Search Results for author: Rodrigo Fonseca

Found 11 papers, 7 papers with code

Exploring LLM-based Agents for Root Cause Analysis

no code implementations7 Mar 2024 Devjeet Roy, Xuchao Zhang, Rashi Bhave, Chetan Bansal, Pedro Las-Casas, Rodrigo Fonseca, Saravan Rajmohan

Lastly, we conduct a case study with a team at Microsoft to equip the ReAct agent with tools that give it access to external diagnostic services that are used by the team for manual RCA.

Management Retrieval

PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis

no code implementations11 Sep 2023 Dylan Zhang, Xuchao Zhang, Chetan Bansal, Pedro Las-Casas, Rodrigo Fonseca, Saravan Rajmohan

Major cloud providers have employed advanced AI-based solutions like large language models to aid humans in identifying the root causes of cloud incidents.

Decision Making

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

2 code implementations NeurIPS 2020 Linnan Wang, Rodrigo Fonseca, Yuandong Tian

If the nonlinear partition function and the local model fits well with ground-truth black-box function, then good partitions and candidates can be reached with much fewer samples.

Bayesian Optimization Neural Architecture Search

Few-shot Neural Architecture Search

2 code implementations11 Jun 2020 Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo

supernet, to approximate the performance of every architecture in the search space via weight-sharing.

Neural Architecture Search Transfer Learning

Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

1 code implementation6 Mar 2020 Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, Ricardo Bianchini

Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud.

Distributed, Parallel, and Cluster Computing

Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search

no code implementations25 Sep 2019 Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal.

Bayesian Optimization Neural Architecture Search

Sample-Efficient Neural Architecture Search by Learning Action Space

1 code implementation17 Jun 2019 Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.

Evolutionary Algorithms Neural Architecture Search

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

1 code implementation26 Mar 2019 Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

Image Captioning Neural Architecture Search +4

Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search

1 code implementation1 Jan 2019 Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.

Evolutionary Algorithms Image Classification +1

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

2 code implementations18 May 2018 Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

Image Captioning Neural Architecture Search +4

A-Tree: A Bounded Approximate Index Structure

no code implementations30 Jan 2018 Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, Tim Kraska

At the core of our index is a tunable error parameter that allows a DBA to balance lookup performance and space consumption.

Databases

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