Lunjia Hu
Hi! I have graduated from my Stanford CS PhD program. Here is my new website with updated information about me and my research.
I'm excited to join Northeastern University in Fall 2025 as an Assistant Professor in the Khoury College of Computer Sciences!
I’m looking for students to join the Northeastern CS PhD program in Fall 2025. If you’re interested in doing fun research with me, apply to the program and mention my name in your application. Also, feel free to email me if you are interested in my work.
Preprints:
Testing Calibration in Nearly-Linear Time. [arXiv]
Lunjia Hu, Arun Jambulapati, Kevin Tian, Chutong Yang
Conference papers:
Predict to Minimize Swap Regret for All Payoff-Bounded Tasks. [arXiv]
Lunjia Hu, Yifan Wu
To appear in FOCS 2024
On Computationally Efficient Multi-Class Calibration. [arXiv]
Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum
COLT 2024
Multigroup Robustness. [arXiv]
Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
ICML 2024
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran
ITCS 2024
When Does Optimizing a Proper Loss Yield Calibration? [arXiv]
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
NeurIPS 2023 (Spotlight)
Simple, Scalable and Effective Clustering via One-Dimensional Projections. [arXiv]
Moses Charikar, Monika Henzinger, Lunjia Hu, Maximilian Vötsch, Erik Waingarten
NeurIPS 2023
Generative Models of Huge Objects. [arXiv]
Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold
CCC 2023
Omnipredictors for Constrained Optimization. [arXiv] [talk at the Simons Institute]
Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang
ICML 2023
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
STOC 2023
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes. [arXiv] [video] [Charlotte's talk at the Simons Institute]
Lunjia Hu, Charlotte Peale
ITCS 2023
Loss Minimization through the Lens of Outcome Indistinguishability. [arXiv] [video by Michael]
Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, Udi Wieder
ITCS 2023
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. [arXiv]
John Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
NeurIPS 2022
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability. [arXiv]
Lunjia Hu, Charlotte Peale, Omer Reingold
ALT 2022
E. M. Gold Best Student Paper Award
An Improved Local Search Algorithm for k-Median. [arXiv]
Vincent Cohen-Addad, Anupam Gupta, Lunjia Hu, Hoon Oh, David Saulpic
SODA 2022
Near-Optimal Explainable k-Means for All Dimensions. [arXiv] [talk at the IDEAL Workshop]
Moses Charikar, Lunjia Hu
SODA 2022
Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers. [arXiv]
Lunjia Hu, Omer Reingold
AISTATS 2021
Approximation Algorithms for Orthogonal Non-negative Matrix Factorization. [arXiv]
Moses Charikar, Lunjia Hu
AISTATS 2021
The Power of Many Samples in Query Complexity. [conference version] [arXiv]
Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan
ICALP 2020
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. [conference version] [arXiv] [spotlight talk]
Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, John Hopcroft
NeurIPS 2018 (Spotlight)
Active Tolerant Testing. [conference version] [arXiv] [conference talk]
Avrim Blum, Lunjia Hu.
COLT 2018
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes. [conference version] [arXiv]
Lunjia Hu, Ruihan Wu, Tianhong Li, Liwei Wang.
COLT 2017
Capacitated Center Problems with Two-Sided Bounds and Outliers. [conference version] [arXiv]
Hu Ding, Lunjia Hu, Lingxiao Huang, Jian Li.
WADS 2017
Professional Activities
Conference & Journal Reviewing: ALT 2019, NeurIPS (2019, 2020, 2023), SODA (2022, 2023), STOC (2022, 2024), ESA 2022, FORC 2023, FOCS 2023, ITCS 2024, COLT 2024, APPROX 2024, JMLR
TAing:
The Practice of Theory Research (CS163), Winter 2020-2021, Stanford.
I was selected as one of the top 5% course assistants in the Computer Science Department at Stanford University in Winter 2020-2021.Randomized Algorithms and Probabilistic Analysis (CS265/CME309), Fall 2020-2021, Stanford.
Other:
I wrote two blog posts on Stanford’s CS theory research blog (Theory Dish) about the sunflower lemma and the backpropagation algorithm.
Contact Information
Email: [first name] [at] alumni [dot] stanford [dot] edu
