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ORCIDNishant A. Mehta
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- affiliation: Georgia Institute of Technology, College of Computing, Atlanta, GA, USA
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Books and Theses
- 2014
[b1]Nishant A. Mehta:
On sparse representations and new meta-learning paradigms for representation learning. Georgia Institute of Technology, Atlanta, GA, USA, 2014
Journal Articles
- 2020
- [j5]Peter D. Grünwald, Nishant A. Mehta:
Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes. J. Mach. Learn. Res. 21: 56:1-56:80 (2020) - 2015
- [j4]Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson:
Fast rates in statistical and online learning. J. Mach. Learn. Res. 16: 1793-1861 (2015) - 2013
- [j3]Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray:
MLPACK: a scalable C++ machine learning library. J. Mach. Learn. Res. 14(1): 801-805 (2013) - 2012
- [j2]Jacqueline Fairley, George K. Georgoulas, Nishant A. Mehta, Alexander G. Gray, Donald L. Bliwise:
Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep. Biomed. Signal Process. Control. 7(6): 606-615 (2012) - 2011
- [j1]Nishant Ajay Mehta, Sadhir Hussain S. Hameed, Melody Moore Jackson:
Optimal Control Strategies for an SSVEP-Based Brain-Computer Interface. Int. J. Hum. Comput. Interact. 27(1): 85-101 (2011)
Conference and Workshop Papers
- 2024
- [c20]Ali Mortazavi, Junhao Lin, Nishant A. Mehta:
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: a regret lower bound for WSU-UX. AISTATS 2024: 4681-4689 - [c19]Bingshan Hu, Nishant A. Mehta:
Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy. COLT 2024: 5330-5334 - 2023
- [c18]Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen:
Thresholded linear bandits. AISTATS 2023: 6968-7020 - [c17]Quan Nguyen, Nishant A. Mehta:
Adversarial Online Multi-Task Reinforcement Learning. ALT 2023: 1124-1165 - 2021
- [c16]Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi:
Best-case lower bounds in online learning. NeurIPS 2021: 21923-21934 - 2020
- [c15]Rianne de Heide, Alisa Kirichenko, Peter Grunwald, Nishant A. Mehta:
Safe-Bayesian Generalized Linear Regression. AISTATS 2020: 2623-2633 - [c14]Pon Kumar Sharoff, Nishant A. Mehta, Ravi Ganti:
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option. AISTATS 2020: 3707-3716 - 2019
- [c13]Rafael M. Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner:
Multi-Observation Regression. AISTATS 2019: 2691-2700 - [c12]Peter D. Grünwald, Nishant A. Mehta:
A tight excess risk bound via a unified PAC-Bayesian-Rademacher-Shtarkov-MDL complexity. ALT 2019: 433-465 - [c11]Bingshan Hu, Yunjin Chen, Zhiming Huang
, Nishant A. Mehta, Jianping Pan:
Intelligent Caching Algorithms in Heterogeneous Wireless Networks with Uncertainty. ICDCS 2019: 1549-1558 - [c10]Hamid Shayestehmanesh, Sajjad Azami, Nishant A. Mehta:
Dying Experts: Efficient Algorithms with Optimal Regret Bounds. NeurIPS 2019: 9983-9992 - [c9]Bingshan Hu, Nishant A. Mehta, Jianping Pan:
Problem-dependent Regret Bounds for Online Learning with Feedback Graphs. UAI 2019: 852-861 - 2018
- [c8]Avery Hiebert, Cole Peterson, Alona Fyshe, Nishant A. Mehta:
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models. BlackboxNLP@EMNLP 2018: 258-266 - 2017
- [c7]Nishant A. Mehta:
Fast rates with high probability in exp-concave statistical learning. AISTATS 2017: 1085-1093 - 2015
- [c6]Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta:
Generalized Mixability via Entropic Duality. COLT 2015: 1501-1522 - 2014
- [c5]Nishant A. Mehta, Robert C. Williamson:
From Stochastic Mixability to Fast Rates. NIPS 2014: 1197-1205 - 2013
