeurIPS 2018 (1)
• Playing Hard Exploration Games by Watching YouTube, NeurIPS 2018 (1)
• Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models, NeurIPS 2018 (1)
• Sim-to-Real Reinforcement Learning for Deformable Object Manipulation, CoRL 2018 (4)
• GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning, CoRL 2018 (1)
• Learning Actionable Representations from Visual Observations, IROS 2018 (4)
• Natural Environment Benchmarks for Reinforcement Learning, arXiv 2018 (1)

Mid-year

• Learning Instance Segmentation by Interaction, CVPR 2018 (1)
• Learning by Playing –- Solving Sparse Reward Tasks from Scratch, ICML 2018 (1)
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures, ICML 2018 (2)
• Universal Planning Networks, ICML 2018 (1)
• Hierarchical Imitation and Reinforcement Learning, ICML 2018 (1)
• Progress & Compress: A Scalable Framework for Continual Learning, ICML 2018 (1)
• Policy Optimization with Demonstrations, ICML 2018 (1)
• Investigating Human Priors for Playing Video Games, ICML 2018 (3)
• Self-Imitation Learning, ICML 2018 (4)
• Automatic Goal Generation for Reinforcement Learning Agents, ICML 2018 (4)
• Reinforcement and Imitation Learning for Diverse Visuomotor Skills, RSS 2018 (2)
• One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning, RSS 2018 (4)
• Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, RSS 2018 (4)
• Asymmetric Actor Critic for Image-Base Robot Learning, RSS 2018 (5)
• Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning, RSS 2018 (1)
• Kickstarting Deep Reinforcement Learning, arXiv 2018 (5)
• Observe and Look Further: Achieving Consistent Performance on Atari, arXiv 2018 (4)

Early-year

• Time-Contrastive Networks: Self-Supervised Learning from Video, ICRA 2018 (1)
• Neural Network Dynamics for Model-Based Deep RL with Model-Free Fine-Tuning, ICRA 2018 (5)
• Learning Robotic Assembly from CAD, ICRA 2018 (3)
• CASSL: Curriculum Accelerated Self-Supervised Learning, ICRA 2018 (5)
• Neural Task Programming: Learning to Generalize Across Hierarchical Tasks, ICRA 2018 (2)
• Overcoming Exploration in Reinforcement Learning with Demonstrations, ICRA 2018 (5)
• Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation, ICRA 2018 (3)
• Parameterized Hierarchical Procedures for Neural Programming, ICLR 2018 (4)
• Meta Learning Shared Hierarchies, ICLR 2018 (5)
• Divide-and-Conquer Reinforcement Learning, ICLR 2018 (3)
• Zero-Shot Visual Imitation, ICLR 2018 (4)
• Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play, ICLR 2018 (3)
• Emergent Complexity via Multi-Agent Competition, ICLR 2018 (3)
• Progressive Reinforcement Learning With Distillation For Multi-Skilled Motion Control, ICLR 2018 (4)
• Model-Ensemble Trust-Region Policy Optimization, ICLR 2018 (3)
• Distributed Prioritized Experience Replay, ICLR 2018 (4)
• Learning to Multi-Task by Active Sampling, ICLR 2018 (1)
• Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning, ICLR 2018 (1)
• Rainbow: Combining Improvements in Deep Reinforcement Learning, AAAI 2018 (4)
• Deep Q-learning from Demonstrations, AAAI 2018 (5)
• Deep Reinforcement Learning that Matters, AAAI 2018 (4)

