Publication

Integration of Grid Cost Model into ISS/VIOLA Meta-scheduler Environment

Related publications (34)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling

Robert West, Maxime Jean Julien Peyrard, Marija Sakota

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 ...

Assoc Computing Machinery2024

Efficient Online Clustering with Moving Costs

Volkan Cevher, Efstratios Panteleimon Skoulakis

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 ...

2023

Modeling and mitigating supply chain disruptions as a bilevel network flow problem

Anna Timonina-Farkas, René Yves Glogg

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

Optimal Matching of Random Parts

Thomas Alois Weber

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

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Sylvain Calinon, Amirreza Razmjoo Fard, Teguh Santoso Lembono

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

Cost and benefit allocation in a energy community

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

Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces

Maryam Kamgarpour

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

Industrial flexibility as demand-side response for electrical grid stability

François Maréchal, Ivan Daniel Kantor, Alessio Santecchia

Electricity markets are currently experiencing a period of rapid change. The intermittent nature of renewable energy is disrupting the conventional methods used in operational planning of the electrical grid, causing a shift from a day-ahead forecast polic ...

ECOS 2020 Local Organizing Committee2020

Learning Nash Equilibria in Monotone Games

Maryam Kamgarpour

We consider multi-agent decision making where each agent's cost function depends on all agents' strategies. We propose a distributed algorithm to learn a Nash equilibrium, whereby each agent uses only obtained values of her cost function at each joint play ...

IEEE2019

Coordinate Descent with Bandit Sampling

Patrick Thiran, Laura Elisa Celis, Farnood Salehi

Coordinate descent methods usually minimize a cost function by updating a random decision variable (corresponding to one coordinate) at a time. Ideally, we would update the decision variable that yields the largest decrease in the cost function. However, f ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018