Navin Goyal

1. Thompson Sampling for Contextual Bandits with Linear Payoffs
   2012/09/15 by Shipra Agrawal, Agrawal, Shipra, Navin Goyal +1 · 128 citations
   Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Machine Learning and Algorithms #Reinforcement Learning in Robotics
2. Analysis of Thompson Sampling for the multi-armed bandit problem
   2011/11/08 by Shipra Agrawal, Agrawal, Shipra, Navin Goyal +1 · 11 citations
   Computer Science · Decision Sciences · #68Q25 #68W40 #Advanced Bandit Algorithms Research #Data Structures and Algorithms (cs.DS) #F.2.0 #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Optimization and Search Problems
3. In-Context Learning through the Bayesian Prism
   2023/06/08 by Madhur Panwar, Panwar, Madhur, Kabir Ahuja +3 · 13 citations
   Computer Science · #Computation and Language (cs.CL) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Speech Recognition and Synthesis #Topic Modeling
4. On the Computational Power of Transformers and its Implications in\n Sequence Modeling
   2020/06/16 by Satwik Bhattamishra, Bhattamishra, Satwik, Arkil Patel +3 · 8 citations
   Computer Science · #Topic Modeling #Natural Language Processing Techniques #Neural Networks and Applications
5. A Theory of Emergent In-Context Learning as Implicit Structure Induction
   2023/03/14 by Michael G. Hahn, Navin Goyal, Hahn, Michael +1 · 6 citations
   Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language and cultural evolution #Machine Learning (cs.LG) #Speech and dialogue systems #Topic Modeling
6. The More, the Merrier: the Blessing of Dimensionality for Learning Large\n Gaussian Mixtures
   2013/11/12 by Joseph Anderson, Anderson, Joseph, Mikhail Belkin +9 · 4 citations
   Computer Science · #Bayesian Methods and Mixture Models #Blind Source Separation Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
7. Guiding Language Models of Code with Global Context using Monitors
   2023/06/19 by Lakshya A Agrawal, Aditya Kanade, Agrawal, Lakshya A +7 · 5 citations
   Computer Science · Decision Sciences · #Advanced Malware Detection Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.2 #I.2.5 #I.2.7 #Machine Learning (cs.LG) #Programming Languages (cs.PL) #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Engineering Research
8. On the Practical Ability of Recurrent Neural Networks to Recognize\n Hierarchical Languages
   2020/11/08 by Satwik Bhattamishra, Bhattamishra, Satwik, Kabir Ahuja +3 · 3 citations
   Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Software Engineering Research #Topic Modeling
9. Towards a Mathematics Formalisation Assistant using Large Language Models
   2022/11/14 by Ayush Agrawal, Agrawal, Ayush, Siddhartha Gadgil +7 · 2 citations
   Computer Science · #Mathematics, Computing, and Information Processing #Natural Language Processing Techniques #Semantic Web and Ontologies
10. Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature
    2019/07/24 by Navin Goyal, Goyal, Navin, Abhishek Shetty +1 · 1 citation
    Mathematics · Computer Science · #Point processes and geometric inequalities #Markov Chains and Monte Carlo Methods #Topological and Geometric Data Analysis
11. Non-Gaussianity of Stochastic Gradient Noise
    2019/10/21 by Abhishek Panigrahi, Panigrahi, Abhishek, Raghav Somani +5 · 1 citation
    Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stochastic Gradient Optimization Techniques
12. Effect of Activation Functions on the Training of Overparametrized\n Neural Nets
    2019/08/16 by Abhishek Panigrahi, Panigrahi, Abhishek, Abhishek Shetty +3 · 1 citation
    Computer Science · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications #Stochastic Gradient Optimization Techniques