default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMikhail Yurochkin
Person information
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c44]Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Aligners: Decoupling LLMs and Alignment. EMNLP (Findings) 2024: 13785-13802- [c43]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. ICLR 2024 - [c42]Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
An Investigation of Representation and Allocation Harms in Contrastive Learning. ICLR 2024 - [c41]Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Aligners: Decoupling LLMs and Alignment. Tiny Papers @ ICLR 2024 - [c40]Felix Petersen, Aashwin Ananda Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin:
Uncertainty Quantification via Stable Distribution Propagation. ICLR 2024 - [c39]Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan H. Greenewald, Brian Belgodere, Mikhail Yurochkin, Jirí Navrátil, Igor Melnyk, Jarret Ross:
Risk Aware Benchmarking of Large Language Models. ICML 2024 - [c38]Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin:
tinyBenchmarks: evaluating LLMs with fewer examples. ICML 2024 - [c37]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. ICML 2024 - [i56]Felix Petersen, Aashwin Ananda Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin:
Uncertainty Quantification via Stable Distribution Propagation. CoRR abs/2402.08324 (2024) - [i55]Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin:
tinyBenchmarks: evaluating LLMs with fewer examples. CoRR abs/2402.14992 (2024) - [i54]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 abs/2402.16842 (2024) - [i53]Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Aligners: Decoupling LLMs and Alignment. CoRR abs/2403.04224 (2024) - [i52]Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin:
Prompt Exploration with Prompt Regression. CoRR abs/2405.11083 (2024) - [i51]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) - [i50]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) - [i49]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 abs/2406.05882 (2024) - [i48]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 abs/2407.00066 (2024) - [i47]Kevin Gu, Eva Tuecke, Dmitriy Katz, Raya Horesh, David Alvarez-Melis, Mikhail Yurochkin:
CharED: Character-wise Ensemble Decoding for Large Language Models. CoRR abs/2407.11009 (2024) - [i46]Shachar Don-Yehiya, Ben Burtenshaw, Ramón Fernandez Astudillo, Cailean Osborne, Mimansa Jaiswal, Tzu-Sheng Kuo, Wenting Zhao, Idan Shenfeld, Andi Peng, Mikhail Yurochkin, Atoosa Kasirzadeh, Yangsibo Huang, Tatsunori Hashimoto, Yacine Jernite, Daniel Vila-Suero, Omri Abend, Jennifer Ding, Sara Hooker, Hannah Rose Kirk, Leshem Choshen:
The Future of Open Human Feedback. CoRR abs/2408.16961 (2024) - [i45]Nimrod Shabtay, Felipe Maia Polo, Sivan Doveh, Wei Lin, M. 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
- [j3]Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon:
Rewiring with Positional Encodings for Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [j2]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) - [c36]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: 565:1-565:20 - [c35]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 - [c34]Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon:
Sampling with Mollified Interaction Energy Descent. ICLR 2023 - [c33]Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Understanding new tasks through the lens of training data via exponential tilting. ICLR 2023 - [c32]Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Simple Disentanglement of Style and Content in Visual Representations. ICML 2023: 26063-26086 - [i44]Songkai Xue, Yuekai Sun, Mikhail Yurochkin:
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. CoRR abs/2301.06195 (2023) - [i43]Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin:
Simple Disentanglement of Style and Content in Visual Representations. CoRR abs/2302.09795 (2023) - [i42]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 abs/2303.00673 (2023) - [i41]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) - [i40]Dustin Klebe, Tal Shnitzer, Mikhail Yurochkin, Leonid Karlinsky, Justin Solomon:
GeRA: Label-Efficient Geometrically Regularized Alignment. CoRR abs/2310.00672 (2023) - [i39]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. CoRR abs/2310.01542 (2023) - [i38]Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
An Investigation of Representation and Allocation Harms in Contrastive Learning. CoRR abs/2310.01583 (2023) - [i37]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 abs/2310.07132 (2023) - [i36]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
