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06/05/2023
End-to-end Differentiable Clustering with Associative Memories
Clustering is a widely used unsupervised learning technique involving an...
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03/04/2023
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Invariant risk minimization (IRM) has received increasing attention as a...
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01/12/2023
Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection
We show, to our knowledge, the first theoretical treatments of two commo...
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10/11/2022
Navigating Ensemble Configurations for Algorithmic Fairness
Bias mitigators can improve algorithmic fairness in machine learning mod...
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10/08/2022
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pru...
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03/03/2022
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
This paper is the first to propose a generic min-max bilevel multi-objec...
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02/16/2022
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm Analysis
We address the relatively unexplored problem of hyper-parameter optimiza...
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02/01/2022
An Empirical Study of Modular Bias Mitigators and Ensembles
There are several bias mitigators that can reduce algorithmic bias in ma...
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12/15/2021
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
We address the relatively unexplored problem of hyper-parameter optimiza...
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12/14/2021
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement
The mathematical formalization of a neurological mechanism in the olfact...
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09/15/2021
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD
We propose a new computationally-efficient first-order algorithm for Mod...
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07/02/2021
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
The pipeline optimization problem in machine learning requires simultane...
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09/29/2020
Learned Fine-Tuner for Incongruous Few-Shot Learning
Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
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08/19/2020
Neural Neighborhood Encoding for Classification
Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter [Dasgu...
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07/04/2020
Lale: Consistent Automated Machine Learning
Automated machine learning makes it easier for data scientists to develo...
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06/17/2020
Solving Constrained CASH Problems with ADMM
The CASH problem has been widely studied in the context of automated con...
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10/22/2019
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily...
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09/05/2019
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our li...
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05/24/2019
Type-Driven Automated Learning with Lale
Machine-learning automation tools, ranging from humble grid-search to hy...
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05/01/2019
Automated Machine Learning via ADMM
We study the automated machine learning (AutoML) problem of jointly sele...
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10/23/2012