Huibin Shen — accepted AI-conference papers

2025

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables

Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen +7

AISTATS 2025poster

Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent a…

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2020

A Quantile-based Approach for Hyperparameter Transfer Learning

David Salinas, Huibin Shen, Valerio Perrone

ICML 2020poster

Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance objectives of the same algorithm acros…

Cited by 53 Source ↗PDF ↗Scholar ↗

2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning

Valerio Perrone, Huibin Shen, Matthias W Seeger, Cedric Archambeau, Rodolphe Jenatton

NeurIPS 2019poster

Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in isolation, there has been recent interest in speeding up BO by transferring knowledge across multiple related black-box…

Cited by 133 Source ↗PDF ↗code ↗Scholar ↗