Shivam Gupta

I’m a PhD student in Computer Science at UT Austin, advised by Eric Price. My research interests are in statistics, machine learning, and theoretical computer science. Recently, I have been most interested in diffusion models. CV Email: shivamgupta@utexas.edu

Papers

• Diffusion Posterior Sampling is Computationally Intractable
  Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price, and Zhiyang Xun
  In Submission
  
• Sample-Efficient Training for Diffusion
  Shivam Gupta, Aditya Parulekar, Eric Price, and Zhiyang Xun
  In Submission
  
• Beyond Catoni: Sharper Rates for Heavy-Tailed and Robust Mean Estimation
  Shivam Gupta, Samuel B. Hopkins, Eric Price
  In Submission
  
• Minimax-Optimal Location Estimation
  Shivam Gupta, Jasper C.H. Lee, Eric Price, and Paul Valiant
  NeurIPS 2023
  
• Finite-Sample Symmetric Mean Estimation with Fisher Information Rate
  Shivam Gupta, Jasper C.H. Lee, and Eric Price
  COLT 2023
  [poster] [slides]
• High-Dimensional Location Estimation via Norm Concentration for Subgamma Vectors
  Shivam Gupta, Jasper C.H. Lee, and Eric Price
  ICML 2023
  
• Finite-Sample Maximum Likelihood Estimation of Location
  Shivam Gupta, Jasper C.H. Lee, Eric Price, and Paul Valiant
  NeurIPS 2022
  
• Outlier-Robust Sparse Estimation via Non-Convex Optimization
  Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel M. Kane, and Mahdi Soltanolkotabi
  NeurIPS 2022
  
• Sharp Constants in Unformity Testing via the Huber Statistic
  Shivam Gupta and Eric Price
  COLT 2022
  Featured on Property Testing Review
  [slides]
• Nash Equilibrium Computation in Resource Allocation Games
  Shivam Gupta and Ruta Mehta
  AAMAS 2018
  

Talks

1. Sample-Efficient Training for Diffusion

   • IFML Workshop on Generative AI, UT Austin

2. A Finite-Sample Theory for Mean Estimation with Fisher Information Rate

   • CMU Theory Lunch (Oct 2023)

   • MIT Algorithms and Complexity Seminar (Oct 2023)

3. Finite-Sample Symmetric Mean Estimation with Fisher Information Rate

   • COLT 2023

Teaching

1. At UT Austin:

   • Fall 2020: TA, Sublinear Algorithms (CS 395T)
   • Spring 2020: TA, Introduction to Algorithms (CS 331)
   • Summer 2019, Fall 2019: TA, Machine Learning (CS 391L)
   • Spring 2019: TA, Elements of Data Analytics (CS 329E)
   • Fall 2018: TA, Elements of Computer Programming (CS 303E)

2. At UIUC:

   • Spring 2018: Course Assistant, Introduction to Algorithms (CS 374) and Grader, Algorithms II (CS 473)