21 Result(s)
within
Mykel J. Kochenderfer
English
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Article
Global optimization of objective functions represented by ReLU networks
Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversa...
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Article
Portfolio construction as linearly constrained separable optimization
Mean–variance portfolio optimization problems often involve separable nonconvex terms, including penalties on capital gains, integer share constraints, and minimum nonzero position and trade sizes. We propose ...
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Article
Guest Editorial: Special issue on robust machine learning
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Article
Generating probabilistic safety guarantees for neural network controllers
Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, however, makes their output difficu...
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Article
Open AccessPersonalizing exoskeleton assistance while walking in the real world
Personalized exoskeleton assistance provides users with the largest improvements in walking speed1 and energy economy2–4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskel...
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Article
Reluplex: a calculus for reasoning about deep neural networks
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty i...
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Chapter and Conference Paper
ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs
Deep neural networks often lack the safety and robustness guarantees needed to be deployed in safety critical systems. Formal verification techniques can be used to prove input-output safety properties of netw...
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Article
Dynamic multi-robot task allocation under uncertainty and temporal constraints
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the en...
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Article
Open AccessExplaining COVID-19 outbreaks with reactive SEIRD models
COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our ...
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Article
Open AccessSensing leg movement enhances wearable monitoring of energy expenditure
Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estima...
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Article
Tax-Aware Portfolio Construction via Convex Optimization
We describe an optimization-based tax-aware portfolio construction method that adds tax liability to standard Markowitz-based portfolio construction. Our method produces a trade list that specifies the number ...
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Chapter and Conference Paper
Normalizing Flow Policies for Multi-agent Systems
Stochastic policy gradient methods using neural representations have had considerable success in single-agent domains with continuous action spaces. These methods typically use networks that output the paramet...
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Article
Open AccessRapid energy expenditure estimation for ankle assisted and inclined loaded walking
Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure usi...
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Article
Decomposition methods with deep corrections for reinforcement learning
Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used to...
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Chapter and Conference Paper
The Marabou Framework for Verification and Analysis of Deep Neural Networks
Deep neural networks are revolutionizing the way complex systems are designed. Consequently, there is a pressing need for tools and techniques for network analysis and certification. To help in addressing that...
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Chapter
Adaptive Stress Testing of Safety-Critical Systems
Stress testing in simulation plays a critical role in the validation of safety-critical systems, including aircraft, cars, medical devices, and spacecraft. The analysis of failure events is important in unders...
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Chapter and Conference Paper
Robust Super-Level Set Estimation Using Gaussian Processes
This paper focuses on the problem of determining as large a region as possible where a function exceeds a given threshold with high probability. We assume that we only have access to a noise-corrupted version ...
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Chapter and Conference Paper
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty i...
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Chapter and Conference Paper
Collision Avoidance Using Partially Controlled Markov Decision Processes
Optimal collision avoidance in stochastic environments requires accounting for the likelihood and costs of future sequences of outcomes in response to different sequences of actions. Prior work has investigate...
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Article
Aircraft Collision Avoidance Using Monte Carlo Real-Time Belief Space Search
The aircraft collision avoidance problem can be formulated using a decision-theoretic planning framework where the optimal behavior requires balancing the competing objectives of avoiding collision and adherin...