Late-year

• Deep Reinforcement Learning from Human Preferences, NeurIPS 2017 (3)
• One-Shot Imitation Learning, NeurIPS 2017 (4)
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning, NeurIPS 2017 (4)
• Robust Imitation of Diverse Behaviors, NeurIPS 2017 (3)
• Bridging the Gap Between Value and Policy Based Reinforcement Learning, NeurIPS 2017 (2)
• Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs, NeurIPS 2017 (5)
• Hindsight Experience Replay, NeurIPS 2017 (5)
• Distral: Robust Multitask Reinforcement Learning, NeurIPS 2017 (1)
• DART: Noise Injection for Robust Imitation Learning, CoRL 2017 (3)
• Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences, CoRL, 2017 (3)
• DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations, CoRL 2017 (5)
• Reverse Curriculum Generation for Reinforcement Learning, CoRL 2017 (5)
• One-Shot Visual Imitation Learning via Meta-Learning, CoRL 2017 (5)
• Sim-to-Real Robot Learning from Pixels with Progressive Nets, CoRL 2017 (1)
• Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task, CoRL 2017 (1)
• Visual Semantic Planning using Deep Successor Representations, ICCV 2017 (4)
• Proximal Policy Optimization Algorithms, arXiv (4)
• Learning Human Behaviors From Motion Capture by Adversarial Imitation, arXiv (3)
• The Uncertainty Bellman Equation and Exploration, arXiv (1)
• Multi-Level Discovery of Deep Options, arXiv (5)
• Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards, arXiv (5)

Mid-year

• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World, IROS 2017 (4)
• Virtual to Real Reinforcement Learning for Autonomous Driving, BMVC 2017 (3)
• ReasoNet: Learning to Stop Reading in Machine Comprehension, KDD 2017 (3)
• Inverse Reinforcement Learning via Deep Gaussian Process, UAI 2017 (2)
• Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning, ICML 2017 (1)
• Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability, ICML 2017 (1)
• Reinforcement Learning with Deep Energy-Based Policies, ICML 2017 (1)
• A Distributional Perspective on Reinforcement Learning, ICML 2017 (2)
• Robust Adversarial Reinforcement Learning, ICML 2017 (5)
• Modular Multitask Reinforcement Learning with Policy Sketches, ICML 2017 (4)
• End-to-End Differentiable Adversarial Imitation Learning, ICML 2017 (4)
• Constrained Policy Optimization, ICML 2017 (2)
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, ICML 2017 (4)
• Curiosity-Driven Exploration by Self-Supervised Prediction, ICML 2017 (4)
• Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access, ACL 2017 (3)
• Unsupervised Perceptual Rewards for Imitation Learning, RSS 2017 (1)
• Loss is its own Reward: Self-Supervision for Reinforcement Learning, arXiv (2)
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning, arXiv (5)

Early-year

• Imitating Driver Behavior with Generative Adversarial Networks, Intelligent Vehicles (IV), 2017 (4)
• Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR 2017 (3)
• Learning to Repeat: Fine-Grained Action Repetition for Deep Reinforcement Learning, ICLR 2017 (4)
• Learning to Act by Predicting the Future, ICLR 2017 (4)
• Learning Visual Servoing with Deep Features and Fitted Q-Iteration, ICLR 2017 (2)
• Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critic, ICLR 2017 (2)
• Stochastic Neural Networks for Hierarchical Reinforcement Learning, ICLR 2017 (4)
• Generalizing Skills With Semi-Supervised Reinforcement Learning ICLR 2017 (3)
• Third-Person Imitation Learning, ICLR 2017 (3)
• Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates ICRA 2017 (5)
• Target-Driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning, ICRA 2017 (5)
• Supervision via Competition: Robot Adversaries for Learning Tasks, ICRA 2017 (4)
• Deep Visual Foresight for Planning Robot Motion, ICRA 2017 (3)
• Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demos, ICRA 2017 (4)
• Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation, ICRA 2017 (5)
• Learning to Push by Grasping: Using Multiple Tasks for Effective Learning, ICRA 2017 (4)
• Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer, ICRA 2017 (4)
• Dynamic Action Repetition for Deep Reinforcement Learning, AAAI 2017 (5)
• Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay, AAAI 2017 (4)
• A Deep Hierarchical Approach to Lifelong Learning in Minecraft, AAAI 2017 (4)
• RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning, arXiv (4)
• Learning to Predict Where to Look in Interactive Environments Using Deep Recurrent Q-Learning, arXiv (3)

