Proximal Newton-type methods for convex optimization. - Jason D. Lee; Yuekai Sun; Michael A. Saunders (NIPS, 2012)

Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form - Jason D. Lee; Yuekai Sun; Michael A. Saunders (CoRR, 2012)

On model selection consistency of penalized M-estimators: a geometric theory. - Jason D. Lee; Yuekai Sun; Jonathan E. Taylor (NIPS, 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 regularized M-estimators. - Jason D. Lee; Yuekai Sun; Jonathan E. Taylor (CoRR, 2013)

Learning Mixtures of Linear Classifiers. - Yuekai Sun; Stratis Ioannidis; Andrea Montanari (ICML, 2014)

Proximal Newton-Type Methods for Minimizing Composite Functions. - Jason D. Lee; Yuekai Sun; Michael A. Saunders (SIAM J. Optim., 2014)

Evaluating the statistical significance of biclusters. - Jason D. Lee; Yuekai Sun; Jonathan E. Taylor (NIPS, 2015)

Communication-efficient sparse regression: a one-shot approach. - Jason D. Lee; Yuekai Sun; Qiang Liu 001; Jonathan E. Taylor (CoRR, 2015)

Feature-distributed sparse regression: a screen-and-clean approach. - Jiyan Yang; Michael W. Mahoney; Michael A. Saunders; Yuekai Sun (NIPS, 2016)

On conditional parity as a notion of non-discrimination in machine learning. - Yaacov Ritov; Yuekai Sun; Ruofei Zhao (CoRR, 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)

Communication-efficient Sparse Regression. - Jason D. Lee; Qiang Liu 001; Yuekai Sun; Jonathan E. Taylor (J. Mach. Learn. Res., 2017)

A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization. - Anil Damle; Yuekai Sun (Technometrics, 2017)

Debiasing representations by removing unwanted variation due to protected attributes. - Amanda Bower; Laura Niss; Yuekai Sun; Alexander Vargo (CoRR, 2018)

Precision Matrix Estimation with Noisy and Missing Data. - Roger Fan; Byoungwook Jang; Yuekai Sun; Shuheng Zhou 002 (AISTATS, 2019)

Dirichlet Simplex Nest and Geometric Inference. - Mikhail Yurochkin; Aritra Guha; Yuekai Sun; XuanLong Nguyen (ICML, 2019)

Learning fair predictors with Sensitive Subspace Robustness. - Mikhail Yurochkin; Amanda Bower; Yuekai Sun (CoRR, 2019)

Auditing ML Models for Individual Bias and Unfairness. - Songkai Xue; Mikhail Yurochkin; Yuekai Sun (AISTATS, 2020)

Federated Learning with Matched Averaging. - Hongyi Wang 001; Mikhail Yurochkin; Yuekai Sun; Dimitris S. Papailiopoulos; Yasaman Khazaeni (ICLR, 2020)

Training individually fair ML models with sensitive subspace robustness. - Mikhail Yurochkin; Amanda Bower; Yuekai Sun (ICLR, 2020)

Two Simple Ways to Learn Individual Fairness Metrics from Data. - Debarghya Mukherjee; Mikhail Yurochkin; Moulinath Banerjee; Yuekai Sun (ICML, 2020)

There is no trade-off: enforcing fairness can improve accuracy. - Subha Maity; Debarghya Mukherjee; Mikhail Yurochkin; Yuekai Sun (CoRR, 2020)

Uniform Bounds for Invariant Subspace Perturbations. - Anil Damle; Yuekai Sun (SIAM J. Matrix Anal. Appl., 2020)

Individually Fair Rankings. - Amanda Bower; Hamid Eftekhari; Mikhail Yurochkin; Yuekai Sun (ICLR, 2021)

Statistical inference for individual fairness. - Subha Maity; Songkai Xue; Mikhail Yurochkin; Yuekai Sun (ICLR, 2021)

Individually Fair Gradient Boosting. - Alexander Vargo; Fan Zhang; Mikhail Yurochkin; Yuekai Sun (ICLR, 2021)

SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. - Mikhail Yurochkin; Yuekai Sun (ICLR, 2021)

Outlier-Robust Optimal Transport. - Debarghya Mukherjee; Aritra Guha; Justin M. Solomon; Yuekai Sun; Mikhail Yurochkin (ICML, 2021)

On sensitivity of meta-learning to support data. - Mayank Agarwal; Mikhail Yurochkin; Yuekai Sun (NeurIPS, 2021)

Does enforcing fairness mitigate biases caused by subpopulation shift? - Subha Maity; Debarghya Mukherjee; Mikhail Yurochkin; Yuekai Sun (NeurIPS, 2021)

Post-processing for Individual Fairness. - Felix Petersen; Debarghya Mukherjee; Yuekai Sun; Mikhail Yurochkin (NeurIPS, 2021)

Individually Fair Ranking. - Amanda Bower; Hamid Eftekhari; Mikhail Yurochkin; Yuekai Sun (CoRR, 2021)

Personalization in Federated Learning. - Mayank Agarwal; Mikhail Yurochkin; Yuekai Sun (Federated Learning, 2022)

Communication-Efficient Model Fusion. - Mikhail Yurochkin; Yuekai Sun (Federated Learning, 2022)

Domain Adaptation meets Individual Fairness. And they get along. - Debarghya Mukherjee; Felix Petersen; Mikhail Yurochkin; Yuekai Sun (NeurIPS, 2022)

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. - Songkai Xue; Yuekai Sun; Mikhail Yurochkin (NeurIPS, 2022)

Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms. - Laura Niss; Yuekai Sun; Ambuj Tewari (CoRR, 2022)

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions. - Subha Maity; Yuekai Sun; Moulinath Banerjee (J. Mach. Learn. Res., 2022)

