Mikhail Yurochkin

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 97

Top Topics

Statistical Inference

Gaussian Processes

Top Venues

Publications

Kevin Gu, Eva Tuecke, Dmitriy Katz, Raya Horesh, David Alvarez-Melis, Mikhail Yurochkin
CharED: Character-wise Ensemble Decoding for Large Language Models. CoRR (2024)
Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
An Investigation of Representation and Allocation Harms in Contrastive Learning. ICLR (2024)
Jiacheng Zhu, Kristjan H. Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon
Asymmetry in Low-Rank Adapters of Foundation Models. CoRR (2024)
Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
Aligners: Decoupling LLMs and Alignment. Tiny Papers @ ICLR (2024)
Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
Aligners: Decoupling LLMs and Alignment. CoRR (2024)
Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin
tinyBenchmarks: evaluating LLMs with fewer examples. CoRR (2024)
Igor Melnyk, Youssef Mroueh, Brian Belgodere, Mattia Rigotti, Apoorva Nitsure, Mikhail Yurochkin, Kristjan H. Greenewald, Jirí Navrátil, Jerret Ross
Distributional Preference Alignment of LLMs via Optimal Transport. CoRR (2024)
Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin
Fusing Models with Complementary Expertise. ICLR (2024)
Felix Petersen, Aashwin Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin
Uncertainty Quantification via Stable Distribution Propagation. CoRR (2024)
Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin
Prompt Exploration with Prompt Regression. CoRR (2024)
Seamus Somerstep, Felipe Maia Polo, Moulinath Banerjee, Yaacov Ritov, Mikhail Yurochkin, Yuekai Sun
A statistical framework for weak-to-strong generalization. CoRR (2024)
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 (2024)
Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan H. Greenewald, Mikhail Yurochkin, Justin Solomon
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead. CoRR (2024)
Felix Petersen, Aashwin Ananda Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin
Uncertainty Quantification via Stable Distribution Propagation. ICLR (2024)
Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon
Sampling with Mollified Interaction Energy Descent. ICLR (2023)
Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon
Rewiring with Positional Encodings for Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Kristjan H. Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
k-Mixup Regularization for Deep Learning via Optimal Transport. Trans. Mach. Learn. Res. 2023 (2023)
Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin
Fusing Models with Complementary Expertise. CoRR (2023)
Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Understanding new tasks through the lens of training data via exponential tilting. ICLR (2023)
Zahra Ashktorab, Benjamin Hoover, Mayank Agarwal, Casey Dugan, Werner Geyer, Hao Bang Yang, Mikhail Yurochkin
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness. CoRR (2023)
Songkai Xue, Yuekai Sun, Mikhail Yurochkin
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. CoRR (2023)
Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li, Kristjan H. Greenewald, Mikhail Yurochkin, Justin Solomon
Learning Proximal Operators to Discover Multiple Optima. ICLR (2023)
Felipe Maia Polo, Mikhail Yurochkin, Moulinath Banerjee, Subha Maity, Yuekai Sun
Estimating Fréchet bounds for validating programmatic weak supervision. CoRR (2023)
Zahra Ashktorab, Benjamin Hoover, Mayank Agarwal, Casey Dugan, Werner Geyer, Hao Bang Yang, Mikhail Yurochkin
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness. CHI (2023)
Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
An Investigation of Representation and Allocation Harms in Contrastive Learning. CoRR (2023)
Tal Shnitzer, Anthony Ou, Mírian Silva, Kate Soule, Yuekai Sun, Justin Solomon, Neil Thompson, Mikhail Yurochkin
Large Language Model Routing with Benchmark Datasets. CoRR (2023)
Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
Simple Disentanglement of Style and Content in Visual Representations. ICML (2023)
Dustin Klebe, Tal Shnitzer, Mikhail Yurochkin, Leonid Karlinsky, Justin Solomon
GeRA: Label-Efficient Geometrically Regularized Alignment. CoRR (2023)
Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
Simple Disentanglement of Style and Content in Visual Representations. CoRR (2023)
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan H. Greenewald, Brian Belgodere, Mikhail Yurochkin, Jirí Navrátil, Igor Melnyk, Jerret Ross