• Learning to Poke by Poking: Experiential Learning of Intuitive Physics, NeurIPS 2016 (5)
• Value Iteration Networks, NeurIPS 2016 (4)
• Generative Adversarial Imitation Learning, NeurIPS 2016 (3)
• VIME: Variational Information Maximizing Exploration, NeurIPS 2016 (3)
• Unsupervised Learning for Physical Interaction through Video Prediction, NeurIPS 2016 (1)
• Deep Exploration via Bootstrapped DQN, NeurIPS 2016 (3)
• Unifying Count-Based Exploration and Intrinsic Motivation, NeurIPS 2016 (1)
• Principled Option Learning in Markov Decision Processes, EWRL 2016 (4)
• Taming the Noise in Reinforcement Learning via Soft Updates, UAI 2016 (4)
• Deep Successor Reinforcement Learning, arXiv 2016 (4)
• Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning, SIGGRAPH 2016 (3)
• Asynchronous Methods for Deep Reinforcement Learning, ICML 2016 (4)
• Benchmarking Deep Reinforcement Learning for Continuous Control, ICML 2016 (4)
• Model-Free Imitation Learning with Policy Optimization, ICML 2016 (4)
• Graying the Black Box: Understanding DQNs, ICML 2016 (4)
• Control of Memory, Active Perception, and Action in Minecraft, ICML 2016 (2)
• Dueling Network Architectures for Deep Reinforcement Learning, ICML 2016 (4)
• Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, ICML 2016 (2)
• Policy Distillation, ICLR 2016 (5)
• Learning Visual Predictive Models of Physics for Playing Billiards, ICLR 2016 (4)
• Prioritized Experience Replay, ICLR 2016 (4)
• High-Dimensional Continuous Control Using Generalized Advantage Estimation, ICLR 2016 (4)
• Continuous Control with Deep Reinforcement Learning, ICLR 2016 (4)
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, ICLR 2016 (2)
• Deep Spatial Autoencoders for Visuomotor Learning, ICRA 2016 (3)
• Learning Deep Neural Network Policies with Continuous Memory States, ICRA 2016 (2)
• End-to-End Training of Deep Visuomotor Policies, JMLR 2016 (2)
• Learning the Variance of the Reward-To-Go, JMLR 2016 (3)
• Deep Reinforcement Learning with Double Q-learning, AAAI 2016 (5)
• Mastering the Game of Go with Deep Neural Networks and Tree Search, Nature 2016 (1)

• Action-Conditional Video Prediction using Deep Networks in Atari Games, NeurIPS2015 (4)
• Gradient Estimation Using Stochastic Computation Graphs, NeurIPS 2015 (1)
• Learning Continuous Control Policies by Stochastic Value Gradients, NeurIPS 2015 (1)
• Deep Attention Recurrent Q-Network, NeurIPS Workshop 2015 (3)
• Deep Recurrent Q-Learning for Partially Observable MDPs, AAAI-SDMIA 2015 (5)
• Trust Region Policy Optimization, ICML 2015 (4)
• Probabilistic Inference for Determining Options in Reinforcement Learning, ICML Workshop 2015 (3)
• Massively Parallel Methods for Deep Reinforcement Learning, ICML Workshop 2015 (2)
• Human-Level Control Through Deep Reinforcement Learning, Nature 2015 (5)

• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, NeurIPS 2014 (3)
• Learning Neural Network Policies with Guided Policy Search Under Unknown Dynamics, NeurIPS 2014 (1)
• Deterministic Policy Gradient Algorithms, ICML 2014 (2)
• Playing Atari with Deep Reinforcement Learning, NeurIPS Workshop 2013 (5)
• Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning, F&T ML 2013 (4)
• A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning, AISTATS 2011 (3)
• Efficient Reductions for Imitation Learning, AISTATS 2010 (3)
• Maximum Entropy Inverse Reinforcement Learning, AAAI 2008 (4)
• Improving Generalisation for Temporal Difference Learning the Successor Representation, N. Computation 1993 (2)
• Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, M. Learning 1992 (2)
• Active Perception and Reinforcement Learning, N. Computation 1990 (3)

• On The Power of Curriculum Learning in Training Deep Neural Networks, ICML 2019 (1)

