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2020 – today
- 2024
[c33]Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Aligners: Decoupling LLMs and Alignment. EMNLP (Findings) 2024: 13785-13802- [c32]Seamus Somerstep
, Yaacov Ritov, Yuekai Sun:
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness. FAccT 2024: 616-630 - [c31]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. ICLR 2024 - [c30]Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
An Investigation of Representation and Allocation Harms in Contrastive Learning. ICLR 2024 - [c29]Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Aligners: Decoupling LLMs and Alignment. Tiny Papers @ ICLR 2024 - [c28]Seamus Somerstep, Yuekai Sun, Yaacov Ritov:
Learning in reverse causal strategic environments with ramifications on two sided markets. ICLR 2024 - [c27]Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin:
tinyBenchmarks: evaluating LLMs with fewer examples. ICML 2024 - [i42]Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin:
tinyBenchmarks: evaluating LLMs with fewer examples. CoRR abs/2402.14992 (2024) - [i41]Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Aligners: Decoupling LLMs and Alignment. CoRR abs/2403.04224 (2024) - [i40]Seamus Somerstep, Yuekai Sun, Yaacov Ritov:
Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets. CoRR abs/2404.13240 (2024) - [i39]Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin:
Prompt Exploration with Prompt Regression. CoRR abs/2405.11083 (2024) - [i38]Daniele Bracale, Subha Maity, Moulinath Banerjee, Yuekai Sun:
Learning the Distribution Map in Reverse Causal Performative Prediction. CoRR abs/2405.15172 (2024) - [i37]Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee, Yaacov Ritov, Mikhail Yurochkin, Yuekai Sun:
A statistical framework for weak-to-strong generalization. CoRR abs/2405.16236 (2024) - [i36]Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin:
Efficient multi-prompt evaluation of LLMs. CoRR abs/2405.17202 (2024) - [i35]Seamus Somerstep, Yaacov Ritov, Yuekai Sun:
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness. CoRR abs/2405.20447 (2024) - [i34]Nimrod Shabtay, Felipe Maia Polo, Sivan Doveh, Wei Lin, Muhammad Jehanzeb Mirza, Leshem Choshen, Mikhail Yurochkin, Yuekai Sun, Assaf Arbelle, Leonid Karlinsky, Raja Giryes:
LiveXiv - A Multi-Modal Live Benchmark Based on Arxiv Papers Content. CoRR abs/2410.10783 (2024) - 2023
- [c26]Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun:
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. ICLR 2023 - [c25]Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Understanding new tasks through the lens of training data via exponential tilting. ICLR 2023 - [c24]Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen:
ISAAC Newton: Input-based Approximate Curvature for Newton's Method. ICLR 2023 - [c23]Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Simple Disentanglement of Style and Content in Visual Representations. ICML 2023: 26063-26086 - [c22]Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee:
Conditional independence testing under misspecified inductive biases. NeurIPS 2023 - [i33]Songkai Xue, Yuekai Sun, Mikhail Yurochkin:
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. CoRR abs/2301.06195 (2023) - [i32]Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Simple Disentanglement of Style and Content in Visual Representations. CoRR abs/2302.09795 (2023) - [i31]Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen:
ISAAC Newton: Input-based Approximate Curvature for Newton's Method. CoRR abs/2305.00604 (2023) - [i30]Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee:
Conditional independence testing under model misspecification. CoRR abs/2307.02520 (2023) - [i29]Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, Mikhail Yurochkin:
Large Language Model Routing with Benchmark Datasets. CoRR abs/2309.15789 (2023) - [i28]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. CoRR abs/2310.01542 (2023) - [i27]Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
