747 citations · 902 across the 22 of their papers we have counts for
22 papers
Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement
Michael Bloesch, Markus Wulfmeier, Philemon Brakel +8
Imitation Learning from Observation (IfO) offers a powerful way to learn behaviors at large-scale: Unlike behavior cloning or offline reinforcement learning, IfO can leverage actio…
Neural rendering enables dynamic tomography
Ivan Grega, William F. Whitney, Vikram S. Deshpande
Interrupted X-ray computed tomography (X-CT) has been the common way to observe the deformation of materials during an experiment. While this approach is effective for quasi-static…
Linear Transformer Topological Masking with Graph Random Features
Isaac Reid, Kumar Avinava Dubey, Deepali Jain +12
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relativ…
Modeling the Real World with High-Density Visual Particle Dynamics
William F. Whitney, Jacob Varley, Deepali Jain +3
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds con…
Learning rigid-body simulators over implicit shapes for large-scale scenes and vision
Yulia Rubanova, Tatiana Lopez-Guevara, Kelsey R. Allen +3
Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously ha…
Scaling Face Interaction Graph Networks to Real World Scenes
Tatiana Lopez-Guevara, Yulia Rubanova, William F. Whitney +3
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design. To better capture complex real dynamics…
Learning 3D Particle-based Simulators from RGB-D Videos
William F. Whitney, Tatiana Lopez-Guevara, Tobias Pfaff +4
Realistic simulation is critical for applications ranging from robotics to animation. Traditional analytic simulators sometimes struggle to capture sufficiently realistic simulatio…
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier +4
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional in…
Learning rigid dynamics with face interaction graph networks
Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara +4
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GN…
The Challenges of Exploration for Offline Reinforcement Learning
Nathan Lambert, Markus Wulfmeier, William Whitney +5
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal…
Quantile Filtered Imitation Learning
David Brandfonbrener, William F. Whitney, Rajesh Ranganath +1
We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning. QFIL performs policy improvement by runni…
Offline RL Without Off-Policy Evaluation
David Brandfonbrener, William F. Whitney, Rajesh Ranganath +1
Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing…
Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning
William F. Whitney, Michael Bloesch, Jost Tobias Springenberg +3
Despite the close connection between exploration and sample efficiency, most state of the art reinforcement learning algorithms include no considerations for exploration beyond max…
Evaluating representations by the complexity of learning low-loss predictors
William F. Whitney, Min Jae Song, David Brandfonbrener +2
We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learn…
Offline Contextual Bandits with Overparameterized Models
David Brandfonbrener, William F. Whitney, Rajesh Ranganath +1
Recent results in supervised learning suggest that while overparameterized models have the capacity to overfit, they in fact generalize quite well. We ask whether the same phenomen…
Dynamics-aware Embeddings
William Whitney, Rajat Agarwal, Kyunghyun Cho +1
In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simulta…
Disentangling Video with Independent Prediction
William F. Whitney, Rob Fergus
We propose an unsupervised variational model for disentangling video into independent factors, i.e. each factor's future can be predicted from its past without considering the othe…
Model-Based Planning with Discrete and Continuous Actions
Mikael Henaff, William F. Whitney, Yann LeCun
Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity…
Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now s…
Understanding Visual Concepts with Continuation Learning
William F. Whitney, Michael Chang, Tejas Kulkarni +1
We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations. We propose to learn these concepts by observing consecutive fram…
Disentangled Representations in Neural Models
William Whitney
Representation learning is the foundation for the recent success of neural network models. However, the distributed representations generated by neural networks are far from ideal.…
Deep Convolutional Inverse Graphics Network
Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli +1
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with…