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paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...
IEEE2021
Making decisions is part and parcel of being human. Among a set of actions, we want to choose the one that has the highest reward. But the uncertainty of the outcome prevents us from always making the right decision. Making decisions under uncertainty can ...
EPFL2020
Coverage-guided greybox fuzzers rely on control-flow coverage feedback to explore a target program and uncover bugs. Compared to control-flow coverage, data-flow coverage offers a more fine-grained approximation of program behavior. Data-flow coverage capt ...
2023
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In the GW approximation, the screened interaction W is a nonlocal and dynamical potential that usually has a complex frequency dependence. A full description of such a dependence is possible but often computationally demanding. For this reason, it is still ...
AMER PHYSICAL SOC2021
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Years of globalization, outsourcing and cost cutting have increased supply chain vulnerability calling for more effective risk mitigation strategies. In our research, we analyze supply chain disruptions in a production setting. Using a bilevel optimization ...
SPRINGER HEIDELBERG2022
This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...
2022
In the transition to a sustainable energy system, energy communities have unique advantages; they require no additional land and allow inhabitants to reduce their energy bills. Nowadays, each shareholder’s profit is calculated based on their share of inves ...
2021
We consider online convex optimization with a zero-order oracle feedback. In particular, the decision maker does not know the explicit representation of the time-varying cost functions, or their gradients. At each time step, she observes the value of the c ...
2020
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In this work we consider an online learning problem, called Online k-Clustering with Moving Costs, at which a learner maintains a set of k facilities over T rounds so as to minimize the connection cost of an adversarially selected sequence of clients. The ...
Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose output the solution can then be extracted. LM performance has consistently be ...
