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ORCIDMohak Shah
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- affiliation: LG Silicon Valley Lab, Santa Clara, USA
- affiliation: Bosch
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2020 – today
- 2024
[c29]Ankur Teredesai
, Michael Zeller, Mohak Shah, Shenghua Bao, Wee Hyong Tok, Linsey Pang:
The 2nd International Workshop: From Innovation to Scale (I2S) - Successfully Build, Commercialize, and Scale AI Innovations. KDD 2024: 6739-6740- [i20]Mohak Shah:
A Social Outcomes and Priorities centered (SOP) Framework for AI policy. CoRR abs/2411.08241 (2024) - 2021
- [j11]Sauptik Dhar, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
A Survey of On-Device Machine Learning: An Algorithms and Learning Theory Perspective. ACM Trans. Internet Things 2(3): 15:1-15:49 (2021) - [c28]Shengdong Zhang, Ehsan Nezhadarya, Homa Fashandi, Jiayi Liu, Darin Graham, Mohak Shah:
Stochastic Whitening Batch Normalization. CVPR 2021: 10978-10987 - [c27]Olimpiya Saha, Viswanath Ganapathy, Javad Heydari, Guohua Ren, Mohak Shah:
Efficient Coverage Path Planning in Initially Unknown Environments Using Graph Representation. ICAR 2021: 1003-1010 - [c26]Olimpiya Saha, Guohua Ren, Javad Heydari, Viswanath Ganapathy, Mohak Shah:
Deep Reinforcement Learning Based Online Area Covering Autonomous Robot. ICARA 2021: 21-25 - [c25]Olimpiya Saha, Guohua Ren, Javad Heydari, Viswanath Ganapathy, Mohak Shah:
Online Area Covering Robot in Unknown Dynamic Environments. ICARA 2021: 38-42 - [i19]Shengdong Zhang, Ehsan Nezhadarya, Homa Fashandi, Jiayi Liu, Darin Graham, Mohak Shah:
Stochastic Whitening Batch Normalization. CoRR abs/2106.04413 (2021) - [i18]Sauptik Dhar, Javad Heydari, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
Evolving GANs: When Contradictions Turn into Compliance. CoRR abs/2106.09946 (2021) - [i17]Viswanath Ganapathy, Sauptik Dhar, Olimpiya Saha, Pelin Kurt Garberson, Javad Heydari, Mohak Shah:
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it. CoRR abs/2110.05015 (2021) - 2020
- [j10]Junyao Guo, Unmesh Kurup, Mohak Shah:
Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving. IEEE Trans. Intell. Transp. Syst. 21(8): 3135-3151 (2020) - [c24]Samarth Tripathi, Jiayi Liu, Sauptik Dhar, Unmesh Kurup, Mohak Shah:
Improving Model Training by Periodic Sampling over Weight Distributions. IEEE BigData 2020: 112-122 - [c23]Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah:
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. ICASSP 2020: 3597-3601 - [c22]Dimple Kaul, Mohak Shah, Neeraj Dhakephalkar:
A Study on the Factors Influencing Behavioral Intention of Indian Consumers in Adopting Voice Assistants. TDIT (1) 2020: 474-483 - [i16]Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey. CoRR abs/2005.04275 (2020) - [i15]Sauptik Dhar, Unmesh Kurup, Mohak Shah:
Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization. CoRR abs/2007.13322 (2020)
2010 – 2019
- 2019
- [j9]Shujian Yu, Zubin Abraham, Heng Wang, Mohak Shah, Yantao Wei, José C. Príncipe:
Concept drift detection and adaptation with hierarchical hypothesis testing. J. Frankl. Inst. 356(5): 3187-3215 (2019) - [j8]Rayid Ghani, Lisa Green, Alberto Bengoa, Mohak Shah:
Solve for Good: A Data Science for Social Good Marketplace. SIGKDD Explor. 21(2): 3-5 (2019) - [j7]Abdeltawab M. Hendawi, Jayant Gupta, Jiayi Liu, Ankur Teredesai, Naveen Ramakrishnan, Mohak Shah, Shaker H. Ali El-Sappagh, Kyung-Sup Kwak, Mohamed H. Ali:
