75 citations · 227 across the 20 of their papers we have counts for
23 papers
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Renbo Tu, Colin White, Jean Kossaifi +5
Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces. However, m…
Neural Architecture Search: Insights from 1000 Papers
Colin White, Mahmoud Safari, Rhea Sukthanker +5
In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, a…
Speeding up NAS with Adaptive Subset Selection
Vishak Prasad C, Colin White, Paarth Jain +2
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 perfo…
AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad +7
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate c…
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
Arjun Krishnakumar, Colin White, Arber Zela +3
Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS). Recent w…
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Samuel Dooley, Rhea Sanjay Sukthanker, John P. Dickerson +3
Face recognition systems are widely deployed in safety-critical applications, including law enforcement, yet they exhibit bias across a range of socio-demographic dimensions, such…
On the Generalizability and Predictability of Recommender Systems
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde +2
While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, r…
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
Yash Mehta, Colin White, Arber Zela +6
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 archit…
NAS-Bench-x11 and the Power of Learning Curves
Shen Yan, Colin White, Yash Savani +1
While early research in neural architecture search (NAS) required extreme computational resources, the recent releases of tabular and surrogate benchmarks have greatly increased th…
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
Yang Liu, Sujay Khandagale, Colin White +1
As machine learning models grow more complex and their applications become more high-stakes, tools for explaining model predictions have become increasingly important. This has spu…
How Powerful are Performance Predictors in Neural Architecture Search?
Colin White, Arber Zela, Binxin Ru +2
Early methods in the rapidly developing field of neural architecture search (NAS) required fully training thousands of neural networks. To reduce this extreme computational cost, d…
A Study on Encodings for Neural Architecture Search
Colin White, Willie Neiswanger, Sam Nolen +1
Neural architecture search (NAS) has been extensively studied in the past few years. A popular approach is to represent each neural architecture in the search space as a directed a…
Intra-Processing Methods for Debiasing Neural Networks
Yash Savani, Colin White, Naveen Sundar Govindarajulu
As deep learning models become tasked with more and more decisions that impact human lives, such as criminal recidivism, loan repayment, and face recognition for law enforcement, b…
Exploring the Loss Landscape in Neural Architecture Search
Colin White, Sam Nolen, Yash Savani
Neural architecture search (NAS) has seen a steep rise in interest over the last few years. Many algorithms for NAS consist of searching through a space of architectures by iterati…
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
Colin White, Willie Neiswanger, Yash Savani
Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimiza…
Data-Driven Clustering via Parameterized Lloyd's Families
Maria-Florina Balcan, Travis Dick, Colin White
Algorithms for clustering points in metric spaces is a long-studied area of research. Clustering has seen a multitude of work both theoretically, in understanding the approximation…
Clustering under Local Stability: Bridging the Gap between Worst-Case and Beyond Worst-Case Analysis
Maria-Florina Balcan, Colin White
Recently, there has been substantial interest in clustering research that takes a beyond worst-case approach to the analysis of algorithms. The typical idea is to design a clusteri…
Robust Communication-Optimal Distributed Clustering Algorithms
Pranjal Awasthi, Ainesh Bakshi, Maria-Florina Balcan +2
In this work, we study the -median and -means clustering problems when the data is distributed across many servers and can contain outliers. While there has been a lot of wor…
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik +1
Max-cut, clustering, and many other partitioning problems that are of significant importance to machine learning and other scientific fields are NP-hard, a reality that has motivat…
Learning Combinatorial Functions from Pairwise Comparisons
Maria-Florina Balcan, Ellen Vitercik, Colin White
A large body of work in machine learning has focused on the problem of learning a close approximation to an underlying combinatorial function, given a small set of labeled examples…
Data Driven Resource Allocation for Distributed Learning
Travis Dick, Mu Li, Venkata Krishna Pillutla +3
In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes,…
-center Clustering under Perturbation Resilience
Maria-Florina Balcan, Nika Haghtalab, Colin White
The -center problem is a canonical and long-studied facility location and clustering problem with many applications in both its symmetric and asymmetric forms. Both versions of…
Lower Bounds in the Preprocessing and Query Phases of Routing Algorithms
Colin White
In the last decade, there has been a substantial amount of research in finding routing algorithms designed specifically to run on real-world graphs. In 2010, Abraham et al. showed…