See the research page for representative papers and Google Scholar for citation metrics.

**Distributionally robust performative prediction**

S Xue, Y Sun. to appear in *NeurIPS 2024*.

**Efficient multi-prompt evaluation of LLMs**

F Maia Polo, R Xu, L Weber, M Silva, O Bhardwaj, L Choshen, A Oliveira, Y Sun, M Yurochkin. to appear in *NeurIPS 2024*.

/microsoft/promptbench

**A transfer learning framework for weak-to-strong generalization**

S Somerstep, F Maia Polo, M Banerjee, Y Ritov, M Yurochkin, Y Sun.

**Learning the Distribution Map in Reverse Causal Performative Prediction**

D Bracale, S Maity, S Somerstep, M Banerjee, Y Sun.

**Estimating Frechet bounds for validating programmatic weak supervision**

F Maia Polo, M Yurochkin, M Banerjee, S Maity, Y Sun. to appear in *NeurIPS 2024*.

**A linear adjustment based approach to posterior drift in transfer learning**

S Maity, D Dutta, J Terhorst, Y Sun, M Banerjee. *Biometrika* (2024).

/smaityumich/linearly-shifted-transfer

**Minimax optimal approaches to the label shift problem**

S Maity, Y Sun, M Banerjee. *Journal of Machine Learning Research* (2022).

/smaityumich/label-shift

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

S Maity, Y Sun, M Banerjee. *Journal of Machine Learning Research* (2022).

/smaityumich/MrLasso

**Matrix Completion Methods for the Total Electron Content Video Reconstruction**

H Sun, Z Hua, J Ren, S Zou, Y Sun, Y Chen. *Annals of Applied Statistics* (2022).

/husun0822/TEC_impute

**Uniform bounds for invariant subspace perturbations**

A Damle, Y Sun. *SIAM Journal of Matrix Analysis and Applications* (2020).

/asdamle/rowwise-perturbation

**Statistical convergence of the EM algorithm on Gaussian mixture models**

R Zhao, Y Li, Y Sun. *Electronic Journal of Statistics* (2020).

**Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0**

L Heirendt et al. *Nature Protocols* (2019).

/opencobra/cobratoolbox

**A geometric approach to archetypal analysis and nonnegative matrix factorization**

A Damle, Y Sun. *Technometrics* (2017).

/yuekai/archetypes

ASQ Jack Youden Award

**Communication-efficient sparse regression**

JD Lee, Q Liu, Y Sun, JE Taylor. *Journal of Machine Learning Research* (2017).

**Exact post-selection inference, with application to the lasso**

JD Lee, DL Sun, Y Sun, JE Taylor. *Annals of Statistics* (2016).

/selective-inference

**Do genome‐scale models need exact solvers or clearer standards?**

A Ebrahim et al. *Molecular Systems Biology* (2015).

**Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data**

L Yang et al. *Proceedings of the National Academy of Sciences* (2015).

**On model selection consistency of regularized M-estimators**

JD Lee, Y Sun, JE Taylor. *Electronic Journal of Statistics* (2015).

A conference version appeared at *NIPS 2013*.

**Proximal Newton-type methods for minimizing composite functions**

JD Lee, Y Sun, MA Saunders. *SIAM Journal on Optimization* (2014).

/yuekai/PNOPT

A conference version appeared at *NIPS 2012*.

**Humidity effects on anisotropic nanofriction behaviors of aligned carbon nanotube carpets**

J Zhang, H Lu, Y Sun, L Ci, PM Ajayan, J Lou. *ACS Applied Materials & Interfaces* (2013).

**Robust flux balance analysis of multiscale biochemical reaction networks**

Y Sun, RMT Fleming, I Thiele, MA Saunders. *BMC Bioinformatics* (2013).

/opencobra/cobratoolbox

**Nanostructure on taro leaves resists fouling by colloids and bacteria under submerged conditions**

J Ma, Y Sun, K Gleichauf, J Lou, Q Li. *Langmuir* (2011).

**Regular and reverse nanoscale stick-slip behavior: Modeling and experiments**

F Landolsi, Y Sun, H Lu, FH Ghorbel, J Lou. *Applied Surface Science* (2010).

**Nanoscale friction dynamic modeling**

F Landolsi, FH Ghorbel, J Lou, H Lu, Y Sun. *ASME Journal of Dynamic Systems, Measurement & Control* (2009).

**Friction and adhesion properties of vertically aligned multi-walled carbon nanotube arrays and fluoro-nanodiamond films**

H Lu, J Goldman, F Ding, Y Sun, MX Pulikkathara, VN Khabashesku, BI Yakobson, J Lou. *Carbon* (2008).

**Mesoscale reverse stick-slip nanofriction behavior of vertically aligned multiwalled carbon nanotube superlattices**

J Lou, F Ding, H Lu, J Goldman, Y Sun, BI Yakobson. *Applied Physics Letters* (2008).

**Aligners: Decoupling LLMs and Alignment**

L Ngweta, M Agarwal, S Maity, A Gittens, Y Sun, M Yurochkin. *EMNLP Findings 2024*.

A short version appeared as a *ICLR 2024 TinyPaper*.

**Prompt Exploration with Prompt Regression**

M Feffer, R Xu, Y Sun, M Yurochkin. *COLM 2024*.

**Large Language Model Routing with Benchmark Datasets**

T Shnitzer, A Ou, M Silva, K Soule, Y Sun, J Solomon, N Thompson, M Yurochkin. *COLM 2024*.