Benchmarking large-scale data management for Internet of Things. J. Supercomput. 75(12): 8207-8230 (2019) - [c21]Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
Auptimizer - an Extensible, Open-Source Framework for Hyperparameter Tuning. IEEE BigData 2019: 339-348 - [c20]Javad Heydari, Viswanath Ganapathy, Mohak Shah:
Dynamic Task Offloading in Multi-Agent Mobile Edge Computing Networks. GLOBECOM 2019: 1-6 - [c19]Sauptik Dhar, Vladimir Cherkassky, Mohak Shah:
Multiclass Learning from Contradictions. NeurIPS 2019: 8398-8408 - [i14]Samarth Tripathi, Jiayi Liu, Unmesh Kurup, Mohak Shah:
Robust Neural Network Training using Periodic Sampling over Model Weights. CoRR abs/1905.05774 (2019) - [i13]Youngsuk Park, Sauptik Dhar, Stephen P. Boyd, Mohak Shah:
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize. CoRR abs/1910.07056 (2019) - [i12]Sauptik Dhar, Junyao Guo, Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
On-Device Machine Learning: An Algorithms and Learning Theory Perspective. CoRR abs/1911.00623 (2019) - [i11]Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
Auptimizer - an Extensible, Open-Source Framework for Hyperparameter Tuning. CoRR abs/1911.02522 (2019) - 2018
- [c18]Abdeltawab M. Hendawi, Jayant Gupta, Jiayi Liu, Ankur Teredesai, Naveen Ramakrishnan, Mohak Shah, Mohamed H. Ali:
Distributed NoSQL Data Stores: Performance Analysis and a Case Study. IEEE BigData 2018: 1937-1944 - [i10]Jayanta K. Dutta, Jiayi Liu, Unmesh Kurup, Mohak Shah:
Effective Building Block Design for Deep Convolutional Neural Networks using Search. CoRR abs/1801.08577 (2018) - [i9]Jiayi Liu, Samarth Tripathi, Unmesh Kurup, Mohak Shah:
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling. CoRR abs/1807.00847 (2018) - [i8]Sauptik Dhar, Vladimir Cherkassky, Mohak Shah:
Multiclass Universum SVM. CoRR abs/1808.08111 (2018) - [i7]Junyao Guo, Unmesh Kurup, Mohak Shah:
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving. CoRR abs/1811.11277 (2018) - 2017
- [c17]Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah:
Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. ICASSP 2017: 5970-5974 - [c16]Pranjul Yadav, Unmesh Kurup, Mohak Shah:
Structured Causal Inference for Rare Events: An Industrial Application to Analyze Heating-Cooling Device Failure. ICMLA 2017: 1055-1060 - [c15]Seyed Hamid Mirebrahim, Mohammad Shokoohi-Yekta, Unmesh Kurup, Torsten Welfonder, Mohak Shah:
A clustering-based rule-mining approach for monitoring long-term energy use and understanding system behavior. BuildSys 2017: 5:1-5:9 - [i6]Shujian Yu, Zubin Abraham, Heng Wang, Mohak Shah, José C. Príncipe:
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing. CoRR abs/1707.07821 (2017) - 2016
- [c14]Juergen Heit, Jiayi Liu, Mohak Shah:
An architecture for the deployment of statistical models for the big data era. IEEE BigData 2016: 1377-1384 - [c13]Mark Grechanik, Nitin Prabhu, Daniel Graham, Denys Poshyvanyk, Mohak Shah:
Can Software Project Maturity Be Accurately Predicted Using Internal Source Code Metrics? MLDM 2016: 774-789 - [e2]Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, Rajeev Rastogi:
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. ACM 2016, ISBN 978-1-4503-4232-2 [contents] - [i5]Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah:
Universum Learning for Multiclass SVM. CoRR abs/1609.09162 (2016) - [i4]Shengdong Zhang, Soheil Bahrampour, Naveen Ramakrishnan, Mohak Shah:
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification. CoRR abs/1612.01254 (2016) - 2015
