Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints

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.

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sens ...

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, i ...

. High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of para ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about ne ...

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 mi ...

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data prin ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

In this thesis, we give new approximation algorithms for some NP-hard problems arising in resource allocation and network design. As a resource allocation problem, we study the Santa Claus problem (also known as the MaxMin Fair Allocation problem) in which ...

Over the past few decades, the debates have shifted from whether to how Computer Science (CS) should be introduced into formal education. Given the diverse ways to introduce CS into formal education, and the struggles many countries have faced, considerabl ...

Information retrieval (IR) systems such as search engines are important for people to find what they need among the tremendous amount of data available in their organization or on the Internet. These IR systems enable users to search in a large data collec ...

This survey aims to investigate research data management practices at EPFL and integrate their results into specific academic services. The previous two editions, in collaboration with TU Delft, Cambridge University and Illinois University, were carried ou ...

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chem ...

Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints

Chat with Graph Search

Data and scripts for the RaFSIP scheme

Robust machine learning for neuroscientific inference

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Methodology for selecting measurement points that optimize information gain for model updating

Community-based reconstruction and simulation of a full-scale model of region CA1 of rat hippocampus

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Validating functional redundancy with mixed generative adversarial networks

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

On Artificial Intelligence and Manipulation

Toward data-driven materials design: From atoms to pilot plants

Data-driven Methods for Control: from Linear to Lifting

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

Shared metadata for data-centric materials science

Transfer learning application of self-supervised learning in ARPES

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Approximation Algorithms for Allocation and Network Design

Co-constructing an Effective, Sustainable and Scalable Primary School Computer Science Curricular Reform

Graph Embedding for Retrieval

Quantitative assessment of research data management practice - 2021

Making the collective knowledge of chemistry open and machine actionable