AutoML to Date and Beyond: Challenges and Opportunities

AutoML to Date and Beyond: Challenges and Opportunities

21 October 2020

Shubhra (Santu) Karmaker

Md. Mahadi Hassan

Chengxiang Zhai

K. Veeramachaneni

Papers citing "AutoML to Date and Beyond: Challenges and Opportunities"

13 / 13 papers shown

Title
Prediction-Assisted Online Distributed Deep Learning Workload Scheduling in GPU Clusters Ziyue Luo Jia-Wei Liu Myungjin Lee Ness B. Shroff 34 0 0 09 Jan 2025
PyGen: A Collaborative Human-AI Approach to Python Package Creation Saikat Barua Mostafizur Rahman Md Jafor Sadek Rafiul Islam Shehnaz Khaled Md. Shohrab Hossain 34 1 0 13 Nov 2024
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Angie Boggust Venkatesh Sivaraman Yannick Assogba Donghao Ren Dominik Moritz Fred Hohman VLM 37 3 0 06 Aug 2024
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks Feng-Lei Fan Meng Wang Hang Dong Jianwei Ma Tieyong Zeng 16 0 0 03 May 2024
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 39 4 0 13 Mar 2024
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation Jikai Jin Vasilis Syrgkanis CML 56 1 0 22 Feb 2024
A knowledge-driven AutoML architecture C. Cofaru Johan Loeckx 11 0 0 28 Nov 2023
Advances and Challenges in Meta-Learning: A Technical Review Anna Vettoruzzo Mohamed-Rafik Bouguelia Joaquin Vanschoren Thorsteinn Rögnvaldsson K. Santosh OffRL 16 69 0 10 Jul 2023
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK L. Nannini Agathe Balayn A. Smith 6 37 0 20 Apr 2023
Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments Zac Pullar-Strecker Xinglong Chang Liam Brydon Ioannis A Ziogas Katharina Dost Jörg Simon Wicker 14 1 0 17 Apr 2023
Addressing UX Practitioners' Challenges in Designing ML Applications: an Interactive Machine Learning Approach K. J. Kevin Feng David W. McDonald HAI 11 11 0 23 Feb 2023
Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment Nikolay O. Nikitin Sergey Teryoshkin Valerii Pokrovskii Sergey Pakulin D. Nasonov 21 1 0 12 Jan 2023
Survey on Feature Selection T. Abdallah B. Iglesia 33 213 0 10 Oct 2015