activity
20202026
most citedWeighting Is Worth the Wait: Bayesian Optimization with Importance Sampling

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026★ 3 cited

Gemma 4 Technical Report

Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…

cs.CL2025★ 2 cited

EmbeddingGemma: Powerful and Lightweight Text Representations

Henrique Schechter Vera, Sahil Dua, Biao Zhang +86

We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledg…

cs.LG2024

The Vizier Gaussian Process Bandit Algorithm

Xingyou Song, Qiuyi Zhang, Chansoo Lee +8

Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a la…

cs.LG2021

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach

Setareh Ariafar, Justin Gilmer, Zachary Nado +3

Black box optimization requires specifying a search space to explore for solutions, e.g. a d-dimensional compact space, and this choice is critical for getting the best results at…

cs.LG2020★ 3 cited

Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling

Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar +3

Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to a…