- [c31]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: 2245-2262 - [c30]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: 3308-3331 - [c29]Yi Fang, Hongfu Liu, Zhiqiang Tao, Mikhail Yurochkin:
Fairness of Machine Learning in Search Engines. CIKM 2022: 5132-5135 - [c28]Tal Shnitzer, Mikhail Yurochkin, Kristjan H. Greenewald, Justin M. Solomon:
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets. ICML 2022: 20106-20124 - [c27]Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun:
Domain Adaptation meets Individual Fairness. And they get along. NeurIPS 2022 - [c26]Songkai Xue, Yuekai Sun, Mikhail Yurochkin:
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. NeurIPS 2022 - [c25]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: 50-54 - [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 - [i35]Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li, Kristjan H. Greenewald, Mikhail Yurochkin, Justin Solomon:
Learning Proximal Operators to Discover Multiple Optima. CoRR abs/2201.11945 (2022) - [i34]Rickard Brüel Gabrielsson, Mikhail Yurochkin, Justin Solomon:
Rewiring with Positional Encodings for Graph Neural Networks. CoRR abs/2201.12674 (2022) - [i33]Tal Shnitzer, Mikhail Yurochkin, Kristjan H. Greenewald, Justin Solomon:
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets. CoRR abs/2202.01671 (2022) - [i32]Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin, Yuekai Sun:
Domain Adaptation meets Individual Fairness. And they get along. CoRR abs/2205.00504 (2022) - [i31]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) - [i30]Subha Maity, Saptarshi Roy, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
How does overparametrization affect performance on minority groups? CoRR abs/2206.03515 (2022) - [i29]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 abs/2209.06378 (2022) - [i28]Yuchen Zeng, Kristjan H. Greenewald, Kangwook Lee, Justin Solomon, Mikhail Yurochkin:
Outlier-Robust Group Inference via Gradient Space Clustering. CoRR abs/2210.06759 (2022) - [i27]Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon:
Sampling with Mollified Interaction Energy Descent. CoRR abs/2210.13400 (2022) - 2021
- [j1]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: 145:1-145:43 (2021) - [c24]Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Rankings. ICLR 2021 - [c23]Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Statistical inference for individual fairness. ICLR 2021 - [c22]Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Gradient Boosting. ICLR 2021 - [c21]Mikhail Yurochkin, Yuekai Sun:
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. ICLR 2021 - [c20]Debarghya Mukherjee, Aritra Guha, Justin M. Solomon, Yuekai Sun, Mikhail Yurochkin:
Outlier-Robust Optimal Transport. ICML 2021: 7850-7860 - [c19]Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
On sensitivity of meta-learning to support data. NeurIPS 2021: 20447-20460 - [c18]Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun:
Does enforcing fairness mitigate biases caused by subpopulation shift? NeurIPS 2021: 25773-25784 - [c17]Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin:
Post-processing for Individual Fairness. NeurIPS 2021: 25944-25955 - [i26]Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Ranking. CoRR abs/2103.11023 (2021) - [i25]Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Statistical inference for individual fairness. CoRR abs/2103.16714 (2021) - [i24]Alexander Vargo, Fan Zhang, Mikhail Yurochkin, Yuekai Sun:
Individually Fair Gradient Boosting. CoRR abs/2103.16785 (2021) - [i23]Kristjan H. Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien:
k-Mixup Regularization for Deep Learning via Optimal Transport. CoRR abs/2106.02933 (2021) - [i22]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 abs/2106.06510 (2021) - [i21]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 abs/2108.01250 (2021) - [i20]Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin:
Post-processing for Individual Fairness. CoRR abs/2110.13796 (2021) - [i19]Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
On sensitivity of meta-learning to support data. CoRR abs/2110.13953 (2021) - 2020
- [c16]Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Auditing ML Models for Individual Bias and Unfairness. AISTATS 2020: 4552-4562 - [c15]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. ICLR 2020 - [c14]Mikhail Yurochkin, Amanda Bower, Yuekai Sun:
Training individually fair ML models with sensitive subspace robustness. ICLR 2020 - [c13]Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon:
Model Fusion with Kullback-Leibler Divergence. ICML 2020: 2038-2047 - [c12]Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Two Simple Ways to Learn Individual Fairness Metrics from Data. ICML 2020: 7097-7107 - [c11]Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M. Solomon:
