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Stochastic Optimization with Adaptive Batch Size: Discrete Choice Models as a Case Study

Publications associées (33)

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

Christophe Ballif, Marine Dominique Cauz, Laure-Emmanuelle Perret Aebi

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 ...

2023

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

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 ...

EPFL2023

Validating functional redundancy with mixed generative adversarial networks

Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Trung-Dung Hoang

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 ...

ELSEVIER2023

In today's world, there is no shortage of disruptors acting on various professional domains. The Fourth Industrial Revolution, with its AI-driven and automation-focused technologies, has fundamentally changed many domains -- particularly the Information an ...

EPFL2022

Optimization methods for collaborative learning

Sai Praneeth Reddy Karimireddy

A traditional machine learning pipeline involves collecting massive amounts of data centrally on a server and training models to fit the data. However, increasing concerns about the privacy and security of user's data, combined with the sheer growth in the ...

EPFL2021

A General Framework for Optimal Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

2021

Graph-Homomorphic Perturbations For Private Decentralized Learning

Ali H. Sayed, Stefan Vlaski

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...

IEEE2021

Clouseau: Blockchain-based Data Integrity for HDFS Clusters

Ioannis Mytilinis

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are acce ...

IEEE COMPUTER SOC2021

Fixed-wing micro UAV open data with digicam and raw INS/GNSS

Jan Skaloud, Davide Antonio Cucci, Kenneth Joseph Paul

We have recently released in the open domain data originating from a series of flights conducted with a fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications. How ...

2021

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The highest share of the global population lives in cities. The current configuration of the latter requires considerable amounts of resource flows causing the degradation of local and global ecosystems. To face the complexity of these challenges, scientis ...

IOP PUBLISHING LTD2021