Kumar Avinava Dubey

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 24

Top Topics

Decision Forest

Instructional Materials

Hierarchical Agglomerative

Markov Chain Monte Carlo

Top Venues

Publications

Arijit Sehanobish, Krzysztof Marcin Choromanski, Yunfan Zhao, Kumar Avinava Dubey, Valerii Likhosherstov
Scalable Neural Network Kernels. ICLR (2024)
Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. AISTATS (2024)
Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
Auctions with LLM Summaries. CoRR (2024)
Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. ICLR (2024)
Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi
Incremental Extractive Opinion Summarization Using Cover Trees. Trans. Mach. Learn. Res. 2024 (2024)
Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller
Efficient Graph Field Integrators Meet Point Clouds. ICML (2023)
Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi
Robust Concept Erasure via Kernelized Rate-Distortion Maximization. NeurIPS (2023)
Deepali Jain, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan
Mnemosyne: Learning to Train Transformers with Transformers. NeurIPS (2023)
Krzysztof Marcin Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. CoRR (2023)
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, Quan Vuong, Vincent Vanhoucke, Huong T. Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R. Sanketi, Grecia Salazar, Michael S. Ryoo, Krista Reymann, Kanishka Rao, Karl Pertsch, Igor Mordatch, Henryk Michalewski, Yao Lu, Sergey Levine, Lisa Lee, Tsang-Wei Edward Lee, Isabel Leal, Yuheng Kuang, Dmitry Kalashnikov, Ryan Julian, Nikhil J. Joshi, Alex Irpan, Brian Ichter, Jasmine Hsu, Alexander Herzog, Karol Hausman, Keerthana Gopalakrishnan, Chuyuan Fu, Pete Florence, Chelsea Finn, Kumar Avinava Dubey, Danny Driess, Tianli Ding, Krzysztof Marcin Choromanski, Xi Chen, Yevgen Chebotar, Justice Carbajal, Noah Brown, Anthony Brohan, Montserrat Gonzalez Arenas, Kehang Han
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. CoRL (2023)
Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel. NeurIPS (2023)
Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat, Kumar Avinava Dubey, Manzil Zaheer
A Fourier Approach to Mixture Learning. NeurIPS (2022)
Valerii Likhosherstov, Krzysztof Marcin Choromanski, Kumar Avinava Dubey, Frederick Liu, Tamás Sarlós, Adrian Weller
Chefs' Random Tables: Non-Trigonometric Random Features. NeurIPS (2022)
Sankalan Pal Chowdhury, Adamos Solomou, Kumar Avinava Dubey, Mrinmaya Sachan
Learning the Transformer Kernel. Trans. Mach. Learn. Res. 2022 (2022)
Nicholas Monath, Kumar Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gökhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, Yuan Wang, Yuchen Wu
Scalable Hierarchical Agglomerative Clustering. KDD (2021)
Nicholas Monath, Manzil Zaheer, Kumar Avinava Dubey, Amr Ahmed, Andrew McCallum
DAG-Structured Clustering by Nearest Neighbors. AISTATS (2021)
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed
Big Bird: Transformers for Longer Sequences. NeurIPS (2020)
Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. AISTATS (2020)
Mrinmaya Sachan, Kumar Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing
Discourse in Multimedia: A Case Study in Information Extraction. CoRR (2018)
Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems. NeurIPS (2018)
Junier B. Oliva, Kumar Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider
Recurrent Estimation of Distributions. CoRR (2017)
Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing
Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS (2016)
Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing
Science Question Answering using Instructional Materials. ACL (2) (2016)
Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing, Matthew Richardson
Learning Answer-Entailing Structures for Machine Comprehension. ACL (1) (2015)
Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing
Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models. UAI (2014)
Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing
Dependent nonparametric trees for dynamic hierarchical clustering. NIPS (2014)