6 citations · 14 across the 14 of their papers we have counts for
14 papers
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Jonathan Wilder Lavington, Ke Zhang, Vasileios Lioutas +9
The training, testing, and deployment, of autonomous vehicles requires realistic and efficient simulators. Moreover, because of the high variability between different problems pres…
Semantically Consistent Video Inpainting with Conditional Diffusion Models
Dylan Green, William Harvey, Saeid Naderiparizi +10
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information acros…
Nearest Neighbour Score Estimators for Diffusion Generative Models
Matthew Niedoba, Dylan Green, Saeid Naderiparizi +9
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased…
A Diffusion-Model of Joint Interactive Navigation
Matthew Niedoba, Jonathan Wilder Lavington, Yunpeng Liu +8
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios i…
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm
Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt +3
We consider minimizing a smooth function subject to a summation constraint over its variables. By exploiting a connection between the greedy 2-coordinate update for this problem an…
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg +8
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We invest…
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington +8
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.…
Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be
Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington +1
The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly. However, our theoret…
Target-based Surrogates for Stochastic Optimization
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad +2
We consider minimizing functions for which it is expensive to compute the (possibly stochastic) gradient. Such functions are prevalent in reinforcement learning, imitation learning…
Vehicle Type Specific Waypoint Generation
Yunpeng Liu, Jonathan Wilder Lavington, Adam Scibior +1
We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior. Many foundation behavior model…
Improved Policy Optimization for Online Imitation Learning
Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
We consider online imitation learning (OIL), where the task is to find a policy that imitates the behavior of an expert via active interaction with the environment. We aim to bridg…
Conditional Permutation Invariant Flows
Berend Zwartsenberg, Adam Ścibior, Matthew Niedoba +7
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation…
Critic Sequential Monte Carlo
Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas +6
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic…
Robust Asymmetric Learning in POMDPs
Andrew Warrington, J. Wilder Lavington, Adam Ścibior +2
Policies for partially observed Markov decision processes can be efficiently learned by imitating policies for the corresponding fully observed Markov decision processes. Unfortuna…