Risk Assessment and Statistical Significance in the Age of Foundation Models. CoRR (2023)
Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon
Sampling with Mollified Interaction Energy Descent. CoRR (2022)
Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li, Kristjan H. Greenewald, Mikhail Yurochkin, Justin Solomon
Learning Proximal Operators to Discover Multiple Optima. CoRR (2022)
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun
Domain Adaptation meets Individual Fairness. And they get along. CoRR (2022)
Bum Chul Kwon, Uri Kartoun, Shaan Khurshid, Mikhail Yurochkin, Subha Maity, Deanna G. Brockman, Amit V. Khera, Patrick T. Ellinor, Steven A. Lubitz, Kenney Ng
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups. IEEE VIS (Short Papers) (2022)
Tal Shnitzer, Mikhail Yurochkin, Kristjan H. Greenewald, Justin Solomon
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets. CoRR (2022)
Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Understanding new tasks through the lens of training data via exponential tilting. CoRR (2022)
Yuchen Zeng, Kristjan H. Greenewald, Kangwook Lee, Justin Solomon, Mikhail Yurochkin
Outlier-Robust Group Inference via Gradient Space Clustering. CoRR (2022)
Yi Fang, Hongfu Liu, Zhiqiang Tao, Mikhail Yurochkin
Fairness of Machine Learning in Search Engines. CIKM (2022)
Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon
Rewiring with Positional Encodings for Graph Neural Networks. CoRR (2022)
Songkai Xue, Yuekai Sun, Mikhail Yurochkin
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. NeurIPS (2022)
Bum Chul Kwon, Uri Kartoun, Shaan Khurshid, Mikhail Yurochkin, Subha Maity, Deanna G. Brockman, Amit V. Khera, Patrick T. Ellinor, Steven A. Lubitz, Kenney Ng
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups. CoRR (2022)
Subha Maity, Saptarshi Roy, Songkai Xue, Mikhail Yurochkin, Yuekai Sun
How does overparametrization affect performance on minority groups? CoRR (2022)
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh, Mikhail Yurochkin
Your fairness may vary: Pretrained language model fairness in toxic text classification. ACL (Findings) (2022)
William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick
Measuring the robustness of Gaussian processes to kernel choice. AISTATS (2022)
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun
Domain Adaptation meets Individual Fairness. And they get along. NeurIPS (2022)
Tal Shnitzer, Mikhail Yurochkin, Kristjan H. Greenewald, Justin M. Solomon
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets. ICML (2022)
Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun
Statistical inference for individual fairness. CoRR (2021)
Debarghya Mukherjee, Aritra Guha, Justin M. Solomon, Yuekai Sun, Mikhail Yurochkin
Outlier-Robust Optimal Transport. ICML (2021)
Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung
On efficient multilevel Clustering via Wasserstein distances. J. Mach. Learn. Res. 22 (2021)
William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick
Measuring the sensitivity of Gaussian processes to kernel choice. CoRR (2021)
Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun
Statistical inference for individual fairness. ICLR (2021)
Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin
Post-processing for Individual Fairness. CoRR (2021)
Mikhail Yurochkin, Yuekai Sun
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. ICLR (2021)
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun
Individually Fair Rankings. ICLR (2021)
Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun
Individually Fair Gradient Boosting. ICLR (2021)
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun
Individually Fair Ranking. CoRR (2021)
Kristjan H. Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
k-Mixup Regularization for Deep Learning via Optimal Transport. CoRR (2021)
Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin
Post-processing for Individual Fairness. NeurIPS (2021)
Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun
Individually Fair Gradient Boosting. CoRR (2021)
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun
Does enforcing fairness mitigate biases caused by subpopulation shift? NeurIPS (2021)
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Mikhail Yurochkin, Moninder Singh
Your fairness may vary: Group fairness of pretrained language models in toxic text classification. CoRR (2021)
Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
On sensitivity of meta-learning to support data. CoRR (2021)
Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
On sensitivity of meta-learning to support data. NeurIPS (2021)
Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni, Djallel Bouneffouf
Online Semi-Supervised Learning with Bandit Feedback. CoRR (2020)
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Jeremy Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Ngoc Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Witherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay
IBM Federated Learning: an Enterprise Framework White Paper V0.1. CoRR (2020)
Songkai Xue, Mikhail Yurochkin, Yuekai Sun
Auditing ML Models for Individual Bias and Unfairness. AISTATS (2020)
Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin Solomon
Continuous Regularized Wasserstein Barycenters. CoRR (2020)
Songkai Xue, Mikhail Yurochkin, Yuekai Sun
Auditing ML Models for Individual Bias and Unfairness. CoRR (2020)
Mikhail Yurochkin, Yuekai Sun
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. CoRR (2020)
Mark Weber, Mikhail Yurochkin, Sherif Botros, Vanio Markov
Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination. CoRR (2020)
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni
Federated Learning with Matched Averaging. ICLR (2020)
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Two Simple Ways to Learn Individual Fairness Metrics from Data. ICML (2020)
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun
Two Simple Ways to Learn Individual Fairness Metrics from Data. CoRR (2020)
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun
There is no trade-off: enforcing fairness can improve accuracy. CoRR (2020)
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni
Federated Learning with Matched Averaging. CoRR (2020)
Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M. Solomon
Continuous Regularized Wasserstein Barycenters. NeurIPS (2020)
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon
Model Fusion with Kullback-Leibler Divergence. CoRR (2020)
Mikhail Yurochkin, Amanda Bower, Yuekai Sun
Training individually fair ML models with sensitive subspace robustness. ICLR (2020)
Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon
Model Fusion with Kullback-Leibler Divergence. ICML (2020)
Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon, Mikhail Yurochkin
Alleviating Label Switching with Optimal Transport. NeurIPS (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
Bayesian Nonparametric Federated Learning of Neural Networks. ICML (2019)
Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen
Dirichlet Simplex Nest and Geometric Inference. ICML (2019)
Mikhail Yurochkin, Amanda Bower, Yuekai Sun
Learning fair predictors with Sensitive Subspace Robustness. CoRR (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang
Statistical Model Aggregation via Parameter Matching. CoRR (2019)
Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen
Dirichlet Simplex Nest and Geometric Inference. CoRR (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang
Statistical Model Aggregation via Parameter Matching. NeurIPS (2019)
Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung
On Efficient Multilevel Clustering via Wasserstein Distances. CoRR (2019)
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon
Hierarchical Optimal Transport for Document Representation. CoRR (2019)
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
Bayesian Nonparametric Federated Learning of Neural Networks. CoRR (2019)
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon
Hierarchical Optimal Transport for Document Representation. NeurIPS (2019)
Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon, Mikhail Yurochkin
Alleviating Label Switching with Optimal Transport. CoRR (2019)
Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen
Scalable inference of topic evolution via models for latent geometric structures. NeurIPS (2019)
Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen
Streaming dynamic and distributed inference of latent geometric structures. CoRR (2018)
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung
Multilevel Clustering via Wasserstein Means. ICML (2017)
Mikhail Yurochkin, XuanLong Nguyen, Nikolaos Vasiloglou
Multi-way Interacting Regression via Factorization Machines. NIPS (2017)
Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen
Conic Scan-and-Cover algorithms for nonparametric topic modeling. NIPS (2017)
Mikhail Yurochkin, XuanLong Nguyen
Geometric Dirichlet Means Algorithm for topic inference. NIPS (2016)