Search Results for author:
Found 9 papers, 7 papers with code
Long-form factuality in large language models
Empirically, we demonstrate that LLM agents can outperform crowdsourced human annotators - on a set of ~16k individual facts, SAFE agrees with crowdsourced human annotators 72% of the time, and on a random subset of 100 disagreement cases, SAFE wins 76% of the time.
Large Language Models as Optimizers
In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language.
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
The best neural architecture for a given machine learning problem depends on many factors: not only the complexity and structure of the dataset, but also on resource constraints including latency, compute, energy consumption, etc.
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Low-precision arithmetic trains deep learning models using less energy, less memory and less time.
TenIPS: Inverse Propensity Sampling for Tensor Completion
Tensors are widely used to represent multiway arrays of data.
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
The theorems show that a relatively sharper regularizer leads to a tighter error bound, which is consistent with our numerical results.
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components.
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
RNLMF constructs a dictionary for the data space by factoring a kernelized feature space; a noisy matrix can then be decomposed as the sum of a sparse noise matrix and a clean data matrix that lies in a low dimensional nonlinear manifold.
OBOE: Collaborative Filtering for AutoML Model Selection
Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning.