Minimax optimal approaches to the label shift problem in non-parametric settings. - Subha Maity; Yuekai Sun; Moulinath Banerjee (J. Mach. Learn. Res., 2022)

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. - Subha Maity; Debarghya Mukherjee; Moulinath Banerjee; Yuekai Sun (ICLR, 2023)

Understanding new tasks through the lens of training data via exponential tilting. - Subha Maity; Mikhail Yurochkin; Moulinath Banerjee; Yuekai Sun (ICLR, 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 (ICLR, 2023)

Simple Disentanglement of Style and Content in Visual Representations. - Lilian Ngweta; Subha Maity; Alex Gittens; Yuekai Sun; Mikhail Yurochkin (ICML, 2023)

Conditional independence testing under misspecified inductive biases. - Felipe Maia Polo; Yuekai Sun; Moulinath Banerjee (NeurIPS, 2023)

Conditional independence testing under model misspecification. - Felipe Maia Polo; Yuekai Sun; Moulinath Banerjee (CoRR, 2023)

Large Language Model Routing with Benchmark Datasets. - Tal Shnitzer; Anthony Ou; Mírian Silva; Kate Soule; Yuekai Sun; Justin Solomon 001; Neil Thompson; Mikhail Yurochkin (CoRR, 2023)

Estimating Fréchet bounds for validating programmatic weak supervision. - Felipe Maia Polo; Mikhail Yurochkin; Moulinath Banerjee; Subha Maity; Yuekai Sun (CoRR, 2023)

Aligners: Decoupling LLMs and Alignment. - Lilian Ngweta; Mayank Agarwal; Subha Maity; Alex Gittens; Yuekai Sun; Mikhail Yurochkin (EMNLP (Findings), 2024)

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness. - Seamus Somerstep; Yaacov Ritov; Yuekai Sun (FAccT, 2024)

Fusing Models with Complementary Expertise. - Hongyi Wang 001; Felipe Maia Polo; Yuekai Sun; Souvik Kundu 009; Eric P. Xing; Mikhail Yurochkin (ICLR, 2024)

An Investigation of Representation and Allocation Harms in Contrastive Learning. - Subha Maity; Mayank Agarwal; Mikhail Yurochkin; Yuekai Sun (ICLR, 2024)

Learning in reverse causal strategic environments with ramifications on two sided markets. - Seamus Somerstep; Yuekai Sun; Yaacov Ritov (ICLR, 2024)

tinyBenchmarks: evaluating LLMs with fewer examples. - Felipe Maia Polo; Lucas Weber; Leshem Choshen; Yuekai Sun; Gongjun Xu; Mikhail Yurochkin (ICML, 2024)

Weak Supervision Performance Evaluation via Partial Identification. - Felipe Maia Polo; Subha Maity; Mikhail Yurochkin; Moulinath Banerjee; Yuekai Sun (NeurIPS, 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 (NeurIPS, 2024)

Distributionally Robust Performative Prediction. - Songkai Xue; Yuekai Sun (NeurIPS, 2024)

Prompt Exploration with Prompt Regression. - Michael Feffer; Ronald Xu; 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)

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)

Microfoundation inference for strategic prediction. - Daniele Bracale; Subha Maity; Felipe Maia Polo; Seamus Somerstep; Moulinath Banerjee; Yuekai Sun (AISTATS, 2025)

Learning the Distribution Map in Reverse Causal Performative Prediction. - Daniele Bracale; Subha Maity; Yuekai Sun; Moulinath Banerjee (AISTATS, 2025)

LiveXiv - A Multi-Modal live benchmark based on Arxiv papers content. - Nimrod Shabtay; Felipe Maia Polo; Sivan Doveh; Wei Lin 019; Muhammad Jehanzeb Mirza; Leshem Choshen; Mikhail Yurochkin; Yuekai Sun; Assaf Arbelle; Leonid Karlinsky; Raja Giryes (ICLR, 2025)

A transfer learning framework for weak to strong generalization. - Seamus Somerstep; Felipe Maia Polo; Moulinath Banerjee; Yaacov Ritov; Mikhail Yurochkin; Yuekai Sun (ICLR, 2025)

Joint optimization of computation offloading and power control in user-centric networks based on dual layer mobile edge computing. - Peiying Zhang 001; Yuekai Sun; Lizhuang Tan; Maher Guizani; Mohammad Kamrul Hasan; Jian Wang 010 (Ad Hoc Networks, 2025)

Dynamic Pricing in the Linear Valuation Model using Shape Constraints. - Daniele Bracale; Moulinath Banerjee; Yuekai Sun; Salam Turki; Kevin Stoll (Trans. Mach. Learn. Res., 2025)

How does overparametrization affect performance on minority groups? - Saptarshi Roy; Subha Maity; Songkai Xue; Mikhail Yurochkin; Yuekai Sun (Trans. Mach. Learn. Res., 2025)

Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policing. - Pramit Das; Moulinath Banerjee; Yuekai Sun (CoRR, 2025)

Optimal Nonlinear Online Learning under Sequential Price Competition via s-Concavity. - Daniele Bracale; Moulinath Banerjee; Cong Shi; Yuekai Sun (CoRR, 2025)

Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation. - Seamus Somerstep; Vinod Raman; Unique Subedi; Yuekai Sun (CoRR, 2025)

Optimal Intervention for Self-triggering Spatial Networks with Application to Urban Crime Analytics. - Pramit Das; Moulinath Banerjee; Yuekai Sun (CoRR, 2025)

Bridging Human and LLM Judgments: Understanding and Narrowing the Gap. - Felipe Maia Polo; Xinhe Wang; Mikhail Yurochkin; Gongjun Xu; Moulinath Banerjee; Yuekai Sun (CoRR, 2025)