Josif Grabocka

Frank Hutter

CoRR, 2024

Ensembling Finetuned Language Models for Text Classification.

[BibT_eX]

[DOI]

CoRR, 2024

Lightweight Correlation-Aware Table Compression.

[BibT_eX]

[DOI]

CoRR, 2024

Dynamic Post-Hoc Neural Ensemblers.

[BibT_eX]

[DOI]

CoRR, 2024

Multi-objective Differentiable Neural Architecture Search.

[BibT_eX]

[DOI]

Rhea Sanjay Sukthanker

CoRR, 2024

Hierarchical Transformers are Efficient Meta-Reinforcement Learners.

[BibT_eX]

[DOI]

Gresa Shala

André Biedenkapp

CoRR, 2024

Tabular Data: Is Attention All You Need?

[BibT_eX]

[DOI]

Guri Zabërgja

CoRR, 2024

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Breaking the Paradox of Explainable Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks.

[BibT_eX]

[DOI]

Mofassir ul Islam Arif

Mohsan Jameel

CoRR, 2023

Deep Power Laws for Hyperparameter Optimization.

[BibT_eX]

[DOI]

CoRR, 2023

Scaling Laws for Hyperparameter Optimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Pipeline Embeddings for AutoML.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Transfer NAS with Meta-learned Bayesian Surrogates.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Gray-Box Gaussian Processes for Automated Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Deep Ranking Ensembles for Hyperparameter Optimization.

[BibT_eX]

[DOI]

Abdus Salam Khazi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Supervising the Multi-Fidelity Race of Hyperparameter Configurations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Zero-shot AutoML with Pretrained Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Transformers Can Do Bayesian Inference.

[BibT_eX]

[DOI]

Samuel Müller

Noah Hollmann

Frank Hutter

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Dataset2Vec: learning dataset meta-features.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., 2021

Multi-task problems are not multi-objective.

[BibT_eX]

[DOI]

Michael Ruchte

CoRR, 2021

Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data.

[BibT_eX]

[DOI]

CoRR, 2021

Efficient Multi-Objective Optimization for Deep Learning.

[BibT_eX]

[DOI]

Michael Ruchte

CoRR, 2021

Hyperparameter Optimization with Differentiable Metafeatures.

[BibT_eX]

[DOI]

CoRR, 2021

A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning.

[BibT_eX]

[DOI]

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Well-tuned Simple Nets Excel on Tabular Datasets.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Few-Shot Bayesian Optimization with Deep Kernel Surrogates.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Scalable Pareto Front Approximation for Deep Multi-Objective Learning.

[BibT_eX]

[DOI]

Michael Ruchte

Proceedings of the IEEE International Conference on Data Mining, 2021

2020

HIDRA: Head Initialization across Dynamic targets for Robust Architectures.

[BibT_eX]

[DOI]

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Self-supervised Learning for Semi-supervised Time Series Classification.

[BibT_eX]

[DOI]

Shayan Jawed

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2020

Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks.

[BibT_eX]

[DOI]

Mofassir ul Islam Arif

Mohsan Jameel

Proceedings of the Conference "Lernen, 2020

2019

Chameleon: Learning Model Initializations Across Tasks With Different Schemas.

[BibT_eX]

[DOI]

CoRR, 2019

Hyp-RL : Hyperparameter Optimization by Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2019

In Hindsight: A Smooth Reward for Steady Exploration.

[BibT_eX]

[DOI]

CoRR, 2019

Learning Surrogate Losses.

[BibT_eX]

[DOI]

Randolf Scholz

CoRR, 2019

Data-Driven Vehicle Trajectory Forecasting.

[BibT_eX]

[DOI]

CoRR, 2019

Attribute-aware non-linear co-embeddings of graph features.

[BibT_eX]

[DOI]

Proceedings of the 13th ACM Conference on Recommender Systems, 2019

A Deep Multi-task Approach for Residual Value Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD.

[BibT_eX]

[DOI]

Mohsan Jameel

Mofassir ul Islam Arif

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

A Hybrid Convolutional Approach for Parking Availability Prediction.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2019

Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights.

[BibT_eX]

[DOI]

Proceedings of the Agents and Artificial Intelligence - 11th International Conference, 2019

Multi-Label Network Classification via Weighted Personalized Factorizations.

[BibT_eX]

[DOI]

Vijaya Krishna Yalavarthi

Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019

Gait Verification using Deep Learning with a Pairwise Loss.

[BibT_eX]

[DOI]

Hareesh Mandalapu

Proceedings of the 2019 International Conference of the Biometrics Special Interest Group, 2019

2018

NeuralWarp: Time-Series Similarity with Warping Networks.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Channel masking for multivariate time series shapelets.

[BibT_eX]

[DOI]

Dripta S. Raychaudhuri

CoRR, 2017

Personalized Deep Learning for Tag Recommendation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2017

2016

Latent Time-Series Motifs.

[BibT_eX]

[DOI]

Nicolas Schilling

ACM Trans. Knowl. Discov. Data, 2016

Fast classification of univariate and multivariate time series through shapelet discovery.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2016

Learning DTW-Shapelets for Time-Series Classification.

[BibT_eX]

[DOI]

Proceedings of the 3rd IKDD Conference on Data Science, 2016

2015

Learning Through Non-linearly Supervised Dimensionality Reduction.

[BibT_eX]

[DOI]

Trans. Large Scale Data Knowl. Centered Syst., 2015

Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2015

Ultra-Fast Shapelets for Time Series Classification.

[BibT_eX]

[DOI]

CoRR, 2015

Scalable Discovery of Time-Series Shapelets.

[BibT_eX]

[DOI]

CoRR, 2015

Optimal Time-Series Motifs.

[BibT_eX]

[DOI]

Nicolas Schilling

CoRR, 2015

2014

Invariant time-series factorization.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., 2014

Supervised Nonlinear Factorizations Excel In Semi-supervised Regression.

[BibT_eX]

[DOI]

Erind Bedalli

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

Learning time-series shapelets.

[BibT_eX]

[DOI]

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Realistic optimal policies for energy-efficient train driving.

[BibT_eX]

[DOI]

Alexandros Dalkalitsis

Athanasios Lois

Evangelos Katsaros

Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, 2014

2013

Time-Series Classification Through Histograms of Symbolic Polynomials.

[BibT_eX]

[DOI]

CoRR, 2013

Invariant Factorization Of Time-Series.

[BibT_eX]

[DOI]

CoRR, 2013

Supervised Dimensionality Reduction via Nonlinear Target Estimation.

[BibT_eX]

[DOI]

Lucas Drumond

Proceedings of the Data Warehousing and Knowledge Discovery, 2013

2012

Invariant Time-Series Classification.

[BibT_eX]

[DOI]

Alexandros Nanopoulos

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Efficient Classification of Long Time-Series.

[BibT_eX]

[DOI]

Erind Bedalli

Proceedings of the ICT Innovations 2012, 2012

Classification of Sparse Time Series via Supervised Matrix Factorization.

[BibT_eX]

[DOI]

Alexandros Nanopoulos