60 citations · 92 across the 10 of their papers we have counts for
11 papers
Optimal Capital Deployment Under Stochastic Deal Arrivals: A Continuous-Time ADP Approach
Kunal Menda, Raphael S Benarrosh
Suppose you are a fund manager with $100 million to deploy and two years to invest it. A deal comes across your desk that looks appealing but costs $50 million -- half of your av…
Strategic Asset Allocation with Illiquid Alternatives
Eric Luxenberg, Stephen Boyd, Mykel Kochenderfer +6
We address the problem of strategic asset allocation (SAA) with portfolios that include illiquid alternative asset classes. The main challenge in portfolio construction with illiqu…
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand
Arec Jamgochian, Di Wu, Kunal Menda +2
Some real-world decision-making problems require making probabilistic forecasts over multiple steps at once. However, methods for probabilistic forecasting may fail to capture corr…
Multi-Vehicle Control in Roundabouts using Decentralized Game-Theoretic Planning
Arec Jamgochian, Kunal Menda, Mykel J. Kochenderfer
Safe navigation in dense, urban driving environments remains an open problem and an active area of research. Unlike typical predict-then-plan approaches, game-theoretic planning co…
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
Kunal Menda, Jayesh K. Gupta, Zachary Manchester +1
Model-based paradigms for decision-making and control are becoming ubiquitous in robotics. They rely on the ability to efficiently learn a model of the system from data. Structured…
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta +3
System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear…
Structured Mechanical Models for Robot Learning and Control
Jayesh K. Gupta, Kunal Menda, Zachary Manchester +1
Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have be…
A General Framework for Structured Learning of Mechanical Systems
Jayesh K. Gupta, Kunal Menda, Zachary Manchester +1
Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or blac…
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer
While imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these…
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning
Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer
While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addre…
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes
Kunal Menda, Yi-Chun Chen, Justin Grana +4
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 de…