Search Results for author: Colin White

Found 24 papers, 14 papers with code

Data Contamination Through the Lens of Time

1 code implementation16 Oct 2023 Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White, Samuel Dooley

Recent claims about the impressive abilities of large language models (LLMs) are often supported by evaluating publicly available benchmarks.

Speeding up Fourier Neural Operators via Mixed Precision

1 code implementation27 Jul 2023 Colin White, Renbo Tu, Jean Kossaifi, Gennady Pekhimenko, Kamyar Azizzadenesheli, Anima Anandkumar

In this work, we (i) profile memory and runtime for FNO with full and mixed-precision training, (ii) conduct a study on the numerical stability of mixed-precision training of FNO, and (iii) devise a training routine which substantially decreases training time and memory usage (up to 34%), with little or no reduction in accuracy, on the Navier-Stokes and Darcy flow equations.

Speeding up NAS with Adaptive Subset Selection

no code implementations2 Nov 2022 Vishak Prasad C, Colin White, Paarth Jain, Sibasis Nayak, Ganesh Ramakrishnan

A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final performance.

Neural Architecture Search

Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition

1 code implementation NeurIPS 2023 Rhea Sanjay Sukthanker, Samuel Dooley, John P. Dickerson, Colin White, Frank Hutter, Micah Goldblum

Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2.

Face Identification Face Recognition +2

AutoML for Climate Change: A Call to Action

1 code implementation7 Oct 2022 Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White

The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications.


NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies

1 code implementation6 Oct 2022 Arjun Krishnakumar, Colin White, Arber Zela, Renbo Tu, Mahmoud Safari, Frank Hutter

Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS).

Neural Architecture Search

On the Generalizability and Predictability of Recommender Systems

1 code implementation23 Jun 2022 Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John P. Dickerson, Colin White

By using far more meta-training data than prior work, RecZilla is able to substantially reduce the level of human involvement when faced with a new recommender system application.

Meta-Learning Recommendation Systems

NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy

1 code implementation ICLR 2022 Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, Frank Hutter

The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS).

Image Classification Neural Architecture Search +4

NAS-Bench-x11 and the Power of Learning Curves

1 code implementation NeurIPS 2021 Shen Yan, Colin White, Yash Savani, Frank Hutter

While early research in neural architecture search (NAS) required extreme computational resources, the recent releases of tabular and surrogate benchmarks have greatly increased the speed and reproducibility of NAS research.

Neural Architecture Search

Synthetic Benchmarks for Scientific Research in Explainable Machine Learning

1 code implementation23 Jun 2021 Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger

In this work, we address this issue by releasing XAI-Bench: a suite of synthetic datasets along with a library for benchmarking feature attribution algorithms.

Benchmarking BIG-bench Machine Learning +1

How Powerful are Performance Predictors in Neural Architecture Search?

1 code implementation NeurIPS 2021 Colin White, Arber Zela, Binxin Ru, Yang Liu, Frank Hutter

Early methods in the rapidly developing field of neural architecture search (NAS) required fully training thousands of neural networks.

Neural Architecture Search

A Study on Encodings for Neural Architecture Search

2 code implementations NeurIPS 2020 Colin White, Willie Neiswanger, Sam Nolen, Yash Savani

First we formally define architecture encodings and give a theoretical characterization on the scalability of the encodings we study Then we identify the main encoding-dependent subroutines which NAS algorithms employ, running experiments to show which encodings work best with each subroutine for many popular algorithms.

Neural Architecture Search

Intra-Processing Methods for Debiasing Neural Networks

3 code implementations NeurIPS 2020 Yash Savani, Colin White, Naveen Sundar Govindarajulu

Intra-processing methods are designed specifically to debias large models which have been trained on a generic dataset and fine-tuned on a more specific task.

Face Recognition Fairness

Exploring the Loss Landscape in Neural Architecture Search

2 code implementations6 May 2020 Colin White, Sam Nolen, Yash Savani

In this work, we show that (1) the simplest hill-climbing algorithm is a powerful baseline for NAS, and (2), when the noise in popular NAS benchmark datasets is reduced to a minimum, hill-climbing to outperforms many popular state-of-the-art algorithms.

Combinatorial Optimization Denoising +3

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

3 code implementations25 Oct 2019 Colin White, Willie Neiswanger, Yash Savani

Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for NAS when it is coupled with a neural predictor.

Bayesian Optimization Hyperparameter Optimization +1

BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search

no code implementations25 Sep 2019 Colin White, Willie Neiswanger, Yash Savani

We develop a path-based encoding scheme to featurize the neural architectures that are used to train the neural network model.

Bayesian Optimization Neural Architecture Search +2

Clustering under Local Stability: Bridging the Gap between Worst-Case and Beyond Worst-Case Analysis

no code implementations19 May 2017 Maria-Florina Balcan, Colin White

The typical idea is to design a clustering algorithm that outputs a near-optimal solution, provided the data satisfy a natural stability notion.


Robust Communication-Optimal Distributed Clustering Algorithms

no code implementations2 Mar 2017 Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan, Colin White, David Woodruff

In this work, we study the $k$-median and $k$-means clustering problems when the data is distributed across many servers and can contain outliers.


Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems

no code implementations14 Nov 2016 Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White

We address this problem for clustering, max-cut, and other partitioning problems, such as integer quadratic programming, by designing computationally efficient and sample efficient learning algorithms which receive samples from an application-specific distribution over problem instances and learn a partitioning algorithm with high expected performance.

Clustering Learning Theory

Learning Combinatorial Functions from Pairwise Comparisons

no code implementations30 May 2016 Maria-Florina Balcan, Ellen Vitercik, Colin White

However, for real-valued functions, cardinal labels might not be accessible, or it may be difficult for an expert to consistently assign real-valued labels over the entire set of examples.

BIG-bench Machine Learning

Data Driven Resource Allocation for Distributed Learning

no code implementations15 Dec 2015 Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria Florina Balcan, Alex Smola

In distributed machine learning, data is dispatched to multiple machines for processing.

$k$-center Clustering under Perturbation Resilience

no code implementations14 May 2015 Maria-Florina Balcan, Nika Haghtalab, Colin White

In this work, we take this approach and provide strong positive results both for the asymmetric and symmetric $k$-center problems under a natural input stability (promise) condition called $\alpha$-perturbation resilience [Bilu and Linia 2012], which states that the optimal solution does not change under any alpha-factor perturbation to the input distances.


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