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Piyush Rai
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2020 – today
- 2024
- [j7]Pratik Mazumder
, Pravendra Singh
Rectification-Based Knowledge Retention for Task Incremental Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1561-1575 (2024) - [j6]Gargi Singh
Text-Based Fine-Grained Emotion Prediction. IEEE Trans. Affect. Comput. 15(2): 405-416 (2024) - [c70]Soumya Banerjee, Vinay Kumar Verma, Avideep Mukherjee, Deepak Gupta, Vinay P. Namboodiri, Piyush Rai:
VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference. ICRA 2024: 493-500 - [i50]Aishwarya Gupta, Indranil Saha, Piyush Rai:
Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage. CoRR abs/2408.06766 (2024) - [i49]Avideep Mukherjee, Soumya Banerjee, Vinay P. Namboodiri, Piyush Rai:
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance. CoRR abs/2408.17095 (2024) - 2023
- [c69]Shrey Bhatt, Aishwarya Gupta, Piyush Rai:
Federated Learning with Uncertainty via Distilled Predictive Distributions. ACML 2023: 153-168 - [c68]Amit Chandak
Gradient Perturbation-based Efficient Deep Ensembles. COMAD/CODS 2023: 28-36 - [c67]Dhanajit Brahma, Piyush Rai:
A Probabilistic Framework for Lifelong Test-Time Adaptation. CVPR 2023: 3582-3591 - [i48]Soumya Banerjee, Vinay Kumar Verma, Avideep Mukherjee, Deepak Gupta, Vinay P. Namboodiri, Piyush Rai:
VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference. CoRR abs/2309.08227 (2023) - 2022
- [j5]Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar:
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents. Trans. Mach. Learn. Res. 2022 (2022) - [c66]K. J. Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N. Balasubramanian:
Spacing Loss for Discovering Novel Categories. CVPR Workshops 2022: 3760-3765 - [c65]K. J. Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N. Balasubramanian:
Novel Class Discovery Without Forgetting. ECCV (24) 2022: 570-586 - [i47]Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar:
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents. CoRR abs/2201.00308 (2022) - [i46]Ankur Singh, Piyush Rai:
Semi-Supervised Super-Resolution. CoRR abs/2204.08192 (2022) - [i45]K. J. Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N. Balasubramanian:
Spacing Loss for Discovering Novel Categories. CoRR abs/2204.10595 (2022) - [i44]Shrey Bhatt, Aishwarya Gupta, Piyush Rai:
Bayesian Federated Learning via Predictive Distribution Distillation. CoRR abs/2206.07562 (2022) - [i43]K. J. Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N. Balasubramanian:
Novel Class Discovery without Forgetting. CoRR abs/2207.10659 (2022) - [i42]Dhanajit Brahma
A Probabilistic Framework for Lifelong Test-Time Adaptation. CoRR abs/2212.09713 (2022) - 2021
- [c64]Pratik Mazumder, Pravendra Singh, Piyush Rai:
Few-Shot Lifelong Learning. AAAI 2021: 2337-2345 - [c63]Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai:
Generalized Adversarially Learned Inference. AAAI 2021: 7185-7192 - [c62]Gargi Singh, Dhanajit Brahma
Fine-Grained Emotion Prediction by Modeling Emotion Definitions. ACII 2021: 1-8 - [c61]Vinay Kumar Verma, Kevin J. Liang, Nikhil Mehta, Piyush Rai, Lawrence Carin:
Efficient Feature Transformations for Discriminative and Generative Continual Learning. CVPR 2021: 13865-13875 - [c60]Pravendra Singh
Rectification-Based Knowledge Retention for Continual Learning. CVPR 2021: 15282-15291 - [c59]Abhishek Kumar, Sunabha Chatterjee, Piyush Rai:
Bayesian Structural Adaptation for Continual Learning. ICML 2021: 5850-5860 - [c58]Mohammed Asad Karim, Vinay Kumar Verma, Pravendra Singh, Vinay P. Namboodiri, Piyush Rai:
Knowledge Consolidation based Class Incremental Online Learning with Limited Data. IJCAI 2021: 2621-2627 - [c57]Sakshi Varshney, Vinay Kumar Verma, P. K. Srijith, Lawrence Carin, Piyush Rai:
