Academic Bio:

I am a Ph.D. student in CS at University of Wisconsin – Madison where I am advised by Fred Sala. Before that, I had the pleasure of working with Ameet Talwalkar and Zack Lipton during my MS at Carnegie Mellon University. As an undergraduate, I was extremely fortunate to work with both Sanjoy Dasgupta and Gary Cottrell at the University of California, San Diego. Before that, I was a community college student at Fresno City College, where I was lucky enough to learn calculus, linear algebra, and C++ from Greg Jamison.

Research Interests:

I am interested in making machine learning more accessible and applicable to new domains. Recently, I’ve been working on automated dataset construction and model selection for diverse tasks—i.e., beyond well-explored domains in ML such as vision and language.

Email: nick11roberts [at] cs [dot] wisc [dot] edu
Office: CS Dept. 5384, 1210 W Dayton St, Madison, WI 53706

• Three papers, a competition, and a workshop paper to appear at NeurIPS ‘22:
  • Benchmarking neural architecture search on diverse tasks
  • Benchmark for automated weak supervision across diverse tasks
  • We lift the favorable theoretical properties of binary weak supervision to structured prediction
  • Jointly leading the AutoML Decathlon competition at NeurIPS ‘22: an AutoML competition for diverse tasks!
    • We are partnering with the AutoML Fall School to host an AutoML Decathlon hackathon!
  • We call on the AutoML community to develop automated methods aimed at high-impact climate change problems
• Selected for the Jacquelin Perry Prove AI Fellowship!
• At ICLR ‘22: our work on lifting weak supervision to diverse settings: regression, rankings, manifolds, graphs, and more!
• At NeurIPS ‘21: we re-imagine NAS operation spaces for diverse tasks: PDE solvers, protein folding, music modeling, and beyond!
• I have started my Ph.D. at UW Madison!

• Escaping Label Subspaces via Label Geometry
  Nicholas Roberts, Xintong Li, Dyah Adila, Sonia Cromp, Tzu-Heng Huang, Jitian Zhao, Frederic Sala.
  Under review.
  

• Generative Modeling Helps Weak Supervision (and Vice Versa)
  Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski.
  Under review.
  [arXiv]

• AutoML for Climate Change: A Call to Action
  Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White.
  NeurIPS 2022 Tackling Climate Change with Machine Learning Workshop.
  [arXiv]

• AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale
  Samuel Guo*, Cong Xu*, Nicholas Roberts*, Mikhail Khodak, Junhong Shen, Evan Sparks, Yuriy Nevmyvaka, Frederic Sala, Kashif Rasul, Anderson Schneider, Ameet Talwalkar.
  NeurIPS 2022 Competition.
  [Website] [Submission Site] [Code] [Blog]

• AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
  Nicholas Roberts*, Xintong Li*, Tzu-Heng Huang, Dyah Adila, Spencer Schoenberg, Cheng-Yu Liu, Lauren Pick, Haotian Ma, Aws Albarghouthi, Frederic Sala.
  NeurIPS 2022.
  [Paper] [arXiv] [Code] [Blog]

• NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
  Renbo Tu*, Nicholas Roberts*, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar.
  NeurIPS 2022.
  [Paper] [arXiv] [Code] [Website] [Blog]

• Lifting Weak Supervision To Structured Prediction
  Harit Vishwakarma, Nicholas Roberts, Frederic Sala.
  NeurIPS 2022.
  [Paper] [arXiv]

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
  Aarohi Srivastava, ..., Nicholas Roberts (276), ..., (442 authors).
  Preprint.
  [arXiv] [Code]
  

• NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
  Kaustubh D. Dhole, ..., Nicholas Roberts (85), ..., (128 authors).
  Preprint.
  [arXiv] [Code]
  

• Universalizing Weak Supervision
  Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, Frederic Sala.
  ICLR 2022.
  [arXiv]

• Rethinking Neural Operations for Diverse Tasks
  Nicholas Roberts*, Mikhail Khodak*, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar.
  NeurIPS 2021.
  [arXiv] [Code] [Software Package] [Talk]
  
  Preliminary version: Searching for Convolutions and a More Ambitious NAS
  Nicholas Roberts*, Mikhail Khodak*, Tri Dao, Liam Li, Maria-Florina Balcan, Christopher Ré, Ameet Talwalkar.
  Plenary Talk: AAAI 2021 Workshop on Learning Network Architecture During Training.
  