Continuous Regularized Wasserstein Barycenters. NeurIPS 2020 - [i18]Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris S. Papailiopoulos, Yasaman Khazaeni:
Federated Learning with Matched Averaging. CoRR abs/2002.06440 (2020) - [i17]Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
Auditing ML Models for Individual Bias and Unfairness. CoRR abs/2003.05048 (2020) - [i16]Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun:
Two Simple Ways to Learn Individual Fairness Metrics from Data. CoRR abs/2006.11439 (2020) - [i15]Mikhail Yurochkin, Yuekai Sun:
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness. CoRR abs/2006.14168 (2020) - [i14]Sebastian Claici, Mikhail Yurochkin, Soumya Ghosh, Justin Solomon:
Model Fusion with Kullback-Leibler Divergence. CoRR abs/2007.06168 (2020) - [i13]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 abs/2007.10987 (2020) - [i12]Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin Solomon:
Continuous Regularized Wasserstein Barycenters. CoRR abs/2008.12534 (2020) - [i11]Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni, Djallel Bouneffouf:
Online Semi-Supervised Learning with Bandit Feedback. CoRR abs/2010.12574 (2020) - [i10]Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun:
There is no trade-off: enforcing fairness can improve accuracy. CoRR abs/2011.03173 (2020) - [i9]Mark Weber, Mikhail Yurochkin, Sherif Botros, Vanio Markov:
Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination. CoRR abs/2012.01193 (2020)
2010 – 2019
- 2019
- [c10]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni:
Bayesian Nonparametric Federated Learning of Neural Networks. ICML 2019: 7252-7261 - [c9]Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen:
Dirichlet Simplex Nest and Geometric Inference. ICML 2019: 7262-7271 - [c8]Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon:
Hierarchical Optimal Transport for Document Representation. NeurIPS 2019: 1599-1609 - [c7]Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen:
Scalable inference of topic evolution via models for latent geometric structures. NeurIPS 2019: 5949-5959 - [c6]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang:
Statistical Model Aggregation via Parameter Matching. NeurIPS 2019: 10954-10964 - [c5]Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon, Mikhail Yurochkin:
Alleviating Label Switching with Optimal Transport. NeurIPS 2019: 13612-13622 - [i8]Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen:
Dirichlet Simplex Nest and Geometric Inference. CoRR abs/1905.11009 (2019) - [i7]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni:
Bayesian Nonparametric Federated Learning of Neural Networks. CoRR abs/1905.12022 (2019) - [i6]Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon:
Hierarchical Optimal Transport for Document Representation. CoRR abs/1906.10827 (2019) - [i5]Mikhail Yurochkin, Amanda Bower, Yuekai Sun:
Learning fair predictors with Sensitive Subspace Robustness. CoRR abs/1907.00020 (2019) - [i4]Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, Dinh Q. Phung:
On Efficient Multilevel Clustering via Wasserstein Distances. CoRR abs/1909.08787 (2019) - [i3]Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang:
Statistical Model Aggregation via Parameter Matching. CoRR abs/1911.00218 (2019) - [i2]Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin Solomon, Mikhail Yurochkin:
Alleviating Label Switching with Optimal Transport. CoRR abs/1911.02053 (2019) - 2018
- [i1]Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen:
Streaming dynamic and distributed inference of latent geometric structures. CoRR abs/1809.08738 (2018) - 2017
- [c4]Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Q. Phung:
Multilevel Clustering via Wasserstein Means. ICML 2017: 1501-1509 - [c3]Mikhail Yurochkin, XuanLong Nguyen, Nikolaos Vasiloglou:
Multi-way Interacting Regression via Factorization Machines. NIPS 2017: 2598-2606 - [c2]Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen:
Conic Scan-and-Cover algorithms for nonparametric topic modeling. NIPS 2017: 3878-3887 - 2016
- [c1]Mikhail Yurochkin, XuanLong Nguyen:
Geometric Dirichlet Means Algorithm for topic inference. NIPS 2016: 2505-2513
Coauthor Index
Justin Solomon 0001
aka: Justin M. Solomon
aka: Justin M. Solomon
[c44] [c43] [c42] [c41] [c38] [i55] [i53] [i52] [i51] [i50] [i45] [c33] [c32] [i44] [i43] [i41] [i39] [i38] [i36] [c27] [c26] [p2] [p1] [i32] [i31] [i30] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [i26] [i25] [i24] [i20] [i19] [c16] [c15] [c14] [c12] [i18] [i17] [i16] [i15] [i10] [c9] [i8] [i5]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-26 20:45 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
