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05/23/2023
Deep Pipeline Embeddings for AutoML
Automated Machine Learning (AutoML) is a promising direction for democra...
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05/22/2023
Breaking the Paradox of Explainable Deep Learning
Deep Learning has achieved tremendous results by pushing the frontier of...
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04/14/2023
Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks
The strength of machine learning models stems from their ability to lear...
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03/27/2023
Deep Ranking Ensembles for Hyperparameter Optimization
Automatically optimizing the hyperparameters of Machine Learning algorit...
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02/01/2023
Deep Power Laws for Hyperparameter Optimization
Hyperparameter optimization is an important subfield of machine learning...
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06/16/2022
Zero-Shot AutoML with Pretrained Models
Given a new dataset D and a low compute budget, how should we choose a p...
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02/20/2022
Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning
Gray-box hyperparameter optimization techniques have recently emerged as...
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12/20/2021
Transformers Can Do Bayesian Inference
Currently, it is hard to reap the benefits of deep learning for Bayesian...
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10/14/2021
Multi-task problems are not multi-objective
Multi-objective optimization (MOO) aims at finding a set of optimal conf...
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06/21/2021
Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
Tabular datasets are the last "unconquered castle" for deep learning, wi...
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06/11/2021
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Hyperparameter optimization (HPO) is a core problem for the machine lear...
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03/24/2021
Efficient Multi-Objective Optimization for Deep Learning
Multi-objective optimization (MOO) is a prevalent challenge for Deep Lea...
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02/07/2021
Hyperparameter Optimization with Differentiable Metafeatures
Metafeatures, or dataset characteristics, have been shown to improve the...
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01/19/2021
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Hyperparameter optimization (HPO) is a central pillar in the automation ...
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10/28/2019
HIDRA: Head Initialization across Dynamic targets for Robust Architectures
The performance of gradient-based optimization strategies depends heavil...
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09/30/2019
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Parametric models, and particularly neural networks, require weight init...
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06/27/2019
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Hyperparameter tuning is an omnipresent problem in machine learning as i...
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06/24/2019
In Hindsight: A Smooth Reward for Steady Exploration
In classical Q-learning, the objective is to maximize the sum of discoun...
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05/27/2019
Dataset2Vec: Learning Dataset Meta-Features
Machine learning tasks such as optimizing the hyper-parameters of a mode...
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05/24/2019
Learning Surrogate Losses
The minimization of loss functions is the heart and soul of Machine Lear...
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02/25/2019
Multi-Label Network Classification via Weighted Personalized Factorizations
Multi-label network classification is a well-known task that is being us...
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02/09/2019
Data-Driven Vehicle Trajectory Forecasting
An active area of research is to increase the safety of self-driving veh...
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12/20/2018
NeuralWarp: Time-Series Similarity with Warping Networks
Research on time-series similarity measures has emphasized the need for ...
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05/03/2015
Optimal Time-Series Motifs
Motifs are the most repetitive/frequent patterns of a time-series. The d...
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07/24/2013