• Decoding and Diversity in Machine Translation
  Nicholas Roberts, Davis Liang, Graham Neubig, Zachary C. Lipton.
  NeurIPS 2020 Resistance AI Workshop.
  [arXiv]

• A Simple Setting for Understanding Neural Architecture Search with Weight-Sharing
  Mikhail Khodak, Liam Li, Nicholas Roberts, Maria-Florina Balcan, Ameet Talwalkar.
  ICML 2020 AutoML Workshop.
  [Paper]

• Weight-Sharing Beyond Neural Architecture Search: Efficient Feature Map Selection and Federated Hyperparameter Tuning
  Mikhail Khodak*, Liam Li*, Nicholas Roberts, Maria-Florina Balcan, Ameet Talwalkar.
  MLSys 2020 On-Device Intelligence Workshop.
  [Paper]

• Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks
  Nicholas Roberts, Dian Ang Yap, Vinay U. Prabhu.
  NeurIPS 2019 Real Neurons and Hidden Units Workshop.
  [arXiv]

• Using Deep Siamese Neural Networks to Speed up Natural Products Research
  Nicholas Roberts, Poornav S. Purushothama, Vishal T. Vasudevan, Siddarth Ravichandran, Chen Zhang, William H. Gerwick, Garrison W. Cottrell.
  NeurIPS 2019 workshop on Machine Learning and the Physical Sciences.
  [Paper]

• Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity
  Dian Ang Yap, Nicholas Roberts, Vinay U. Prabhu.
  NeurIPS 2019 workshop on Bayesian Deep Learning.
  NeurIPS 2019 workshop on Information Theory and Machine Learning.
  [arXiv]

• Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker
  Nicholas Roberts, Vinay U. Prabhu, Matthew McAteer.
  Spotlight: ICML 2019 workshop on Security and Privacy of Machine Learning.
  [arXiv]

• Learning from Discriminative Feature Feedback
  Sanjoy Dasgupta, Akansha Dey, Nicholas Roberts, Sivan Sabato.
  NeurIPS 2018.
  [Paper]

• Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research
  Chen Zhang*, Yerlan Idelbayev*, Nicholas Roberts, Yiwen Tao, Yashwanth Nannapaneni, Brendan M. Duggan, Jie Min, Eugene C. Lin, Erik C. Gerwick, Garrison W. Cottrell, William H. Gerwick.
  Scientific Reports 2017.
  [Paper]
  
  Poster: Small Molecule Accurate Recognition Technology (SMART): A Digital Frontier to Reshape Natural Product Research
  Chen Zhang*, Yerlan Idelbayev*, Nicholas Roberts (presenter), Yiwen Tao, Yashwanth Nannapaneni, Brendan M. Duggan, Jie Min, Eugene C. Lin, Erik C. Gerwick, Garrison W. Cottrell, William H. Gerwick.
  Best Spotlight Presentation Award: Applied Machine Learning Days 2018.
  

University of Wisconsin–Madison

Ph.D. Computer Science
August 2021 - Present

• Member of the Hoofer Sailing Club
  

Carnegie Mellon University

M.S. Machine Learning
August 2019 - May 2021

• MSML Student Committee Leader
  

University of California, San Diego

B.S. Computer Science
Mathematics minor
CSE Honors Program
September 2015 - March 2019
Magna Cum Laude
CSE Highest Distinction

• Tutor for Data Science 10 (Principles of Data Science)
  
• Tutor for Data Science 20 (Data Structures for Data Science)
  
• Data analyst, Triton Engineering Student Council
  
• Machine learning workshop facilitator, Data Science Student Society at UCSD
  
• House leader, Tau Beta Pi, California Psi
  

Fresno City College

Computer Science
Leon S. Peters Honors Program
August 2013 - May 2015

• Tutor for CIT 65 (Android Application Development)
  
• Mathematics Tutor
  
• Computer Science Tutor
  
• President/Founder, Google Developer Group Fresno City College
  
• Treasurer, Science and Engineering Club
  

Sala Group

research assistant

• Research related to Weak Supervision and Automated Machine Learning supervised by Fred Sala.