• Stochastic Adversarial Video Prediction, arXiv 2018 (3)
• Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels, NeurIPS 2018 (1)
• Learning to Teach With Dynamic Loss Functions, NeurIPS 2018 (2)
• Skill Rating for Generative Models, arXiv 2018 (3)
• Born Again Neural Networks, ICML 2018 (5)
• Large Scale Distributed Neural Network Training Through Online Distillation, ICLR 2018 (1)
• Learning to Teach, ICLR 2018 (5)
• Interpretable and Pedagogical Examples arXiv 2018 (5)

• Continual Learning with Deep Generative Replay, NeurIPS 2017 (3)
• Attention is All You Need, NeurIPS 2017 (4)
• Dynamic Routing Between Capsules, NeurIPS 2017 (1)
• Tensor Regression Networks, arXiv 2017 (2)
• One Model to Learn Them All, arXiv 2017 (3)
• Population Based Training of Neural Networks, arXiv 2017 (5)
• Automated Curriculum Learning for Neural Networks, ICML 2017 (3)
• Wasserstein GAN, ICML 2017 (3)
• Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo, ICML 2017 (2)
• Get To The Point: Summarization with Pointer-Generator Networks, ACL 2017 (3)
• Adversarial Discriminative Domain Adaptation, CVPR 2017 (4)
• Towards Principled Methods for Training Generative Adversarial Networks, ICLR 2017 (1)
• Unrolled Generative Adversarial Networks, ICLR 2017 (3)
• Understanding Deep Learning Requires Rethinking Generalization, ICLR 2017 (5)
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, ICLR 2017 (2)
• Do Deep Convolutional Nets Really Need to be Deep and Convolutional?, ICLR 2017 (3)
• Overcoming Catastrophic Forgetting in Neural Networks, PNAS 2017 (1)

• NeurIPS 2016 Tutorial: Generative Adversarial Networks, arXiv (4)
• Improving Variational Autoencoders with Inverse Autoregressive Flow, NeurIPS 2016 (1)
• Conditional Image Generation with PixelCNN Decoders, NeurIPS 2016 (2)
• Using Fast Weights to Attend to the Recent Past, NeurIPS 2016 (2)
• Improved Techniques for Training GANs, NeurIPS 2016 (3)
• InfoGAN: Interpretable Representation Learning by Information Maximizing GANs, NeurIPS 2016 (2)
• WaveNet: A Generative Model for Raw Audio, arXiv (1)
• Tutorial on Variational Autoencoders, arXiv (3)
• Active Long Term Memory Networks, arXiv (3)
• Progressive Neural Networks, arXiv 2016 (4)
• Deep Residual Learning for Image Recognition, CVPR 2016 (1)
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, ICML 2016 (2)
• Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images, IJCV 2016 (1)
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, ICLR 2016 (2)
• A Note on the Evaluation of Generative Models, ICLR 2016 (3)
• Neural Programmer-Interpreters, ICLR 2016 (1)
• Visualizing and Understanding Recurrent Networks, ICLR Workshop 2016 (1)
• Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks, AAAI 2016 (3)
• Attention and Augmented Recurrent Neural Networks, Distill (3)

• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, ICCV 2015 (2)
• Spatial Transformer Networks, NeurIPS 2015 (4)
• Effective Approaches to Attention-based Neural Machine Translation, EMNLP 2015 (3)
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ICML 2015 (4)
• Siamese Neural Networks for One-shot Image Recognition, ICML 2015 (4)
• DRAW: A Recurrent Neural Network For Image Generation, ICML 2015 (2)
• Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, ICML 2015 (3)
• Going Deeper with Convolutions, CVPR 2015 (1)
• Deep Visual-Semantic Alignments for Generating Image Descriptions, CVPR 2015 (3)
• Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015 (1)
• ADAM: A Method for Stochastic Optimization, ICLR 2015 (2)
• Explaining and Harnessing Adversarial Examples, ICLR 2015 (2)

