Yuekai Sun

According to our database, Yuekai Sun authored at least 63 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families.

Felipe Maia Polo, Seamus Somerstep, Leshem Choshen, Yuekai Sun, Mikhail Yurochkin

CoRR, 2024

Distributionally Robust Performative Prediction.

Songkai Xue, Yuekai Sun

CoRR, 2024

Microfoundation Inference for Strategic Prediction.

Daniele Bracale, Subha Maity, Felipe Maia Polo, Seamus Somerstep, Moulinath Banerjee, Yuekai Sun

CoRR, 2024

LiveXiv - A Multi-Modal Live Benchmark Based on Arxiv Papers Content.

Nimrod Shabtay, Felipe Maia Polo, Sivan Doveh, Wei Lin, Muhammad Jehanzeb Mirza, Leshem Choshen, Mikhail Yurochkin, Yuekai Sun, Assaf Arbelle, Leonid Karlinsky, Raja Giryes

CoRR, 2024

Efficient multi-prompt evaluation of LLMs.

Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin

CoRR, 2024

A statistical framework for weak-to-strong generalization.

Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee, Yaacov Ritov, Mikhail Yurochkin, Yuekai Sun

CoRR, 2024

Learning the Distribution Map in Reverse Causal Performative Prediction.

Daniele Bracale, Subha Maity, Moulinath Banerjee, Yuekai Sun

CoRR, 2024

Prompt Exploration with Prompt Regression.

Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin

CoRR, 2024

tinyBenchmarks: evaluating LLMs with fewer examples.

Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning in reverse causal strategic environments with ramifications on two sided markets.

Seamus Somerstep, Yuekai Sun, Yaacov Ritov

Proceedings of the Twelfth International Conference on Learning Representations, 2024

An Investigation of Representation and Allocation Harms in Contrastive Learning.

Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Fusing Models with Complementary Expertise.

Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness.

Seamus Somerstep, Yaacov Ritov, Yuekai Sun

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Aligners: Decoupling LLMs and Alignment.

Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2023

Estimating Fréchet bounds for validating programmatic weak supervision.

Felipe Maia Polo, Mikhail Yurochkin, Moulinath Banerjee, Subha Maity, Yuekai Sun

CoRR, 2023

Large Language Model Routing with Benchmark Datasets.

Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, Mikhail Yurochkin

CoRR, 2023

Conditional independence testing under model misspecification.

Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee

CoRR, 2023

Conditional independence testing under misspecified inductive biases.

Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Simple Disentanglement of Style and Content in Visual Representations.

Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin

Proceedings of the International Conference on Machine Learning, 2023

ISAAC Newton: Input-based Approximate Curvature for Newton's Method.

Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Understanding new tasks through the lens of training data via exponential tilting.

Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift.

Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Minimax optimal approaches to the label shift problem in non-parametric settings.

Subha Maity, Yuekai Sun, Moulinath Banerjee

J. Mach. Learn. Res., 2022

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions.

Subha Maity, Yuekai Sun, Moulinath Banerjee

J. Mach. Learn. Res., 2022

How does overparametrization affect performance on minority groups?

Subha Maity, Saptarshi Roy, Songkai Xue, Mikhail Yurochkin, Yuekai Sun

CoRR, 2022

Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms.

Laura Niss, Yuekai Sun, Ambuj Tewari

CoRR, 2022

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees.

Songkai Xue, Yuekai Sun, Mikhail Yurochkin

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Domain Adaptation meets Individual Fairness. And they get along.

Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Communication-Efficient Model Fusion.

Mikhail Yurochkin, Yuekai Sun

Proceedings of the Federated Learning, 2022

Personalization in Federated Learning.

Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun

Proceedings of the Federated Learning, 2022

2021

Individually Fair Ranking.

Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun

CoRR, 2021

Post-processing for Individual Fairness.

Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Does enforcing fairness mitigate biases caused by subpopulation shift?

Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On sensitivity of meta-learning to support data.

Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Outlier-Robust Optimal Transport.

Debarghya Mukherjee, Aritra Guha, Justin M. Solomon, Yuekai Sun, Mikhail Yurochkin

Proceedings of the 38th International Conference on Machine Learning, 2021

SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness.

Mikhail Yurochkin, Yuekai Sun

Proceedings of the 9th International Conference on Learning Representations, 2021

Individually Fair Gradient Boosting.

Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun

Proceedings of the 9th International Conference on Learning Representations, 2021

Statistical inference for individual fairness.

Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun

Proceedings of the 9th International Conference on Learning Representations, 2021

Individually Fair Rankings.

Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Uniform Bounds for Invariant Subspace Perturbations.

Anil Damle, Yuekai Sun

SIAM J. Matrix Anal. Appl., 2020

There is no trade-off: enforcing fairness can improve accuracy.

Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun

CoRR, 2020

Two Simple Ways to Learn Individual Fairness Metrics from Data.

Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun

Proceedings of the 37th International Conference on Machine Learning, 2020

Training individually fair ML models with sensitive subspace robustness.

Mikhail Yurochkin, Amanda Bower, Yuekai Sun

Proceedings of the 8th International Conference on Learning Representations, 2020

Federated Learning with Matched Averaging.

Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni

Proceedings of the 8th International Conference on Learning Representations, 2020

Auditing ML Models for Individual Bias and Unfairness.

Songkai Xue, Mikhail Yurochkin, Yuekai Sun

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Learning fair predictors with Sensitive Subspace Robustness.

Mikhail Yurochkin, Amanda Bower, Yuekai Sun

CoRR, 2019

Dirichlet Simplex Nest and Geometric Inference.

Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen

Proceedings of the 36th International Conference on Machine Learning, 2019

Precision Matrix Estimation with Noisy and Missing Data.

Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Debiasing representations by removing unwanted variation due to protected attributes.

Amanda Bower, Laura Niss, Yuekai Sun, Alexander Vargo

CoRR, 2018

2017

A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization.

Anil Damle, Yuekai Sun

Technometrics, 2017

Communication-efficient Sparse Regression.

Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor

J. Mach. Learn. Res., 2017

An inexact subsampled proximal Newton-type method for large-scale machine learning.

Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun

CoRR, 2017

On conditional parity as a notion of non-discrimination in machine learning.

Yaacov Ritov, Yuekai Sun, Ruofei Zhao

CoRR, 2017

2016

Feature-distributed sparse regression: a screen-and-clean approach.

Jiyan Yang, Michael W. Mahoney, Michael A. Saunders, Yuekai Sun

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Communication-efficient sparse regression: a one-shot approach.

Jason D. Lee, Yuekai Sun, Qiang Liu, Jonathan E. Taylor

CoRR, 2015

Evaluating the statistical significance of biclusters.

Jason D. Lee, Yuekai Sun, Jonathan E. Taylor

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Proximal Newton-Type Methods for Minimizing Composite Functions.

Jason D. Lee, Yuekai Sun, Michael A. Saunders

SIAM J. Optim., 2014

Learning Mixtures of Linear Classifiers.

Yuekai Sun, Stratis Ioannidis, Andrea Montanari

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

On model selection consistency of regularized M-estimators.

Jason D. Lee, Yuekai Sun, Jonathan E. Taylor

CoRR, 2013

Robust flux balance analysis of multiscale biochemical reaction networks.

Yuekai Sun, Ronan M. T. Fleming, Ines Thiele, Michael A. Saunders

BMC Bioinform., 2013

On model selection consistency of penalized M-estimators: a geometric theory.

Jason D. Lee, Yuekai Sun, Jonathan E. Taylor

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form

Jason D. Lee, Yuekai Sun, Michael A. Saunders

CoRR, 2012

Proximal Newton-type methods for convex optimization.

Jason D. Lee, Yuekai Sun, Michael A. Saunders

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012