An Investigation of Representation and Allocation Harms in Contrastive Learning. CoRR abs/2310.01583 (2023) - [i26]Felipe Maia Polo, Mikhail Yurochkin, Moulinath Banerjee, Subha Maity, Yuekai Sun:
Estimating Fréchet bounds for validating programmatic weak supervision. CoRR abs/2312.04601 (2023) - 2022
- [j7]Subha Maity, Yuekai Sun, Moulinath Banerjee:
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions. J. Mach. Learn. Res. 23: 198:1-198:50 (2022) - [j6]Subha Maity, Yuekai Sun, Moulinath Banerjee:
Minimax optimal approaches to the label shift problem in non-parametric settings. J. Mach. Learn. Res. 23: 346:1-346:45 (2022) - [c21]Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun:
Domain Adaptation meets Individual Fairness. And they get along. NeurIPS 2022 - [c20]Songkai Xue, Yuekai Sun, Mikhail Yurochkin:
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. NeurIPS 2022 - [p2]Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
Personalization in Federated Learning. Federated Learning 2022: 71-98 - [p1]Mikhail Yurochkin, Yuekai Sun:
Communication-Efficient Model Fusion. Federated Learning 2022: 145-176 - [i25]Laura Niss, Yuekai Sun, Ambuj Tewari:
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms. CoRR abs/2204.06664 (2022) - [i24]Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun:
Domain Adaptation meets Individual Fairness. And they get along. CoRR abs/2205.00504 (2022) - [i23]Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun:
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. CoRR abs/2205.13575 (2022) - [i22]Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Understanding new tasks through the lens of training data via exponential tilting. CoRR abs/2205.13577 (2022) - [i21]Subha Maity, Saptarshi Roy, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
How does overparametrization affect performance on minority groups? CoRR abs/2206.03515 (2022) - 2021
- [c19]Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Rankings. ICLR 2021 - [c18]Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Statistical inference for individual fairness. ICLR 2021 - [c17]Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Gradient Boosting. ICLR 2021 - [c16]Mikhail Yurochkin, Yuekai Sun:
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. ICLR 2021 - [c15]Debarghya Mukherjee, Aritra Guha, Justin M. Solomon, Yuekai Sun, Mikhail Yurochkin:
Outlier-Robust Optimal Transport. ICML 2021: 7850-7860 - [c14]Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
On sensitivity of meta-learning to support data. NeurIPS 2021: 20447-20460 - [c13]Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun:
Does enforcing fairness mitigate biases caused by subpopulation shift? NeurIPS 2021: 25773-25784 - [c12]Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin:
Post-processing for Individual Fairness. NeurIPS 2021: 25944-25955 - [i20]Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Ranking. CoRR abs/2103.11023 (2021) - [i19]Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Statistical inference for individual fairness. CoRR abs/2103.16714 (2021) - [i18]Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Gradient Boosting. CoRR abs/2103.16785 (2021) - [i17]Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin:
Post-processing for Individual Fairness. CoRR abs/2110.13796 (2021) - [i16]Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
On sensitivity of meta-learning to support data. CoRR abs/2110.13953 (2021) - 2020
- [j5]Anil Damle, Yuekai Sun:
Uniform Bounds for Invariant Subspace Perturbations. SIAM J. Matrix Anal. Appl. 41(3): 1208-1236 (2020) - [c11]Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Auditing ML Models for Individual Bias and Unfairness. AISTATS 2020: 4552-4562 - [c10]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. ICLR 2020 - [c9]Mikhail Yurochkin, Amanda Bower, Yuekai Sun:
