Search Results for author:
Found 9 papers, 6 papers with code
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand
In this paper, we introduce the conditional approximate normalizing flow (CANF) to make probabilistic multi-step time-series forecasts when correlations are present over long time horizons.
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems, typically fit to data by minimizing the error between predicted and observed accelerations or next states.
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
System identification is a key step for model-based control, estimator design, and output prediction.
Structured Mechanical Models for Robot Learning and Control
Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge.
Non-linear System Identification from Partial Observations via Iterative Smoothing and Learning
System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs.
A General Framework for Structured Learning of Mechanical Systems
We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
While imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors.
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes
The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems.
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning
While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors.
