Goyal, Navin

26 publications

NeurIPS 2025 For Better or for Worse, Transformers Seek Patterns for Memorization Madhur Panwar, Gail Weiss, Navin Goyal, Antoine Bosselut

NeurIPSW 2024 Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal, Satya Lokam, Sunayana Sitaram

NeurIPSW 2024 Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal, Satya Lokam, Sunayana Sitaram

NeurIPSW 2024 How Do Active Dendrite Networks Mitigate Catastrophic Forgetting? Sankarshan Damle, Satya Lokam, Navin Goyal

ICLR 2024 In-Context Learning Through the Bayesian Prism Madhur Panwar, Kabir Ahuja, Navin Goyal

NeurIPS 2024 InversionView: A General-Purpose Method for Reading Information from Neural Activations Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn

ICMLW 2024 InversionView: A General-Purpose Method for Reading Information from Neural Activations Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn

ICMLW 2024 Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov

NeurIPSW 2023 In-Context Learning and Bayesian Inference Madhur Panwar, Kabir Ahuja, Navin Goyal

NeurIPS 2023 Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu Lahiri, Sriram Rajamani

NeurIPSW 2023 Surprising Deviations from Bayesian View in In-Context Learning Madhur Panwar, Kabir Ahuja, Navin Goyal

NeurIPSW 2023 Transformers Can Learn to Solve Linear-Inverse Problems In-Context Kabir Ahuja, Madhur Panwar, Navin Goyal

AISTATS 2022 Learning and Generalization in Overparameterized Normalizing Flows Kulin Shah, Amit Deshpande, Navin Goyal

UAI 2022 Robust Identifiability in Linear Structural Equation Models of Causal Inference Karthik A. Sankararaman, Anand Louis, Navin Goyal

NeurIPS 2021 Learning and Generalization in RNNs Abhishek Panigrahi, Navin Goyal

ICLR 2020 Effect of Activation Functions on the Training of Overparametrized Neural Nets Abhishek Panigrahi, Abhishek Shetty, Navin Goyal

COLT 2019 Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature Navin Goyal, Abhishek Shetty

UAI 2019 Stability of Linear Structural Equation Models of Causal Inference Karthik Abhinav Sankararaman, Anand Louis, Navin Goyal

AAAI 2017 Heavy-Tailed Analogues of the Covariance Matrix for ICA Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher

ICML 2016 Non-Negative Matrix Factorization Under Heavy Noise Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani

COLT 2014 The More, the Merrier: The Blessing of Dimensionality for Learning Large Gaussian Mixtures Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss

COLT 2013 Efficient Learning of Simplices Joseph Anderson, Navin Goyal, Luis Rademacher

AISTATS 2013 Further Optimal Regret Bounds for Thompson Sampling Shipra Agrawal, Navin Goyal

ICML 2013 Thompson Sampling for Contextual Bandits with Linear Payoffs Shipra Agrawal, Navin Goyal

COLT 2012 Analysis of Thompson Sampling for the Multi-Armed Bandit Problem Shipra Agrawal, Navin Goyal

COLT 2009 Learning Convex Bodies Is Hard Luis Rademacher, Navin Goyal