Conditional Models for Non-smooth Ranking Loss Functions. - Avinava Dubey; Jinesh Machchhar; Chiranjib Bhattacharyya; Soumen Chakrabarti (ICDM, 2009)

A Cluster-Level Semi-supervision Model for Interactive Clustering. - Avinava Dubey; Indrajit Bhattacharya; Shantanu Godbole (ECML/PKDD (1), 2010)

Learning Dirichlet Processes from Partially Observed Groups. - Avinava Dubey; Indrajit Bhattacharya; Mrinal Kanti Das; Tanveer A. Faruquie; Chiranjib Bhattacharyya (ICDM, 2011)

Diversity in ranking via resistive graph centers. - Avinava Dubey; Soumen Chakrabarti; Chiranjib Bhattacharyya (KDD, 2011)

AUSUM: approach for unsupervised bug report summarization. - Senthil Mani; Rose Catherine; Vibha Singhal Sinha; Avinava Dubey (SIGSOFT FSE, 2012)

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models. - Sinead Williamson; Avinava Dubey; Eric P. Xing (ICML (1), 2013)

A Nonparametric Mixture Model for Topic Modeling over Time. - Avinava Dubey; Ahmed Hefny; Sinead Williamson; Eric P. Xing (SDM, 2013)

Dependent nonparametric trees for dynamic hierarchical clustering. - Kumar Avinava Dubey; Qirong Ho; Sinead A. Williamson; Eric P. Xing (NIPS, 2014)

Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models. - Kumar Avinava Dubey; Sinead Williamson; Eric P. Xing (UAI, 2014)

Spatial compactness meets topical consistency: jointly modeling links and content for community detection. - Mrinmaya Sachan; Avinava Dubey; Shashank Srivastava; Eric P. Xing; Eduard H. Hovy (WSDM, 2014)

Learning Answer-Entailing Structures for Machine Comprehension. - Mrinmaya Sachan; Kumar Avinava Dubey; Eric P. Xing; Matthew Richardson (ACL (1), 2015)

Large-scale Distributed Dependent Nonparametric Trees. - Zhiting Hu; Qirong Ho; Avinava Dubey; Eric P. Xing (ICML, 2015)

Large-scale randomized-coordinate descent methods with non-separable linear constraints. - Sashank J. Reddi; Ahmed Hefny; Carlton Downey; Avinava Dubey; Suvrit Sra (UAI, 2015)

Science Question Answering using Instructional Materials. - Mrinmaya Sachan; Kumar Avinava Dubey; Eric P. Xing (ACL (2), 2016)

Bayesian Nonparametric Kernel-Learning. - Junier B. Oliva; Avinava Dubey; Andrew Gordon Wilson; Barnabás Póczos; Jeff G. Schneider; Eric P. Xing (AISTATS, 2016)

Estimating Accuracy from Unlabeled Data: A Bayesian Approach. - Emmanouil Antonios Platanios; Avinava Dubey; Tom M. Mitchell (ICML, 2016)

Variance Reduction in Stochastic Gradient Langevin Dynamics. - Kumar Avinava Dubey; Sashank J. Reddi; Sinead A. Williamson; Barnabás Póczos; Alexander J. Smola; Eric P. Xing (NIPS, 2016)

From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems. - Mrinmaya Sachan; Avinava Dubey; Eric P. Xing (EMNLP, 2017)

Recurrent Estimation of Distributions. - Junier B. Oliva; Kumar Avinava Dubey; Barnabás Póczos; Eric P. Xing; Jeff G. Schneider (CoRR, 2017)

Transformation Autoregressive Networks. - Junier B. Oliva; Avinava Dubey; Manzil Zaheer; Barnabás Póczos; Ruslan Salakhutdinov; Eric P. Xing; Jeff Schneider 001 (ICML, 2018)

Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems. - Mrinmaya Sachan; Kumar Avinava Dubey; Tom M. Mitchell; Dan Roth 001; Eric P. Xing (NeurIPS, 2018)

The Intriguing Properties of Model Explanations. - Maruan Al-Shedivat; Avinava Dubey; Eric P. Xing (CoRR, 2018)

Personalized Survival Prediction with Contextual Explanation Networks. - Maruan Al-Shedivat; Avinava Dubey; Eric P. Xing (CoRR, 2018)

Discourse in Multimedia: A Case Study in Information Extraction. - Mrinmaya Sachan; Kumar Avinava Dubey; Eduard H. Hovy; Tom M. Mitchell; Dan Roth 001; Eric P. Xing (CoRR, 2018)

Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks. - Mrinmaya Sachan; Avinava Dubey; Eduard H. Hovy; Tom M. Mitchell; Dan Roth 001; Eric P. Xing (Comput. Linguistics, 2019)

Distributed, partially collapsed MCMC for Bayesian Nonparametrics. - Kumar Avinava Dubey; Michael Minyi Zhang; Eric P. Xing; Sinead Williamson (AISTATS, 2020)

Big Bird: Transformers for Longer Sequences. - Manzil Zaheer; Guru Guruganesh; Kumar Avinava Dubey; Joshua Ainslie; Chris Alberti; Santiago Ontañón; Philip Pham; Anirudh Ravula; Qifan Wang 001; Li Yang; Amr Ahmed 001 (NeurIPS, 2020)

Scalable Bottom-Up Hierarchical Clustering. - Nicholas Monath; Avinava Dubey; Guru Guruganesh; Manzil Zaheer; Amr Ahmed 001; Andrew McCallum; Gökhan Mergen; Marc Najork; Mert Terzihan; Bryon Tjanaka; Yuan Wang; Yuchen Wu (CoRR, 2020)

Contextual Explanation Networks. - Maruan Al-Shedivat; Avinava Dubey; Eric P. Xing (J. Mach. Learn. Res., 2020)

DAG-Structured Clustering by Nearest Neighbors. - Nicholas Monath; Manzil Zaheer; Kumar Avinava Dubey; Amr Ahmed 001; Andrew McCallum (AISTATS, 2021)

Scalable Hierarchical Agglomerative Clustering. - Nicholas Monath; Kumar Avinava Dubey; Guru Guruganesh; Manzil Zaheer; Amr Ahmed 001; Andrew McCallum; Gökhan Mergen; Marc Najork; Mert Terzihan; Bryon Tjanaka; Yuan Wang; Yuchen Wu (KDD, 2021)

Exact and approximate hierarchical clustering using A. - Craig S. Greenberg; Sebastian Macaluso; Nicholas Monath; Avinava Dubey; Patrick Flaherty; Manzil Zaheer; Amr Ahmed 001; Kyle Cranmer; Andrew McCallum (UAI, 2021)

Chefs' Random Tables: Non-Trigonometric Random Features. - Valerii Likhosherstov; Krzysztof Marcin Choromanski; Kumar Avinava Dubey; Frederick Liu; Tamás Sarlós; Adrian Weller (NeurIPS, 2022)

A Fourier Approach to Mixture Learning. - Mingda Qiao; Guru Guruganesh; Ankit Singh Rawat; Kumar Avinava Dubey; Manzil Zaheer (NeurIPS, 2022)

Automated Deep Aberration Detection from Chromosome Karyotype Images. - Zahra Shamsi; Drew Bryant; Jacob Wilson; Xiaoyu Qu; Avinava Dubey; Konik Kothari; Mostafa Dehghani 001; Mariya Chavarha; Valerii Likhosherstov; Brian Williams; Michael Frumkin; Fred Appelbaum; et al. (CoRR, 2022)

Learning the Transformer Kernel. - Sankalan Pal Chowdhury; Adamos Solomou; Kumar Avinava Dubey; Mrinmaya Sachan (Trans. Mach. Learn. Res., 2022)

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. - Brianna Zitkovich; Tianhe Yu; Sichun Xu; Peng Xu 010; Ted Xiao; Fei Xia 002; Jialin Wu; Paul Wohlhart; Stefan Welker; Ayzaan Wahid; Quan Vuong; Vincent Vanhoucke; et al. (CoRL, 2023)

Unsupervised Opinion Summarization Using Approximate Geodesics. - Somnath Basu Roy Chowdhury; Nicholas Monath; Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (EMNLP (Findings), 2023)

Efficient Graph Field Integrators Meet Point Clouds. - 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; et al. (ICML, 2023)

Robust Concept Erasure via Kernelized Rate-Distortion Maximization. - Somnath Basu Roy Chowdhury; Nicholas Monath; Kumar Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (NeurIPS, 2023)

Mnemosyne: Learning to Train Transformers with Transformers. - Deepali Jain; Krzysztof Marcin Choromanski; Kumar Avinava Dubey; Sumeet Singh; Vikas Sindhwani; Tingnan Zhang; Jie Tan 001 (NeurIPS, 2023)

Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel. - Valerii Likhosherstov; Krzysztof Marcin Choromanski; Kumar Avinava Dubey; Frederick Liu; Tamás Sarlós; Adrian Weller (NeurIPS, 2023)

FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features. - Valerii Likhosherstov; Krzysztof Choromanski; Avinava Dubey; Frederick Liu; Tamás Sarlós; Adrian Weller (CoRR, 2023)

Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers. - Krzysztof Choromanski; Shanda Li; Valerii Likhosherstov; Kumar Avinava Dubey; Shengjie Luo; Di He 001; Yiming Yang; Tamás Sarlós; Thomas Weingarten; Adrian Weller (AISTATS, 2024)

Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (EMNLP (Findings), 2024)

Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. - Somnath Basu Roy Chowdhury; Nicholas Monath; Ahmad Beirami; Rahul Kidambi; Kumar Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (ICLR, 2024)

Scalable Neural Network Kernels. - Arijit Sehanobish; Krzysztof Marcin Choromanski; Yunfan Zhao; Kumar Avinava Dubey; Valerii Likhosherstov (ICLR, 2024)

SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention. - Isabel Leal; Krzysztof Choromanski; Deepali Jain; Avinava Dubey; Jake Varley; Michael S. Ryoo; Yao Lu 006; Frederick Liu; Vikas Sindhwani; Quan Vuong; Tamás Sarlós; Ken Oslund; et al. (ICRA, 2024)

Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity. - Jake Varley; Sumeet Singh; Deepali Jain; Krzysztof Choromanski; Andy Zeng 001; Somnath Basu Roy Chowdhury; Avinava Dubey; Vikas Sindhwani (IROS, 2024)

Auctions with LLM Summaries. - Avinava Dubey; Zhe Feng 004; Rahul Kidambi; Aranyak Mehta; Di Wang 005 (KDD, 2024)

Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers. - Krzysztof Marcin Choromanski; Arijit Sehanobish; Somnath Basu Roy Chowdhury; Han Lin; Kumar Avinava Dubey; Tamás Sarlós; Snigdha Chaturvedi (NeurIPS, 2024)

Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning. - Arijit Sehanobish; Kumar Avinava Dubey; Krzysztof Marcin Choromanski; Somnath Basu Roy Chowdhury; Deepali Jain; Vikas Sindhwani; Snigdha Chaturvedi (NeurIPS, 2024)

Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning. - Arijit Sehanobish; Avinava Dubey; Krzysztof Choromanski; Somnath Basu Roy Chowdhury; Deepali Jain; Vikas Sindhwani; Snigdha Chaturvedi (CoRR, 2024)

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (CoRR, 2024)

Magnituder Layers for Implicit Neural Representations in 3D. - Sang Min Kim; Byeongchan Kim; Arijit Sehanobish; Krzysztof Choromanski; Dongseok Shim; Avinava Dubey; Min-hwan Oh (CoRR, 2024)

Incremental Extractive Opinion Summarization Using Cover Trees. - Somnath Basu Roy Chowdhury; Nicholas Monath; Kumar Avinava Dubey; Manzil Zaheer; Andrew McCallum; Amr Ahmed 001; Snigdha Chaturvedi (Trans. Mach. Learn. Res., 2024)

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs. - Krzysztof Marcin Choromanski; Isaac Reid; Arijit Sehanobish; Kumar Avinava Dubey (AISTATS, 2025)

Fundamental Limits of Perfect Concept Erasure. - Somnath Basu Roy Chowdhury; Kumar Avinava Dubey; Ahmad Beirami; Rahul Kidambi; Nicholas Monath; Amr Ahmed 001; Snigdha Chaturvedi (AISTATS, 2025)

Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning. - Somnath Basu Roy Chowdhury; Krzysztof Marcin Choromanski; Arijit Sehanobish; Kumar Avinava Dubey; Snigdha Chaturvedi (ICLR, 2025)

Linear Transformer Topological Masking with Graph Random Features. - Isaac Reid; Kumar Avinava Dubey; Deepali Jain; William F. Whitney; Amr Ahmed 001; Joshua Ainslie; Alex Bewley; Mithun George Jacob; Aranyak Mehta; David Rendleman; Connor Schenck; Richard E. Turner; et al. (ICLR, 2025)

Learning the RoPEs: Better 2D and 3D Position Encodings with STRING. - Connor Schenck; Isaac Reid; Mithun George Jacob; Alex Bewley; Joshua Ainslie; David Rendleman; Deepali Jain; Mohit Sharma 001; Avinava Dubey; Ayzaan Wahid; Sumeet Singh; René Wagner; et al. (CoRR, 2025)