- [c4]Nishant Ajay Mehta, Alexander G. Gray:
Sparsity-Based Generalization Bounds for Predictive Sparse Coding. ICML (1) 2013: 36-44 - 2012
- [c3]Nishant A. Mehta, Dongryeol Lee, Alexander G. Gray:
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL. NIPS 2012: 2159-2167 - 2010
- [c2]Nishant Ajay Mehta, Thad Starner, Melody Moore Jackson, Karolyn O. Babalola, George Andrew James:
Recognizing Sign Language from Brain Imaging. ICPR 2010: 3842-3845 - 2009
- [c1]Nishant A. Mehta, Alexander G. Gray:
FuncICA for Time Series Pattern Discovery. SDM 2009: 73-84
Informal and Other Publications
- 2024
- [i22]Quan Nguyen, Nishant A. Mehta:
Near-optimal Per-Action Regret Bounds for Sleeping Bandits. CoRR abs/2403.01315 (2024) - [i21]Ali Mortazavi, Junhao Lin, Nishant A. Mehta:
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX. CoRR abs/2404.05155 (2024) - [i20]Quan Nguyen, Nishant A. Mehta, Cristóbal Guzmán:
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity. CoRR abs/2410.00690 (2024) - 2023
- [i19]Quan Nguyen, Nishant A. Mehta:
Adversarial Online Multi-Task Reinforcement Learning. CoRR abs/2301.04268 (2023) - [i18]Nishant A. Mehta:
An improved regret analysis for UCB-N and TS-N. CoRR abs/2305.04093 (2023) - 2021
- [i17]Bingshan Hu, Zhiming Huang, Nishant A. Mehta:
Optimal Algorithms for Private Online Learning in a Stochastic Environment. CoRR abs/2102.07929 (2021) - [i16]Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi:
Best-Case Lower Bounds in Online Learning. CoRR abs/2106.12688 (2021) - 2020
- [i15]Pon Kumar Sharoff, Nishant A. Mehta, Ravi Ganti:
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option. CoRR abs/2003.03456 (2020) - 2019
- [i14]Rianne de Heide, Alisa Kirichenko, Nishant A. Mehta, Peter Grünwald:
Safe-Bayesian Generalized Linear Regression. CoRR abs/1910.09227 (2019) - [i13]Hamid Shayestehmanesh, Sajjad Azami, Nishant A. Mehta:
Dying Experts: Efficient Algorithms with Optimal Regret Bounds. CoRR abs/1910.13521 (2019) - 2018
- [i12]Rafael M. Frongillo, Nishant A. Mehta, Tom Morgan, Bo Waggoner:
Multi-Observation Regression. CoRR abs/1802.09680 (2018) - 2017
- [i11]Peter D. Grünwald, Nishant A. Mehta:
A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity. CoRR abs/1710.07732 (2017) - 2016
- [i10]Peter D. Grünwald, Nishant A. Mehta:
Fast Rates with Unbounded Losses. CoRR abs/1605.00252 (2016) - [i9]Nishant A. Mehta:
From exp-concavity to variance control: O(1/n) rates and online-to-batch conversion with high probability. CoRR abs/1605.01288 (2016) - [i8]Nishant A. Mehta, Alistair P. Rendell, Anish Varghese, Christfried Webers:
CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method. CoRR abs/1609.03319 (2016) - 2015
- [i7]Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson:
Fast rates in statistical and online learning. CoRR abs/1507.02592 (2015) - 2014
- [i6]Nishant A. Mehta, Robert C. Williamson:
From Stochastic Mixability to Fast Rates. CoRR abs/1406.3781 (2014) - [i5]Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson, Nishant A. Mehta:
Generalized Mixability via Entropic Duality. CoRR abs/1406.6130 (2014) - 2012
- [i4]Nishant A. Mehta, Alexander G. Gray:
On the Sample Complexity of Predictive Sparse Coding. CoRR abs/1202.4050 (2012) - [i3]Nishant A. Mehta, Dongryeol Lee, Alexander G. Gray:
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL. CoRR abs/1209.2784 (2012) - [i2]Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray:
MLPACK: A Scalable C++ Machine Learning Library. CoRR abs/1210.6293 (2012) - 2010
- [i1]Nishant A. Mehta, Alexander G. Gray:
Generative and Latent Mean Map Kernels. CoRR abs/1005.0188 (2010)
Coauthor Index
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