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks. NeurIPS 2021: 15175-15187 - [c56]Vinay Kumar Verma, Ashish Mishra, Anubha Pandey, Hema A. Murthy, Piyush Rai:
Towards Zero-Shot Learning with Fewer Seen Class Examples. WACV 2021: 2240-2250 - [i41]Pratik Mazumder, Pravendra Singh, Piyush Rai:
Few-Shot Lifelong Learning. CoRR abs/2103.00991 (2021) - [i40]Sakshi Varshney, Vinay Kumar Verma, Lawrence Carin, Piyush Rai:
Efficient Continual Adaptation for Generative Adversarial Networks. CoRR abs/2103.04032 (2021) - [i39]Vinay Kumar Verma, Kevin J. Liang, Nikhil Mehta, Piyush Rai, Lawrence Carin:
Efficient Feature Transformations for Discriminative and Generative Continual Learning. CoRR abs/2103.13558 (2021) - [i38]Rahul Sharma, Soumya Banerjee, Dootika Vats, Piyush Rai:
Variational Rejection Particle Filtering. CoRR abs/2103.15343 (2021) - [i37]Pravendra Singh, Pratik Mazumder, Piyush Rai, Vinay P. Namboodiri:
Rectification-based Knowledge Retention for Continual Learning. CoRR abs/2103.16597 (2021) - [i36]Mohammed Asad Karim, Vinay Kumar Verma, Pravendra Singh, Vinay P. Namboodiri, Piyush Rai:
Knowledge Consolidation based Class Incremental Online Learning with Limited Data. CoRR abs/2106.06795 (2021) - [i35]Gargi Singh, Dhanajit Brahma, Piyush Rai, Ashutosh Modi:
Fine-Grained Emotion Prediction by Modeling Emotion Definitions. CoRR abs/2107.12135 (2021) - [i34]Dhanajit Brahma, Vinay Kumar Verma, Piyush Rai:
Hypernetworks for Continual Semi-Supervised Learning. CoRR abs/2110.01856 (2021) - [i33]Avinandan Bose, Aniket Das, Yatin Dandi, Piyush Rai:
NeurInt : Learning to Interpolate through Neural ODEs. CoRR abs/2111.04123 (2021) - [i32]Ansh Khurana, Sujoy Paul, Piyush Rai, Soma Biswas, Gaurav Aggarwal:
SITA: Single Image Test-time Adaptation. CoRR abs/2112.02355 (2021) - 2020
- [j4]Pravendra Singh
HetConv: Beyond Homogeneous Convolution Kernels for Deep CNNs. Int. J. Comput. Vis. 128(8): 2068-2088 (2020) - [j3]Pravendra Singh
Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning. IEEE J. Sel. Top. Signal Process. 14(4): 838-847 (2020) - [c55]Arindam Sarkar, Nikhil Mehta, Piyush Rai:
Graph Representation Learning via Ladder Gamma Variational Autoencoders. AAAI 2020: 5604-5611 - [c54]Vinay Kumar Verma, Dhanajit Brahma
Meta-Learning for Generalized Zero-Shot Learning. AAAI 2020: 6062-6069 - [c53]Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar:
P-SIF: Document Embeddings Using Partition Averaging. AAAI 2020: 7863-7870 - [c52]Pawan Kumar, Dhanajit Brahma
Deep Attentive Ranking Networks for Learning to Order Sentences. AAAI 2020: 8115-8122 - [c51]He Zhao, Piyush Rai, Lan Du, Wray L. Buntine
Variational Autoencoders for Sparse and Overdispersed Discrete Data. AISTATS 2020: 1684-1694 - [c50]Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai:
Calibrating CNNs for Lifelong Learning. NeurIPS 2020 - [c49]Pravendra Singh
Leveraging Filter Correlations for Deep Model Compression. WACV 2020: 824-833 - [c48]Vinay Kumar Verma, Pravendra Singh
A "Network Pruning Network" Approach to Deep Model Compression. WACV 2020: 2998-3007 - [c47]Yatin Dandi, Aniket Das, Soumye Singhal, Vinay P. Namboodiri
Jointly Trained Image and Video Generation using Residual Vectors. WACV 2020: 3017-3031 - [c46]Varun Khare, Divyat Mahajan, Homanga Bharadhwaj, Vinay Kumar Verma, Piyush Rai:
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation. WACV 2020: 3090-3099 - [i31]Pawan Kumar, Dhanajit Brahma, Harish Karnick, Piyush Rai:
Deep Attentive Ranking Networks for Learning to Order Sentences. CoRR abs/2001.00056 (2020) - [i30]Vinay Kumar Verma, Pravendra Singh, Vinay P. Namboodiri, Piyush Rai:
A "Network Pruning Network" Approach to Deep Model Compression. CoRR abs/2001.05545 (2020) - [i29]Saiteja Utpala, Piyush Rai:
