Search Results for author:
Found 10 papers, 2 papers with code
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making.
Conditional Flow Variational Autoencoders for Structured Sequence Prediction
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world.
Ranked #11 on
Trajectory Prediction
on Stanford Drone
Non-cooperative Multi-agent Systems with Exploring Agents
This leads to a set of coupled Bellman equations that describes the behavior of the agents.
Haar Wavelet based Block Autoregressive Flows for Trajectories
This yields an exact inference method that models trajectories at different spatio-temporal resolutions in a hierarchical manner.
State-Only Imitation Learning by Trajectory Distribution Matching
Our training objective minimizes the Kulback-Leibler divergence between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion.
Imitation Learning by State-Only Distribution Matching
We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric.
Fail-Safe Adversarial Generative Imitation Learning
For flexible yet safe imitation learning (IL), we propose theory and a modular method, with a safety layer that enables a closed-form probability density/gradient of the safe generative continuous policy, end-to-end generative adversarial training, and worst-case safety guarantees.
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data
We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing flow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the contrastive dataset.
A powerful rank-based correction to multiple testing under positive dependency
We develop a novel multiple hypothesis testing correction with family-wise error rate (FWER) control that efficiently exploits positive dependencies between potentially correlated statistical hypothesis tests.
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
In particular, we leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object bounding boxes.