SAGE Lab

research assistant

• Research related to Neural Architecture Search supervised by Ameet Talwalkar

Amazon AI

applied scientist intern

• Researched and developed methods for hypothesis rescoring in ASR systems using neural language modeling
• Identified areas for improvement in many existing ASR systems when recognizing rare or zero shot entities
• Technologies used: Python, PyTorch, RWTH ASR, Kaldi, AWS

UnifyID

ai fellow
machine learner intern

• Researched various ways in which research from network neuroscience could be applied to deep learning
• Developed a novel model extraction attack against deep learning models for computer vision using just noise inputs
• Technologies used: Python, Keras, PyTorch, TensorFlow, MATLAB, AWS

Intuit Futures

software engineering intern

• Researched and implemented a novel deep learning model for controllable text generation as a service within Intuit
• Developed a system for proposing alternative candidate sentences for Intuit content writers using deep learning
• Investigated the use of dynamic topic models for customer support tickets to gain actionable insights over time
• Technologies used: Python, PyTorch, TensorFlow, Gensim, Keras

Altum

applied scientist intern

• Developed language model to extract NLP features from text data regarding cryptocurrency trading
• Investigated unsupervised learning techniques for extracting sentiment data in real time from online forums
• Technologies used: Python, PyTorch

The Cottrell Lab (GURU: Gary's Unbelievable Research Unit)

undergraduate researcher

• Published, Scientific Reports:
• "Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research"
• Presented SMART research at the Applied Machine Learning Days 2018 conference at EPFL, Lausanne, Switzerland
  • Won an award for Best Spotlight Presentation
• Analyzed performance of deep learning system for natural products research with Scripps Institute of Oceanography
• Explored the effects of artificial experimental noise added to the dataset and showed resistance to gaussian noise
• Improved quality of image dataset by identifying and handling noisy outliers using principal component analysis
• Technologies used: Python, Tensorflow, Lasagne, Theano, SciPy

Teradata

software engineering intern

• Developed open source Spark-Teradata connector forked from Databricks’ connector for AWS Redshift in Scala
• Designed and implemented Teradata stored procedures in Java to mimic Redshift’s UNLOAD and COPY using S3
• Improved training methodology and architecture of deep learning time series model used internally
• Implemented system for updating the time series dataset and fine tuning the deep learning model
• Technologies used: Scala, Java, Maven, Teradata SQL, AWS, Tensorflow, Flask

The Comeback Community

volunteer full stack developer

• Developed site in Go, gohtml, and CSS on Google App Engine
• Mentored new developers in web development
• Technologies used: Go, Google App Engine, gohtml, HTML5, CSS3, JavaScript

Skqrl

software engineering intern

• Developed web crawler to compile needfinding and product data using Scrapy and Selenium
• Designed and implemented an extensible product search solution designed to handle future user search needs
• Technologies used: Python, Scrapy, Selenium, Django, MySQL, JavaScript

ModSpot

software engineering intern

• Implemented new user account, edit profile, and login designs in Objective-C for iOS application
• Refactored analytics code for gathering statistics on app usage, helping designers make more informed choices
• Technologies used: Objective-C, Cocoa Touch, Flurry Analytics

Extracurricular:

• Sailing
• Photography
• Pottery

Extra-Extracurricular:

• Ordained Dudeist priest. I can legally officiate weddings in the US. If you’d like to book me for your wedding, please free to reach out.
• Head Researcher of Margarita Machine Lounge Therapy at Vacation Inc. Check out our selection of luxury sunscreens today!¹
• Unofficial Toyota Prius landspeed record holder at Bonneville Speedway.

¹Don’t forget to check out Poolsuite FM: the ultra-summer music player for the Macintosh Computer; transporting you to a virtual vacation where the sun never sets.