**tinyBenchmarks: evaluating LLMs with few examples**

F Maia Polo, L Weber, L Choshen, Y Sun, G Xu, M Yurochkin. *ICML 2024*.

/felipemaiapolo/tinyBenchmarks

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

S Somerstep, Y Ritov, Y Sun. *FAccT 2024*.

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

S Somerstep, Y Sun, Y Ritov. *ICLR 2024*.

**Fusing Models with Complementary Expertise**

H Wang, F Maia Polo, Y Sun, S Kundu, E Xing, M Yurochkin. *ICLR 2024*.

**An Investigation of Representation and Allocation Harms in Contrastive Learning**

S Maity, M Agarwal, M Yurochkin, Y Sun. *ICLR 2024*.

**Conditional independence testing under misspecified inductive biases**

F Maia Polo, Y Sun, M Banerjee. *NeurIPS 2023*.

**Simple Disentanglement of Style and Content in Visual Representations**

L Ngweta, S Maity, A Gittens, Y Sun, M Yurochkin. *ICML 2023*.

/lilianngweta/PISCO

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

S Maity, M Yurochkin, M Banerjee, Y Sun. *ICLR 2023*.

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

S Maity, D Mukherjee, M Banerjee, Y Sun. *ICLR 2023*.

/smaityumich/concept-drift

**ISAAC Newton: Input-based Approximate Curvature for Newton’s Method**

F Petersen, T Sutter, C Borgelt, D Huh, H Kuehne, Y Sun, O Deussen. *ICLR 2023*.

**Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees**

S Xue, M Yurochkin, Y Sun. *NeurIPS 2022*.

**Domain Adaptation meets Individual Fairness. And they get along.**

D Mukherjee, F Petersen, M Yurochkin, Y Sun. *NeurIPS 2022*.

**Post-processing for Individual Fairness**

F Petersen, D Mukherjee, Y Sun, M Yurochkin. *NeurIPS 2021*.

/Felix-Petersen/fairness-post-processing

**On sensitivity of meta-learning to support data**

M Agarwal, M Yurochkin, Y Sun. *NeurIPS 2021*.

**Does enforcing fairness mitigate biases caused by subpopulation shift?**

S Maity, D Mukherjee, M Yurochkin, Y Sun. *NeurIPS 2021*.

**Outlier-Robust Optimal Transport**

D Mukherjee, A Guha, J Solomon, Y Sun, M Yurochkin. *ICML 2021*.

/debarghya-mukherjee/Robust-Optimal-Transport

**Statistical Inference for Individual Fairness**

S Maity, S Xue, M Yurochkin, Y Sun. *ICLR 2021*.

/smaityumich/individual-fairness-testing

**Individually Fair Rankings**

A Bower, H Eftekhari, M Yurochkin, Y Sun. *ICLR 2021*.

**Individually fair gradient boosting**

A Vargo, F Zhang, M Yurochkin, Y Sun. *ICLR 2021*.

**SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness**

M Yurochkin, Y Sun. *ICLR 2021*.

**Two simple ways to learn individual fairness metrics from data**

D Mukherjee, M Yurochkin, M Banerjee, Y Sun. *ICML 2020*.

/debarghya-mukherjee/Fair_metric_learning

**Auditing ML models for individual bias and unfairness**

S Xue, M Yurochkin, Y Sun. *AISTATS 2020*.

**Federated Learning with Matched Averaging**

H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni. *ICLR 2020*.

/IBM/FedMA

**Training individually fair machine learning models with Sensitive Subspace Robustness**

M Yurochkin, A Bower, Y Sun. *ICLR 2020*.

/IBM/sensitive-subspace-robustness

**Dirichlet Simplex Nest and Geometric Inference**

M Yurochkin, A Guha, Y Sun, XL Nguyen. *ICML 2019*.

/moonfolk/VLAD

**Precision Matrix Estimation with Noisy and Missing Data**

R Fan, B Jang, Y Sun, S Zhou. *AISTATS 2019*.

**Debiasing representations by removing unwanted variation due to protected attributes**

A Bower, L Niss, Y Sun, A Vargo. *FAT/ML 2018*.

/Amandarg/debias

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

J Yang, MW Mahoney, M Saunders, Y Sun. *NIPS 2016*.

**Evaluating the statistical significance of biclusters**

JD Lee, Y Sun, JE Taylor. *NIPS 2015*.

**Learning Mixtures of Linear Classifiers**

Y Sun, S Ioannidis, A Montanari. *ICML 2014*.

**Communication Efficient Model Fusion**

In *Federated Learning: A Comprehensive Overview of Methods and Applications*. H Ludwig, N Baracaldo (eds). Springer (2022).

**Personalization in Federated Learning**

In *Federated Learning: A Comprehensive Overview of Methods and Applications*. H Ludwig, N Baracaldo (eds). Springer (2022).

**On uniform consistency of spectral embeddings**

R Zhao, S Xue, Y Sun.

**How does overparametrization affect performance on minority groups?**

S Maity, S Roy, S Xue, M Yurochkin, Y Sun.

/smaityumich/overparameterization

**Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms**

L Niss, Y Sun, A Tewari.

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

X Liu, CJ Hsieh, JD Lee, Y Sun.

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

Y Ritov, Y Sun, R Zhao.

**Valid post-correction inference for censored regression problems**

Y Sun, JE Taylor.