- [c12]Sauptik Dhar, Congrui Yi, Naveen Ramakrishnan, Mohak Shah:
ADMM based scalable machine learning on Spark. IEEE BigData 2015: 1174-1182 - [i3]Mohak Shah:
Big Data and the Internet of Things. CoRR abs/1503.07092 (2015) - [i2]Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, Mohak Shah:
Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning. CoRR abs/1511.06435 (2015) - 2012
- [j6]Mohak Shah, Mario Marchand, Jacques Corbeil:
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data. IEEE Trans. Pattern Anal. Mach. Intell. 34(1): 174-186 (2012) - [j5]Zahra Karimaghaloo, Mohak Shah, Simon J. Francis, Douglas L. Arnold, D. Louis Collins, Tal Arbel:
Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields. IEEE Trans. Medical Imaging 31(6): 1181-1194 (2012) - 2011
- [j4]Mohak Shah, Yiming Xiao, Nagesh K. Subbanna, Simon J. Francis, Douglas L. Arnold, D. Louis Collins, Tal Arbel:
Evaluating intensity normalization on MRIs of human brain with multiple sclerosis. Medical Image Anal. 15(2): 267-282 (2011) - [j3]Mohak Shah, Jacques Corbeil:
A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments. IEEE ACM Trans. Comput. Biol. Bioinform. 8(1): 14-26 (2011) - [c11]Karim T. Abou-Moustafa, Mohak Shah, Fernando De la Torre, Frank P. Ferrie:
Relaxed Exponential Kernels for Unsupervised Learning. DAGM-Symposium 2011: 184-195 - [c10]Heidar Pirzadeh, Abdelwahab Hamou-Lhadj, Mohak Shah:
Exploiting text mining techniques in the analysis of execution traces. ICSM 2011: 223-232 - [c9]Mohak Shah:
Generalized Agreement Statistics over Fixed Group of Experts. ECML/PKDD (3) 2011: 191-206 - [e1]Nathalie Japkowicz, Mohak Shah:
Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press 2011, ISBN 9780521196000 - 2010
- [j2]François Laviolette, Mario Marchand, Mohak Shah, Sara Shanian:
Learning the set covering machine by bound minimization and margin-sparsity trade-off. Mach. Learn. 78(1-2): 175-201 (2010) - [c8]Zahra Karimaghaloo, Mohak Shah, Simon J. Francis, Douglas L. Arnold, D. Louis Collins, Tal Arbel:
Detection of Gad-Enhancing Lesions in Multiple Sclerosis Using Conditional Random Fields. MICCAI (3) 2010: 41-48 - [c7]Yiming Xiao, Mohak Shah, Simon J. Francis, Douglas L. Arnold, Tal Arbel, D. Louis Collins:
Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis. MLMI 2010: 165-173 - [i1]Mohak Shah, Mario Marchand, Jacques Corbeil:
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data. CoRR abs/1005.0530 (2010)
2000 – 2009
- 2008
- [c6]Mohak Shah:
Risk Bounds for Randomized Sample Compressed Classifiers. NIPS 2008: 1449-1456 - 2007
- [c5]Mohak Shah:
Sample compression bounds for decision trees. ICML 2007: 799-806 - 2006
- [j1]Mohak Shah, Marina Sokolova, Stan Szpakowicz:
Process-Specific Information for Learning Electronic Negotiation Outcomes. Fundam. Informaticae 74(2-3): 351-373 (2006) - 2005
- [c4]François Laviolette, Mario Marchand, Mohak Shah:
Margin-Sparsity Trade-Off for the Set Covering Machine. ECML 2005: 206-217 - [c3]François Laviolette, Mario Marchand, Mohak Shah:
A PAC-Bayes approach to the Set Covering Machine. NIPS 2005: 731-738 - 2004
- [c2]Mario Marchand, Mohak Shah:
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. NIPS 2004: 881-888 - 2003
- [c1]Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova:
The Set Covering Machine with Data-Dependent Half-Spaces. ICML 2003: 520-527
Coauthor Index
aka: Jiayi (Jason) Liu
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