Search Results for author: Nezihe Merve Gürel

Found 10 papers, 3 papers with code

C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models

no code implementations5 Feb 2024 Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song, Bo Li

Specifically, we provide conformal risk analysis for RAG models and certify an upper confidence bound of generation risks, which we refer to as conformal generation risk.

Retrieval

Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning

no code implementations28 May 2023 Patrik Okanovic, Roger Waleffe, Vasilis Mageirakos, Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis Kalogerias, Nezihe Merve Gürel, Theodoros Rekatsinas

Methods for carefully selecting or generating a small set of training data to learn from, i. e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks.

Data Compression

Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks

1 code implementation11 Jun 2021 Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li

In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task.

BIG-bench Machine Learning

A Data Quality-Driven View of MLOps

no code implementations15 Feb 2021 Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel, Bojan Karlaš, Wentao Wu, Ce Zhang

Developing machine learning models can be seen as a process similar to the one established for traditional software development.

BIG-bench Machine Learning

Online Active Model Selection for Pre-trained Classifiers

1 code implementation19 Oct 2020 Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš, Johannes Rausch, Ce Zhang, Andreas Krause

Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries?

Model Selection

FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

no code implementations29 Jul 2020 Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei. Lin, Jingren Zhou

Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph.

Recommendation Systems

Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions

1 code implementation11 May 2020 Bojan Karlaš, Peng Li, Renzhi Wu, Nezihe Merve Gürel, Xu Chu, Wentao Wu, Ce Zhang

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data.

BIG-bench Machine Learning

DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

no code implementations10 Oct 2019 Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.

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