Quantile Regularization: Towards Implicit Calibration of Regression Models. CoRR abs/2002.12860 (2020) - [i28]Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar:
P-SIF: Document Embeddings Using Partition Averaging. CoRR abs/2005.09069 (2020) - [i27]Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai:
Generalized Adversarially Learned Inference. CoRR abs/2006.08089 (2020) - [i26]Vinay Kumar Verma, Ashish Mishra, Anubha Pandey, Hema A. Murthy, Piyush Rai:
Towards Zero-Shot Learning with Fewer Seen Class Examples. CoRR abs/2011.07279 (2020)
2010 – 2019
- 2019
- [j2]Archit Sharma, Siddhartha Saxena
A flexible probabilistic framework for large-margin mixture of experts. Mach. Learn. 108(8-9): 1369-1393 (2019) - [c45]Vivek Gupta, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, Piyush Rai:
Distributional Semantics Meets Multi-Label Learning. AAAI 2019: 3747-3754 - [c44]Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha P. Talukdar:
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks. ACL (1) 2019: 3308-3318 - [c43]Rajat Panda, Ankit Pensia, Nikhil Mehta, Mingyuan Zhou
Deep Topic Models for Multi-label Learning. AISTATS 2019: 2849-2857 - [c42]Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra, Piyush Rai:
Generative Model for Zero-Shot Sketch-Based Image Retrieval. CVPR Workshops 2019: 704-713 - [c41]Pravendra Singh
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs. CVPR 2019: 4835-4844 - [c40]Nikhil Mehta, Lawrence Carin, Piyush Rai:
Stochastic Blockmodels meet Graph Neural Networks. ICML 2019: 4466-4474 - [c39]Pravendra Singh
Play and Prune: Adaptive Filter Pruning for Deep Model Compression. IJCAI 2019: 3460-3466 - [i25]Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri:
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs. CoRR abs/1903.04120 (2019) - [i24]Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra, Piyush Rai:
Generative Model for Zero-Shot Sketch-Based Image Retrieval. CoRR abs/1904.08542 (2019) - [i23]He Zhao, Piyush Rai, Lan Du, Wray L. Buntine, Mingyuan Zhou:
Variational Autoencoders for Sparse and Overdispersed Discrete Data. CoRR abs/1905.00616 (2019) - [i22]Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri:
Play and Prune: Adaptive Filter Pruning for Deep Model Compression. CoRR abs/1905.04446 (2019) - [i21]Nikhil Mehta, Lawrence Carin, Piyush Rai:
Stochastic Blockmodels meet Graph Neural Networks. CoRR abs/1905.05738 (2019) - [i20]Varun Khare, Divyat Mahajan, Homanga Bharadhwaj, Vinay Kumar Verma, Piyush Rai:
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation. CoRR abs/1906.03038 (2019) - [i19]Vinay Kumar Verma, Dhanajit Brahma, Piyush Rai:
A Meta-Learning Framework for Generalized Zero-Shot Learning. CoRR abs/1909.04344 (2019) - [i18]Rahul Sharma, Abhishek Kumar, Piyush Rai:
Refined α-Divergence Variational Inference via Rejection Sampling. CoRR abs/1909.07627 (2019) - [i17]Abhishek Kumar, Sunabha Chatterjee, Piyush Rai:
Nonparametric Bayesian Structure Adaptation for Continual Learning. CoRR abs/1912.03624 (2019) - [i16]Karthikeyan K, Shubham Kumar Bharti, Piyush Rai:
On the relationship between multitask neural networks and multitask Gaussian Processes. CoRR abs/1912.05723 (2019) - [i15]Yatin Dandi, Aniket Das, Soumye Singhal, Vinay P. Namboodiri, Piyush Rai:
Jointly Trained Image and Video Generation using Residual Vectors. CoRR abs/1912.07991 (2019) - 2018
- [c38]Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin:
Zero-Shot Learning via Class-Conditioned Deep Generative Models. AAAI 2018: 4211-4218 - [c37]Ankush Gupta, Arvind Agarwal, Prawaan Singh, Piyush Rai:
A Deep Generative Framework for Paraphrase Generation. AAAI 2018: 5149-5156 - [c36]He Zhao, Piyush Rai, Lan Du, Wray L. Buntine:
Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences. AISTATS 2018: 1943-1951 - [c35]Vinay Kumar Verma, Gundeep Arora, Ashish Mishra, Piyush Rai:
Generalized Zero-Shot Learning via Synthesized Examples. CVPR 2018: 4281-4289 - [c34]Gundeep Arora, Anupreet Porwal
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels. IJCAI 2018: 2000-2006 - [c33]Ashish Mishra, Vinay Kumar Verma, M. Shiva Krishna Reddy, Arulkumar Subramaniam, Piyush Rai, Anurag Mittal:
A Generative Approach to Zero-Shot and Few-Shot Action Recognition. WACV 2018: 372-380 - [i14]Ashish Mishra, Vinay Kumar Verma, M. Shiva Krishna Reddy, Arulkumar Subramaniam, Piyush Rai, Anurag Mittal:
A Generative Approach to Zero-Shot and Few-Shot Action Recognition. CoRR abs/1801.09086 (2018) - [i13]Gundeep Arora, Anupreet Porwal, Kanupriya Agarwal, Avani Samdariya, Piyush Rai:
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels. CoRR abs/1807.03570 (2018) - [i12]Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Piyush Rai, Chiranjib Bhattacharyya, Partha P. Talukdar:
Graph Convolutional Networks based Word Embeddings. CoRR abs/1809.04283 (2018) - [i11]Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri:
Leveraging Filter Correlations for Deep Model Compression. CoRR abs/1811.10559 (2018) - 2017
- [c32]Piyush Rai:
Non-Negative Inductive Matrix Completion for Discrete Dyadic Data. AAAI 2017: 2499-2505 - [c31]Changwei Hu, Piyush Rai, Lawrence Carin:
Deep Generative Models for Relational Data with Side Information. ICML 2017: 1578-1586 - [c30]Vikas Jain, Nirbhay Modhe, Piyush Rai:
Scalable Generative Models for Multi-label Learning with Missing Labels. ICML 2017: 1636-1644 - [c29]Vinay Kumar Verma, Piyush Rai:
A Simple Exponential Family Framework for Zero-Shot Learning. ECML/PKDD (2) 2017: 792-808 - [c28]Abhilash Gaure, Aishwarya Gupta, Vinay Kumar Verma, Piyush Rai:
A Probabilistic Framework for Multi-Label Learning with Unseen Labels. UAI 2017 - [i10]Vinay Kumar Verma, Piyush Rai:
A Simple Exponential Family Framework for Zero-Shot Learning. CoRR abs/1707.08040 (2017) - [i9]Ankush Gupta, Arvind Agarwal, Prawaan Singh, Piyush Rai:
A Deep Generative Framework for Paraphrase Generation. CoRR abs/1709.05074 (2017) - [i8]Rahul Wadbude, Vivek Gupta, Piyush Rai, Nagarajan Natarajan, Harish Karnick:
Leveraging Distributional Semantics for Multi-Label Learning. CoRR abs/1709.05976 (2017) - [i7]Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin:
Zero-Shot Learning via Class-Conditioned Deep Generative Models. CoRR abs/1711.05820 (2017) - [i6]Gundeep Arora, Vinay Kumar Verma, Ashish Mishra, Piyush Rai:
Generalized Zero-Shot Learning via Synthesized Examples. CoRR abs/1712.03878 (2017) - 2016
- [c27]Changwei Hu, Piyush Rai, Lawrence Carin:
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information. AISTATS 2016: 1124-1132 - [c26]Changwei Hu, Piyush Rai, Lawrence Carin:
Topic-Based Embeddings for Learning from Large Knowledge Graphs. AISTATS 2016: 1133-1141 - [c25]Saurav Muralidharan, Amit Roy, Mary W. Hall
Architecture-Adaptive Code Variant Tuning. ASPLOS 2016: 325-338 - [c24]Wenlin Wang, Changyou Chen, Wenlin Chen, Piyush Rai, Lawrence Carin
Deep Metric Learning with Data Summarization. ECML/PKDD (1) 2016: 777-794 - [i5]Wenlin Wang, Changyou Chen, Wenqi Wang, Piyush Rai, Lawrence Carin:
Earliness-Aware Deep Convolutional Networks for Early Time Series Classification. CoRR abs/1611.04578 (2016) - 2015
- [c23]Wenzhao Lian, Piyush Rai, Esther Salazar, Lawrence Carin:
Integrating Features and Similarities: Flexible Models for Heterogeneous Multiview Data. AAAI 2015: 2757-2763 - [c22]Piyush Rai, Yingjian Wang, Lawrence Carin:
Leveraging Features and Networks for Probabilistic Tensor Decomposition. AAAI 2015: 2942-2948 - [c21]Yi Zhen, Piyush Rai, Hongyuan Zha, Lawrence Carin:
Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision. AAAI 2015: 3203-3209 - [c20]Piyush Rai, Changwei Hu, Matthew Harding, Lawrence Carin:
Scalable Probabilistic Tensor Factorization for Binary and Count Data. IJCAI 2015: 3770-3776 - [c19]Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin:
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings. NIPS 2015: 3222-3230 - [c18]Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding
Scalable Bayesian Non-negative Tensor Factorization for Massive Count Data. ECML/PKDD (2) 2015: 53-70 - [c17]Changwei Hu, Piyush Rai, Lawrence Carin:
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors. UAI 2015: 375-384 - [i4]Changwei Hu, Piyush Rai, Lawrence Carin:
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors. CoRR abs/1508.04210 (2015) - [i3]Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin:
Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data. CoRR abs/1508.04211 (2015) - 2014
- [c16]Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunson, Lawrence Carin:
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors. ICML 2014: 1800-1808 - 2013
- [b1]Piyush Rai:
Learning Latent Structures Via Bayesian Nonparametrics: New Models and Efficient Interference. University of Utah, USA, 2013 - [c15]Joyce Jiyoung Whang, Piyush Rai, Inderjit S. Dhillon:
Stochastic Blockmodel with Cluster Overlap, Relevance Selection, and Similarity-Based Smoothing. ICDM 2013: 817-826 - 2012
- [j1]Anusua Trivedi, Piyush Rai, Hal Daumé III, Scott L. DuVall
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering. ACM Trans. Intell. Syst. Technol. 3(4): 67:1-67:18 (2012) - [c14]Alexandre Passos, Piyush Rai, Jacques Wainer, Hal Daumé III:
Flexible Modeling of Latent Task Structures in Multitask Learning. ICML 2012 - [c13]Piyush Rai, Abhishek Kumar, Hal Daumé III:
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression. NIPS 2012: 3194-3202 - 2011
- [c12]Piyush Rai, Hal Daumé III:
Beam Search based MAP Estimates for the Indian Buffet Process. ICML 2011: 705-712 - [c11]Niraj Kumar, Piyush Rai, Chandrika Pulla, C. V. Jawahar:
Video Scene Segmentation with a Semantic Similarity. IICAI 2011: 970-981 - [c10]Junxing Zhang, Sneha Kumar Kasera, Neal Patwari, Piyush Rai:
Distinguishing locations across perimeters using wireless link measurements. INFOCOM 2011: 3155-3163 - [c9]Jiarong Jiang, Piyush Rai, Hal Daumé III:
Message-Passing for Approximate MAP Inference with Latent Variables. NIPS 2011: 1197-1205 - [c8]Abhishek Kumar, Piyush Rai, Hal Daumé III:
Co-regularized Multi-view Spectral Clustering. NIPS 2011: 1413-1421 - [c7]Avishek Saha, Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian
Active Supervised Domain Adaptation. ECML/PKDD (3) 2011: 97-112 - [c6]Avishek Saha, Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian:
Online Learning of Multiple Tasks and Their Relationships. AISTATS 2011: 643-651 - 2010
- [c5]Anusua Trivedi, Piyush Rai, Scott L. DuVall, Hal Daumé III:
Exploiting tag and word correlations for improved webpage clustering. SMUC@CIKM 2010: 3-12 - [c4]Piyush Rai, Hal Daumé III:
Infinite Predictor Subspace Models for Multitask Learning. AISTATS 2010: 613-620
2000 – 2009
- 2009
- [c3]Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian:
Streamed Learning: One-Pass SVMs. IJCAI 2009: 1211-1216 - [c2]Piyush Rai, Hal Daumé III:
Multi-Label Prediction via Sparse Infinite CCA. NIPS 2009: 1518-1526 - [i2]Piyush Rai, Hal Daumé III:
The Infinite Hierarchical Factor Regression Model. CoRR abs/0908.0570 (2009) - [i1]Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian:
Streamed Learning: One-Pass SVMs. CoRR abs/0908.0572 (2009) - 2008
- [c1]Piyush Rai, Hal Daumé III:
The Infinite Hierarchical Factor Regression Model. NIPS 2008: 1321-1328
Coauthor Index
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