• Conditional Generative Adversarial Nets, arXiv 2014 (5)
• Recurrent Neural Network Regularization, arXiv 2014 (1)
• Distilling the Knowledge in a Neural Network, DL workshop NeurIPS 2014 (4)
• Generative Adversarial Nets, NeurIPS 2014 (5)
• Recurrent Models of Visual Attention, NeurIPS 2014 (4)
• Visualizing and Understanding Convolutional Networks, ECCV 2014 (3)
• Auto-Encoding Variational Bayes, ICLR 2014 (3)
• On the Importance of Initialization and Momentum in Deep Learning, ICML 2013 (2)
• ImageNet Classification with Deep Convolutional Neural Networks, NeurIPS 2012 (5)
• Large Scale Distributed Deep Networks, NeurIPS 2012 (1)
• Training Deep and Recurrent Networks With Hessian-Free Optimization, NNs Tricks of the Trade, 2012 (1)
• Deep Learning via Hessian-Free Optimization, ICML 2010 (2)
• Curriculum Learning, ICML 2009 (5)
• A Fast Learning Algorithm for Deep Belief Nets, Neural Computation 2006 (1)

(Mostly about MCMC, Machine Learning, and/or Robotics.)

• Picking Towels in Point Clouds, Sensors 2019 (1)

• Cloth Manipulation Using Random-Forest-Based Controller Parametrization, arXiv 2018 (1)
• Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN, arXiv 2018 (4)
• Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias, NeurIPS 2018 (5)
• Establishing Appropriate Trust via Critical States, IROS 2018 (1)
• Learning to See Forces: Surgical Force Prediction w/RGB-Point Cloud Temporal CNNs, MICCAI 2018 (3)
• Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary, ICRA 2018 (1)
• Towards Black-box Iterative Machine Teaching, ICML 2018 (1)
• Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets using Analytic Model and DL, ICRA 2018 (4)
• Learning Robust Bed Making using Deep Imitation Learning with Dart, arXiv 2018 (5)
• An Overview of Machine Teaching, arXiv 2018 (5)

• Machine Teaching: A New Paradigm for Building Machine Learning Systems, arXiv 2017 (5)
• Learning to Fly by Crashing, IROS 2017 (5)
• A Vision-Guided Multi-Robot Cooperation Framework for L-by-Demos and Task Reproduction, IROS 2017 (1)
• Mini-batch Tempered MCMC, arXiv 2017 (3)
• Using dVRK Teleoperation to Facilitate Deep Learning of Automation Tasks for an Industrial Robot, CASE 2017 (4)
• Magnetic Hamiltonian Monte Carlo, ICML 2017 (1)
• Iterative Machine Teaching, ICML 2017 (3)
• Dex-Net 2.0: DL to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics, RSS 2017 (3)
• In-Datacenter Performance Analysis of a Tensor Processing Unit, ISCA 2017 (1)
• Multilateral Surgical Pattern Cutting in 2D Orthotropic Gauze w/DeepRL Policies for Tensioning, ICRA 2017 (5)
• Autonomous Suturing: An Algorithm for Optimal Selection of Needle Diameter, Shape, and Path, ICRA 2017 (4)
• C-LEARN: Geometric Constraints from Demos for Multi-Step Manipulation in Shared Autonomy, ICRA 2017 (3)
• Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, IJRR (5)
• On Markov Chain Monte Carlo Methods for Tall Data, JMLR 2017 (3)
• A Conceptual Introduction to Hamiltonian Monte Carlo, arXiv (4)
• Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects, IEEE TASE 2017 (3)

• SWIRL: A Sequential Windowed IRL Algorithm for Robot Tasks With Delayed Rewards, WAFR 2016 (2)
• Minimum-Information LQG Control Part I: Memoryless Controllers, CDC 2016 (2)
• Minimum-Information LQG Control Part II: Retentive Controllers, CDC 2016 (1)
• Adaptive Optimal Training of Animal Behavior, NeurIPS 2016 (3)
• Cooperative Inverse Reinforcement Learning, NeurIPS 2016 (3)
• Bayesian Optimization with Robust Bayesian Neural Networks, NeurIPS 2016 (2)
• Tumor Localization using Automated Palpation with Gaussian Process Adaptive Sampling, CASE 2016 (3)
• Robot Grasping in Clutter: Using a Hierarchy of Supervisors for Learning from Demonstrations, CASE 2016 (4)
• Gradient Descent Converges to Minimizers, COLT 2016 (3)
• Scalable Discrete Sampling as a Multi-Armed Bandit Problem, ICML 2016 (1)
• Supersizing Self-Supervision: Learning to Grasp from 50K Tries and 700 Robot Hours, ICRA 2016 (5)
• Dex-Net 1.0: A Cloud-Based Network of 3D Objects for R. Grasp Planning Using a M-A Bandit w/CR, ICRA 2016 (4)
• TSC-DL: Unsupervised Trajectory Segmentation of Multi-Modal Surgical Demonstrations with DL, ICRA 2016 (3)
• Automating Multi-Throw Multilateral Surgical Suturing with a Mechanical Needle Guide and SCO, ICRA 2016 (4)
• Supervised Autonomous Robotic Soft Tissue Surgery, Science Translational Medicine, 2016 (3)

