FuncICA for Time Series Pattern Discovery. - Nishant A. Mehta; Alexander G. Gray (SDM, 2009)

Recognizing Sign Language from Brain Imaging. - Nishant Ajay Mehta; Thad Starner; Melody Moore Jackson; Karolyn O. Babalola; George Andrew James (ICPR, 2010)

Generative and Latent Mean Map Kernels - Nishant A. Mehta; Alexander G. Gray (CoRR, 2010)

Optimal Control Strategies for an SSVEP-Based Brain-Computer Interface. - Nishant Ajay Mehta; Sadhir Hussain S. Hameed; Melody Moore Jackson (Int. J. Hum. Comput. Interact., 2011)

Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL. - Nishant A. Mehta; Dongryeol Lee; Alexander G. Gray (NIPS, 2012)

Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep. - Jacqueline Fairley; George K. Georgoulas; Nishant A. Mehta; Alexander G. Gray; Donald L. Bliwise (Biomed. Signal Process. Control., 2012)

On the Sample Complexity of Predictive Sparse Coding - Nishant A. Mehta; Alexander G. Gray (CoRR, 2012)

Sparsity-Based Generalization Bounds for Predictive Sparse Coding. - Nishant Ajay Mehta; Alexander G. Gray (ICML (1), 2013)

MLPACK: a scalable C++ machine learning library. - Ryan R. Curtin; James R. Cline; N. P. Slagle; William B. March; Parikshit Ram; Nishant A. Mehta; Alexander G. Gray (J. Mach. Learn. Res., 2013)

From Stochastic Mixability to Fast Rates. - Nishant A. Mehta; Robert C. Williamson (NIPS, 2014)

On sparse representations and new meta-learning paradigms for representation learning. - Nishant A. Mehta (2014)

Generalized Mixability via Entropic Duality. - Mark D. Reid; Rafael M. Frongillo; Robert C. Williamson; Nishant A. Mehta (COLT, 2015)

Fast rates in statistical and online learning. - Tim van Erven; Peter D. Grünwald; Nishant A. Mehta; Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2015)

Fast Rates with Unbounded Losses. - Peter D. Grünwald; Nishant A. Mehta (CoRR, 2016)

From exp-concavity to variance control: O(1/n) rates and online-to-batch conversion with high probability. - Nishant A. Mehta (CoRR, 2016)

CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method. - Nishant A. Mehta; Alistair P. Rendell; Anish Varghese; Christfried Webers (CoRR, 2016)

Fast rates with high probability in exp-concave statistical learning. - Nishant A. Mehta (AISTATS, 2017)

Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models. - Avery Hiebert; Cole Peterson; Alona Fyshe; Nishant A. Mehta (BlackboxNLP@EMNLP, 2018)

Multi-Observation Regression. - Rafael M. Frongillo; Nishant A. Mehta; Tom Morgan; Bo Waggoner (AISTATS, 2019)

A tight excess risk bound via a unified PAC-Bayesian-Rademacher-Shtarkov-MDL complexity. - Peter D. Grünwald; Nishant A. Mehta (ALT, 2019)

Intelligent Caching Algorithms in Heterogeneous Wireless Networks with Uncertainty. - Bingshan Hu; Yunjin Chen; Zhiming Huang 002; Nishant A. Mehta; Jianping Pan 001 (ICDCS, 2019)

Dying Experts: Efficient Algorithms with Optimal Regret Bounds. - Hamid Shayestehmanesh; Sajjad Azami; Nishant A. Mehta (NeurIPS, 2019)

Problem-dependent Regret Bounds for Online Learning with Feedback Graphs. - Bingshan Hu; Nishant A. Mehta; Jianping Pan 001 (UAI, 2019)

Safe-Bayesian Generalized Linear Regression. - Rianne de Heide; Alisa Kirichenko; Peter Grunwald; Nishant A. Mehta (AISTATS, 2020)

A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option. - Pon Kumar Sharoff; Nishant A. Mehta; Ravi Ganti (AISTATS, 2020)

Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes. - Peter D. Grünwald; Nishant A. Mehta (J. Mach. Learn. Res., 2020)

Best-case lower bounds in online learning. - Cristóbal Guzmán; Nishant A. Mehta; Ali Mortazavi (NeurIPS, 2021)

Optimal Algorithms for Private Online Learning in a Stochastic Environment. - Bingshan Hu; Zhiming Huang 002; Nishant A. Mehta (CoRR, 2021)

Thresholded linear bandits. - Nishant A. Mehta; Junpei Komiyama; Vamsi K. Potluru; Andrea Nguyen; Mica Grant-Hagen (AISTATS, 2023)

Adversarial Online Multi-Task Reinforcement Learning. - Quan Nguyen; Nishant A. Mehta (ALT, 2023)

An improved regret analysis for UCB-N and TS-N. - Nishant A. Mehta (CoRR, 2023)

On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: a regret lower bound for WSU-UX. - Ali Mortazavi; Junhao Lin; Nishant A. Mehta (AISTATS, 2024)

Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy. - Bingshan Hu; Nishant A. Mehta (COLT, 2024)

Near-optimal Per-Action Regret Bounds for Sleeping Bandits. - Quan Nguyen; Nishant A. Mehta (CoRR, 2024)

Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity. - Quan Nguyen; Nishant A. Mehta; Cristóbal Guzmán (CoRR, 2024)

No-Regret Incentive-Compatible Online Learning under Exact Truthfulness with Non-Myopic Experts. - Junpei Komiyama; Nishant A. Mehta; Ali Mortazavi (EC, 2025)

Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. - Quan Nguyen; Shinji Ito; Junpei Komiyama; Nishant A. Mehta (CoRR, 2025)