Training individually fair ML models with sensitive subspace robustness. ICLR 2020 - [c8]Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Two Simple Ways to Learn Individual Fairness Metrics from Data. ICML 2020: 7097-7107 - [i15]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. CoRR abs/2002.06440 (2020) - [i14]Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Auditing ML Models for Individual Bias and Unfairness. CoRR abs/2003.05048 (2020) - [i13]Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Two Simple Ways to Learn Individual Fairness Metrics from Data. CoRR abs/2006.11439 (2020) - [i12]Mikhail Yurochkin, Yuekai Sun:
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. CoRR abs/2006.14168 (2020) - [i11]Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun:
There is no trade-off: enforcing fairness can improve accuracy. CoRR abs/2011.03173 (2020)
2010 – 2019
- 2019
- [c7]Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou:
Precision Matrix Estimation with Noisy and Missing Data. AISTATS 2019: 2810-2819 - [c6]Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen:
Dirichlet Simplex Nest and Geometric Inference. ICML 2019: 7262-7271 - [i10]Roger Fan, Byoungwook Jang, Yuekai Sun, Shuheng Zhou:
Precision Matrix Estimation with Noisy and Missing Data. CoRR abs/1904.03548 (2019) - [i9]Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen:
Dirichlet Simplex Nest and Geometric Inference. CoRR abs/1905.11009 (2019) - [i8]Mikhail Yurochkin, Amanda Bower, Yuekai Sun:
Learning fair predictors with Sensitive Subspace Robustness. CoRR abs/1907.00020 (2019) - 2018
- [i7]Amanda Bower, Laura Niss, Yuekai Sun, Alexander Vargo:
Debiasing representations by removing unwanted variation due to protected attributes. CoRR abs/1807.00461 (2018) - 2017
- [j4]Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor:
Communication-efficient Sparse Regression. J. Mach. Learn. Res. 18: 5:1-5:30 (2017) - [j3]Anil Damle, Yuekai Sun:
A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization. Technometrics 59(3): 361-370 (2017) - [i6]Yaacov Ritov, Yuekai Sun, Ruofei Zhao:
On conditional parity as a notion of non-discrimination in machine learning. CoRR abs/1706.08519 (2017) - [i5]Xuanqing Liu, Cho-Jui Hsieh, Jason D. Lee, Yuekai Sun:
An inexact subsampled proximal Newton-type method for large-scale machine learning. CoRR abs/1708.08552 (2017) - 2016
- [c5]Jiyan Yang, Michael W. Mahoney, Michael A. Saunders, Yuekai Sun:
Feature-distributed sparse regression: a screen-and-clean approach. NIPS 2016: 2712-2720 - 2015
- [c4]Jason D. Lee, Yuekai Sun, Jonathan E. Taylor:
Evaluating the statistical significance of biclusters. NIPS 2015: 1324-1332 - [i4]Jason D. Lee, Yuekai Sun, Qiang Liu, Jonathan E. Taylor:
Communication-efficient sparse regression: a one-shot approach. CoRR abs/1503.04337 (2015) - 2014
- [j2]Jason D. Lee, Yuekai Sun, Michael A. Saunders:
Proximal Newton-Type Methods for Minimizing Composite Functions. SIAM J. Optim. 24(3): 1420-1443 (2014) - [c3]Yuekai Sun, Stratis Ioannidis, Andrea Montanari:
Learning Mixtures of Linear Classifiers. ICML 2014: 721-729 - 2013
- [j1]Yuekai Sun, Ronan M. T. Fleming, Ines Thiele, Michael A. Saunders:
Robust flux balance analysis of multiscale biochemical reaction networks. BMC Bioinform. 14: 240 (2013) - [c2]Jason D. Lee, Yuekai Sun, Jonathan E. Taylor:
On model selection consistency of penalized M-estimators: a geometric theory. NIPS 2013: 342-350 - [i3]Jason D. Lee, Yuekai Sun, Jonathan E. Taylor:
On model selection consistency of regularized M-estimators. CoRR abs/1305.7477 (2013) - [i2]Yuekai Sun, Stratis Ioannidis, Andrea Montanari:
Learning Mixtures of Linear Classifiers. CoRR abs/1311.2547 (2013) - 2012
- [c1]Jason D. Lee, Yuekai Sun, Michael A. Saunders:
Proximal Newton-type methods for convex optimization. NIPS 2012: 836-844 - [i1]Jason D. Lee, Yuekai Sun, Michael A. Saunders:
Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form. CoRR abs/1206.1623 (2012)
Coauthor Index
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last updated on 2024-12-26 01:53 CET by the dblp team
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