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20232025
most citedMultiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

math.OC2025

OPO: Making Decision-Focused Data Acquisition Decisions

Egon Peršak, Miguel F. Anjos

We propose a model for making data acquisition decisions for variables in contextual stochastic optimisation problems. Data acquisition decisions are typically treated as separate…

cs.LG2025

Generating Poisoning Attacks against Ridge Regression Models with Categorical Features

Monse Guedes-Ayala, Lars Schewe, Zeynep Suvak +1

Machine Learning (ML) models have become a very powerful tool to extract information from large datasets and use it to make accurate predictions and automated decisions. However, M…

cs.LG2024★ 1 cited

Multiple-Resolution Tokenization for Time Series Forecasting with an Application to Pricing

Egon Peršak, Miguel F. Anjos, Sebastian Lautz +1

We propose a transformer architecture for time series forecasting with a focus on time series tokenisation and apply it to a real-world prediction problem from the pricing domain.…

math.OC2024

Decision-Focused Forecasting: A Differentiable Multistage Optimisation Architecture

Egon Peršak, Miguel F. Anjos

Most decision-focused learning work has focused on single stage problems whereas many real-world decision problems are more appropriately modelled using multistage optimisation. In…

math.OC2023

Learning Deterministic Surrogates for Robust Convex QCQPs

Egon Peršak, Miguel F. Anjos

Decision-focused learning is a promising development for contextual optimisation. It enables us to train prediction models that reflect the contextual sensitivity structure of the…