• Bayesian Dark Knowledge, NeurIPS 2015 (2)
• A Complete Recipe for Stochastic Gradient MCMC, NeurIPS 2015 (2)
• Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC, KDD 2015 (1)
• The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling, ICML 2015 (2)
• LbO Surgical Subtasks: Multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantoms, ICRA 2015 (4)

• Eliciting Good Teaching From Humans for Machine Learners, Artificial Intelligence 2014 (4)
• Bimanual Telerobotic Surgery With Asymmetric Force Feedback: a da vinci Implementation, IROS 2014 (3)
• Learning Accurate Kinematic Control of Cable-Driven Surgical Robots Using Data Cleaning and GPR, CASE 2014 (5)
• Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget, ICML 2014 (4)
• Stochastic Gradient Hamiltonian Monte Carlo, ICML 2014 (4)
• Hamiltonian Monte Carlo Without Detailed Balance, ICML 2014 (2)
• Towards Scaling up Markov Chain Monte Carlo: An Adaptive Subsampling Approach, ICML 2014 (4)
• Autonomous Multilateral Debridement with the Raven Surgical Robot, ICRA 2014 (5)
• Teaching People How to Teach Robots, HRI 2014 (4)
• RRE: A Game-Theoretic Intrusion Response and Recovery Engine, IEEE Trans on P&D Systems 2014 (4)

• A Case Study of Trajectory Transfer Through Non-Rigid Registration for a Simplified Suturing Scenario, IROS 2013 (2)
• Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization, RSS 2013 (3)
• Learning Task Error Models for Manipulation, ICRA 2013 (4)
• Training a Robot via Human Feedback: A Case Study, ICSR 2013 (1)
• Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring, ICML 2012 (2)
• Algorithmic and Human Teaching of Sequential Decision Tasks, AAAI 2012 (4)
• The Jacobian Condition Number as a Dexterity Index in 6R Machining Robots, RCIM 2012 (2)
• Bayesian Learning via Stochastic Gradient Langevin Dynaimcs, ICML 2011 (4)
• Bringing Clothing Into Desired Configurations with Limited Perception, ICRA 2011 (3)
• Visual Measurement of Suture Strain for Robotic Surgery, C&MM in Medicine 2011 (3)
• Towards an Assistive Robot that Autonomously Performs Bed Baths, IROS 2010 (4)
• Development of a Nursing-Care Assistant Robot RIBA, IROS 2010 (4)
• Cloth Grasp Point Detection Based on Multiple-View Geometric Cues w/Applic. to Towel Folding, ICRA 2010 (5)
• MCMC Using Hamiltonian Dynamics, Handbook of Markov Chain Monte Carlo 2010 (4)
• Active Perception: Interactive Manipulation for Improving Object Detection, Technical Report 2010 (3)
• Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from H-G Demos, ICRA 2010 (2)
• Active Learning for Real-Time Motion Controllers, SIGGRAPH 2007 (3)
• Core Knowledge, Developmental Science 2007 (3)
• Effect of Sensory Substitution on Suture-Manipulation Forces for Robot Surgery, J Thorac Cardiovasc Surg 2005 (3)
• Analysis of Suture Manipulation Forces for Teleoperation with Force Feedback, MICCAI 2002 (3)
• An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, Technical Report, 1994 (3)
• Active Perception, Proceedings of the IEEE 1988 (2)