61 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Arizona State UniversityUS7 papers
- University of MichiganUS4 papers
- California Institute of TechnologyUS3 papers
- University of ArizonaUS3 papers
- University of Illinois Urbana-ChampaignUS3 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- IBM Research - HaifaIL2 papers
- Institut de Recherche en Informatique et Systèmes AléatoiresFR2 papers
- Jet Propulsion LaboratoryUS2 papers
- OpticaUS2 papers
- Rutgers, The State University of New JerseyUS2 papers
- Université de RennesFR2 papers
8 papers · 1 filter
Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces
Kaito Baba, Yoshihiko Ozaki, Shuhei Watanabe
We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search spaces, where the presence or domain o…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li +3
Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies ha…
Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning
Yair Horesh, Noa Haas, Elhanan Mishraky +2
As AI systems develop in complexity it is becoming increasingly hard to ensure non-discrimination on the basis of protected attributes such as gender, age, and race. Many recent me…
Guided Random Forest in the RRF Package
Houtao Deng
Random Forest (RF) is a powerful supervised learner and has been popularly used in many applications such as bioinformatics. In this work we propose the guided random forest (GRF)…
A Time Series Forest for Classification and Feature Extraction
Houtao Deng, George Runger, Eugene Tuv +1
We propose a tree ensemble method, referred to as time series forest (TSF), for time series classification. TSF employs a combination of the entropy gain and a distance measure, re…
Gene selection with guided regularized random forest
Houtao Deng, George Runger
The regularized random forest (RRF) was recently proposed for feature selection by building only one ensemble. In RRF the features are evaluated on a part of the training data at e…
Feature Selection via Regularized Trees
Houtao Deng, George Runger
We propose a tree regularization framework, which enables many tree models to perform feature selection efficiently. The key idea of the regularization framework is to penalize sel…