Abhishek Kumar

52 papers A* 14A 4Journal 25Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Score-based Causal Representation Learning: Linear and General Transformations. J. Mach. Learn. Res. Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
2024	J	`jnl`	Score-based Causal Representation Learning: Linear and General Transformations. CoRR Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
2024	A*	`conf`	The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. NeurIPS Tyler LaBonte, John C. Hill, Xinchen Zhang, Vidya Muthukumar, Abhishek Kumar
2024	J	`jnl`	The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. CoRR Tyler LaBonte, John C. Hill, Xinchen Zhang, Vidya Muthukumar, Abhishek Kumar
2023	J	`jnl`	Score-based Causal Representation Learning with Interventions. CoRR Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Abhishek Kumar, Ali Tajer
2023	A*	`conf`	Towards Last-layer Retraining for Group Robustness with Fewer Annotations. NeurIPS Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
2023	J	`jnl`	Towards Last-layer Retraining for Group Robustness with Fewer Annotations. CoRR Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
2022	J	`jnl`	DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents. CoRR Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar
2022	J	`jnl`	DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents. Trans. Mach. Learn. Res. Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar
2021	A*	`conf`	Bayesian Structural Adaptation for Continual Learning. ICML Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
2021	A*	`conf`	Generalized Adversarially Learned Inference. AAAI Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai
2020	J	`jnl`	Generalized Adversarially Learned Inference. CoRR Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai
2019	J	`jnl`	Nonparametric Bayesian Structure Adaptation for Continual Learning. CoRR Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
2019	J	`jnl`	Refined α-Divergence Variational Inference via Rejection Sampling. CoRR Rahul Sharma, Abhishek Kumar, Piyush Rai
2019	A*	`conf`	SpotTune: Transfer Learning Through Adaptive Fine-Tuning. CVPR Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogério Schmidt Feris
2018	A*	`conf`	BlockDrop: Dynamic Inference Paths in Residual Networks. CVPR Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogério Schmidt Feris
2018	A*	`conf`	Co-regularized Alignment for Unsupervised Domain Adaptation. NeurIPS Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, Bill Freeman, Gregory W. Wornell
2018	J	`jnl`	Co-regularized Alignment for Unsupervised Domain Adaptation. CoRR Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, William T. Freeman, Gregory W. Wornell
2018	A*	`conf`	Delta-encoder: an effective sample synthesis method for few-shot object recognition. NeurIPS Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogério Schmidt Feris, Raja Giryes, Alexander M. Bronstein
2018	J	`jnl`	Delta-encoder: an effective sample synthesis method for few-shot object recognition. CoRR Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Rogério Schmidt Feris, Abhishek Kumar, Raja Giryes, Alexander M. Bronstein
2018	J	`jnl`	RepMet: Representative-based metric learning for classification and one-shot object detection. CoRR Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Sharathchandra Pankanti, Rogério Schmidt Feris, Abhishek Kumar, Raja Giryes, Alexander M. Bronstein
2018	J	`jnl`	SpotTune: Transfer Learning through Adaptive Fine-tuning. CoRR Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogério Schmidt Feris
2018	—	`conf`	The Riemannian Geometry of Deep Generative Models. CVPR Workshops Hang Shao, Abhishek Kumar, P. Thomas Fletcher
2018	J	`jnl`	Understanding Unequal Gender Classification Accuracy from Face Images. CoRR Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney
2018	—	`conf`	Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. ICLR (Poster) Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
2017	J	`jnl`	BlockDrop: Dynamic Inference Paths in Residual Networks. CoRR Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogério Schmidt Feris
2017	A*	`conf`	Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification. CVPR Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogério Schmidt Feris
2017	J	`jnl`	Improved Semi-supervised Learning with GANs using Manifold Invariances. CoRR Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher
2017	A	`conf`	Local Group Invariant Representations via Orbit Embeddings. AISTATS Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, Bernhard Schölkopf
2017	J	`jnl`	Performance of natural language classifiers in a question-answering system. IBM J. Res. Dev. Raimo Bakis, Daniel P. Connors, Parijat Dube, Pavan Kapanipathi, Abhishek Kumar, Dmitry Malioutov, Chitra Venkatramani
2017	A*	`conf`	S3Pool: Pooling with Stochastic Spatial Sampling. CVPR Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, Rogério Schmidt Feris
2017	—	`conf`	Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference. NIPS Abhishek Kumar, Prasanna Sattigeri, Tom Fletcher
2017	J	`jnl`	The Riemannian Geometry of Deep Generative Models. CoRR Hang Shao, Abhishek Kumar, P. Thomas Fletcher
2017	J	`jnl`	Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. CoRR Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
2016	J	`jnl`	Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification. CoRR Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogério Schmidt Feris
2016	A	`conf`	Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices. UAI Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen
2016	J	`jnl`	Local Group Invariant Representations via Orbit Embeddings. CoRR Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher, Bernhard Schölkopf
2016	J	`jnl`	S3Pool: Pooling with Stochastic Spatial Sampling. CoRR Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, Rogério Schmidt Feris
2016	A	`conf`	Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation. AISTATS Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar
2015	A	`conf`	Near-separable Non-negative Matrix Factorization with ℓ SDM Abhishek Kumar, Vikas Sindhwani
2013	—	`conf`	Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization. ICML (1) Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur
2013	—	`—`	Learning with Multiple Similarities. Abhishek Kumar
2013	J	`jnl`	Near-separable Non-negative Matrix Factorization with ℓ CoRR Abhishek Kumar, Vikas Sindhwani
2012	A*	`conf`	A Binary Classification Framework for Two-Stage Multiple Kernel Learning. ICML Abhishek Kumar, Alexandru Niculescu-Mizil, Koray Kavukcuoglu, Hal Daumé III
2012	J	`jnl`	Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization CoRR Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur
2012	A*	`conf`	Generalized Multiview Analysis: A discriminative latent space. CVPR Abhishek Sharma, Abhishek Kumar, Hal Daumé III, David W. Jacobs
2012	A*	`conf`	Learning Task Grouping and Overlap in Multi-task Learning. ICML Abhishek Kumar, Hal Daumé III
2012	—	`conf`	Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy. STIA Manasi Datar, Prasanna Muralidharan, Abhishek Kumar, Sylvain Gouttard, Joseph Piven, Guido Gerig, Ross T. Whitaker, P. Thomas Fletcher
2012	—	`conf`	Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression. NIPS Piyush Rai, Abhishek Kumar, Hal Daumé III
2011	A*	`conf`	A Co-training Approach for Multi-view Spectral Clustering. ICML Abhishek Kumar, Hal Daumé III
2011	—	`conf`	Co-regularized Multi-view Spectral Clustering. NIPS Abhishek Kumar, Piyush Rai, Hal Daumé III
2010	—	`conf`	Co-regularization Based Semi-supervised Domain Adaptation. NIPS Hal Daumé III, Abhishek Kumar, Avishek Saha