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Pieter Abbeel
Robert Anderson
Peter Bartlett
Alexandre Bayen
John Canny
Trevor Darrell
Anca Dragan
Laurent El Ghaoui
Ronald Fearing
Ken Goldberg
Joseph Gonzalez
Alison Gopnik
Nika Haghtalab
Michael Jordan
Angjoo Kanazawa
Kurt Keutzer
Dan Klein
Sergey Levine
Michael Mahoney
Jitendra Malik
Kristofer Pister
Raluca Ada Popa
Stuart Russell
Sanjit Seshia
Dawn Song
Koushil Sreenath
Ion Stoica
Claire Tomlin
Vladimir Stojanovic
David Wagner
Martin Wainwright
Stella Yu
Avideh Zakhor
Results
Active Visual Planning: Handling Uncertainty in Perception, Prediction, and Planning Pipelines
Adding Safety and Robustness to Learning for Robots by Learning on Robots
Addressing Challenges in Large-scale Distributed AI Systems
Amazon-Berkeley Objects: A Large-Scale Dataset for 3D Object Understanding
Animating Animals from Video
Automated Collision Prediction in Autonomous Systems with Monocular Camera
Automating Multi-Agent Curriculum Learning with Probabilistic Programs
Autonomous Skill Discovery Through Self-Supervised Exploration
Better Visual Representations through Language Supervision
Combating Hallucination in Conditional Sequence Generation
Combining Causal Reasoning and Information Theory to Empower Humans through Human-Agent Collaboration
Compressing High Capacity Models with Implicit Neural Networks and Frank-Wolfe
Control of Microrobots with Data and Computationally Efficient Reinforcement Learning
Dimension-free Statistical and Computational Guarantee for Optimal Transport
Distributed Learning: Privacy and Data Summarization
Enabling Non-Experts to Annotate Complex Logical Forms at Scale
Factorized language representations with knowledge and logic
Formal Skill Representation and Composition for Task Generalization
Generalizing Domain Randomization for Zero-Shot Transfer
Grounded and Modular Vision and Language Learning
Hardware Software Co-Design for NLP and Recommendation Systems
Hierarchical Model-Based Reinforcement Learning with Temporal Abstractions
Interactive Learning from Vision and Touch
Investigations into the Complexity of Nonconvex Optimization
Knowledge Transferable Bayesian Optimization
Large-scale 3D Reconstruction from Multi-view Image Datasets
Learning From Play in Children and Robots: Who Can Train a Robot Better?
Learning Safety-Assured Collaborative Quadrupedal Manipulation
Learning Selective Invariance Upon Parametric Density Functions of Transformations
Learning Space Partitions for Path Planning
Learning to Collaborate with Human Players
Learning-based Safe Navigation for Dynamic Legged Robots
Learning-Driven Exploration For Search
Leveraging Demonstrations with Goal-Directed Multi-Task Bisimulation
Local Parametric Learning Rules for Parallel and Scalable Training
Long Term Video Understanding
Low-Data Learning for Assistive Video Description
ML-Based Robotic Manipulation via the Use of Diverse Datasets
Modeling Interpersonal Multimodal Signals in Social Conversation
Multi-agent Social Learning
Never Decrypt Data Lake
NumS: NumPy API-Compatible Framework backed by Ray
Offline Recovery RL: Offline Reinforcement Learning with Safe Online Adaptation
Optimal Data Augmentation Strategy Search
Personalized federated learning: new algorithms and statistical rates,
Pre-trained Representations for Language-Guided Web Navigation
Realistic Large-Scale Benchmark for Adversarial Patch
Regret Bounds for Contextual Bandits Under Slate Feedback
Reinforcement Learning in High Dimensional Systems
Robust Image Classification via Parts and Disentangled Attributes
Robustness for Deep Learning/Ethical AI Through Human Value Modeling
Safe and Sound: Learning Locomotion Skills Across Robot Morphology
Safe Robotic Learning Via Reachability Theory
Scene Sketch to Photo Synthesis
Secure and Privacy-Preserving Federated Learning
Self Supervised Semantic Segmentation in the Wild
Self-supervised Learning for Generic Visual Representation
Smart Practice: Learning to Practice Skills from Demonstrations
Task-Specific World Models for Robotic Manipulation
Towards Human-like Attention
Towards Robust Neural Networks with Conditional Generative Models
Training Sparse High Capacity Models with Implicit Neural Networks and Frank-Wolfe
Uncertainty Aware Machine Learning for Model Based Planning and Control
Universal Representation Learning for Control
Using Deep Reinforcement Learning to Generalize Search in Games
Video Representation Learning for Global and Local Features
Visual and Tactile Learning for Object Manipulation via Hierarchical Learning
Weakly Supervised Multimodal Feature Representation Learning from Video
X-Ray for Lateral Access Mechanical Search