Kumar Krishna Agrawal

28 papers A* 6A 1Journal 15Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Atlas: Multi-Scale Attention Improves Long Context Image Modeling. CoRR Kumar Krishna Agrawal, Long Lian, Longchao Liu, Natalia Harguindeguy, Boyi Li, Alexander Bick, Maggie Chung, Trevor Darrell, Adam Yala
2025	J	`jnl`	Pillar-0: A New Frontier for Radiology Foundation Models. CoRR Kumar Krishna Agrawal, Longchao Liu, Long Lian, Michael Nercessian, Natalia Harguindeguy, Yufu Wu, Peter Mikhael, Gigin Lin, Lecia V. Sequist, Florian Fintelmann, Trevor Darrell, Yutong Bai, Maggie Chung, Adam Yala
2025	J	`jnl`	RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics. CoRR Gur-Eyal Sela, Kumar Krishna Agrawal, Bharathan Balaji, Joseph Gonzalez, Ion Stoica
2025	J	`jnl`	Tracing the Representation Geometry of Language Models from Pretraining to Post-training. CoRR Melody Zixuan Li, Kumar Krishna Agrawal, Arna Ghosh, Komal Kumar Teru, Adam Santoro, Guillaume Lajoie, Blake A. Richards
2025	J	`jnl`	vAttention: Verified Sparse Attention. CoRR Aditya Desai, Kumar Krishna Agrawal, Shuo Yang, Alejandro Cuadron, Luis Gaspar Schroeder, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica
2024	A*	`conf`	Attribute Diversity Determines the Systematicity Gap in VQA. EMNLP Ian Berlot-Attwell, Kumar Krishna Agrawal, Annabelle Michael Carrell, Yash Sharma, Naomi Saphra
2024	A*	`conf`	Harnessing small projectors and multiple views for efficient vision pretraining. NeurIPS Arna Ghosh, Kumar Krishna Agrawal, Shagun Sodhani, Adam Oberman, Blake A. Richards
2023	J	`jnl`	Addressing Sample Inefficiency in Multi-View Representation Learning. CoRR Kumar Krishna Agrawal, Arna Ghosh, Adam Oberman, Blake A. Richards
2023	J	`jnl`	Attribute Diversity Determines the Systematicity Gap in VQA. CoRR Ian Berlot-Attwell, A. Michael Carrell, Kumar Krishna Agrawal, Yash Sharma, Naomi Saphra
2022	A*	`conf`	$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay. NeurIPS Kumar Krishna Agrawal, Arnab Kumar Mondal, Arna Ghosh, Blake A. Richards
2022	—	`conf`	Context-Aware Streaming Perception in Dynamic Environments. ECCV (38) Gur-Eyal Sela, Ionel Gog, Justin Wong, Kumar Krishna Agrawal, Xiangxi Mo, Sukrit Kalra, Peter Schafhalter, Eric Leong, Xin Wang, Bharathan Balaji, Joseph Gonzalez, Ion Stoica
2022	J	`jnl`	Context-Aware Streaming Perception in Dynamic Environments. CoRR Gur-Eyal Sela, Ionel Gog, Justin Wong, Kumar Krishna Agrawal, Xiangxi Mo, Sukrit Kalra, Peter Schafhalter, Eric Leong, Xin Wang, Bharathan Balaji, Joseph Gonzalez, Ion Stoica
2022	J	`jnl`	Investigating Power laws in Deep Representation Learning. CoRR Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal, Blake A. Richards
2022	A	`conf`	Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. AISTATS Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya K. Muthukumar, Ashwin Pananjady
2021	J	`jnl`	Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits. CoRR Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady
2020	A*	`conf`	Model AI Assignments 2020. AAAI Todd W. Neller, Stephen Keeley, Michael Guerzhoy, Wolfgang Hönig, Jiaoyang Li, Sven Koenig, Ameet Soni, Krista Thomason, Lisa Zhang, Bibin Sebastian, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar Krishna Agrawal, James Allingham, Sejong Yoon, Jonathan Chen, Tom Larsen, Marion Neumann, Narges Norouzi, Ryan Hausen, Matthew Evett
2019	—	`conf`	Discrete Flows: Invertible Generative Models of Discrete Data. DGS@ICLR Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019	A*	`conf`	Discrete Flows: Invertible Generative Models of Discrete Data. NeurIPS Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019	J	`jnl`	Discrete Flows: Invertible Generative Models of Discrete Data. CoRR Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019	—	`conf`	Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning. ICLR (Poster) Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson
2019	—	`conf`	GANSynth: Adversarial Neural Audio Synthesis. ICLR (Poster) Jesse H. Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts
2019	J	`jnl`	GANSynth: Adversarial Neural Audio Synthesis. CoRR Jesse H. Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts
2019	A*	`conf`	Model AI Assignments 2019. AAAI Todd W. Neller, Raja Sooriamurthi, Michael Guerzhoy, Lisa Zhang, Paul G. Talaga, Christopher Archibald, Adam Summerville, Joseph C. Osborn, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar Krishna Agrawal, Nate Derbinsky, Elena Strange, Marion Neumann, Jonathan Chen, Zac Christensen, Michael Wollowski, Oscar Youngquist
2018	J	`jnl`	Addressing Sample Inefficiency and Reward Bias in Inverse Reinforcement Learning. CoRR Ilya Kostrikov, Kumar Krishna Agrawal, Sergey Levine, Jonathan Tompson
2018	J	`jnl`	Towards Mixed Optimization for Reinforcement Learning with Program Synthesis. CoRR Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh
2017	—	`conf`	Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games. RepEval@EMNLP Anmol Gulati, Kumar Krishna Agrawal
2017	—	`conf`	Recurrent Memory Addressing for Describing Videos. CVPR Workshops Arnav Kumar Jain, Abhinav Agarwalla, Kumar Krishna Agrawal, Pabitra Mitra
2016	J	`jnl`	Recurrent Memory Addressing for describing videos. CoRR Kumar Krishna Agrawal, Arnav Kumar Jain, Abhinav Agarwalla, Pabitra Mitra