Navin Goyal - RankMe

104 papers A* 25A 7B 5Journal 60Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Characterizing Deep Research: A Benchmark and Formal Definition. CoRR Abhinav Java, Ashmit Khandelwal, Sukruta Prakash Midigeshi, Aaron Halfaker, Amit Deshpande, Navin Goyal, Ankur Gupta, Nagarajan Natarajan, Amit Sharma
2025	J	`jnl`	CurLL: A Developmental Framework to Evaluate Continual Learning in Language Models. CoRR Pavan Kalyan, Shubhra Mishra, Satya Lokam, Navin Goyal
2025	J	`jnl`	Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers. Trans. Assoc. Comput. Linguistics Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
2025	J	`jnl`	Shape of Thought: When Distribution Matters More than Correctness in Reasoning Tasks. CoRR Abhranil Chandra, Ayush Agrawal, Arian Hosseini, Sebastian Fischmeister, Rishabh Agarwal, Navin Goyal, Aaron C. Courville
2024	J	`jnl`	Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model. CoRR Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal, Satya Lokam, Sunayana Sitaram
2024	A*	`conf`	In-Context Learning through the Bayesian Prism. ICLR Madhur Panwar, Kabir Ahuja, Navin Goyal
2024	A*	`conf`	InversionView: A General-Purpose Method for Reading Information from Neural Activations. NeurIPS Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn
2024	J	`jnl`	InversionView: A General-Purpose Method for Reading Information from Neural Activations. CoRR Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn
2024	J	`jnl`	Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically. CoRR Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
2023	J	`jnl`	A Theory of Emergent In-Context Learning as Implicit Structure Induction. CoRR Michael Hahn, Navin Goyal
2023	J	`jnl`	Guiding Language Models of Code with Global Context using Monitors. CoRR Lakshya A. Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani
2023	J	`jnl`	In-Context Learning through the Bayesian Prism. CoRR Kabir Ahuja, Madhur Panwar, Navin Goyal
2023	A*	`conf`	Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context. NeurIPS Lakshya A. Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani
2022	A	`conf`	Learning and Generalization in Overparameterized Normalizing Flows. AISTATS Kulin Shah, Amit Deshpande, Navin Goyal
2022	—	`conf`	Revisiting the Compositional Generalization Abilities of Neural Sequence Models. ACL (2) Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal
2022	J	`jnl`	Revisiting the Compositional Generalization Abilities of Neural Sequence Models. CoRR Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal
2022	A	`conf`	Robust identifiability in linear structural equation models of causal inference. UAI Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
2022	J	`jnl`	Towards a Mathematics Formalisation Assistant using Large Language Models. CoRR Ayush Agrawal, Siddhartha Gadgil, Navin Goyal, Ashvni Narayanan, Anand Tadipatri
2022	A*	`conf`	When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. EMNLP Ankur Sikarwar, Arkil Patel, Navin Goyal
2022	J	`jnl`	When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. CoRR Ankur Sikarwar, Arkil Patel, Navin Goyal
2021	J	`jnl`	Analyzing the Nuances of Transformers' Polynomial Simplification Abilities. CoRR Vishesh Agarwal, Somak Aditya, Navin Goyal
2021	—	`conf`	Are NLP Models really able to Solve Simple Math Word Problems? NAACL-HLT Arkil Patel, Satwik Bhattamishra, Navin Goyal
2021	J	`jnl`	Are NLP Models really able to Solve Simple Math Word Problems? CoRR Arkil Patel, Satwik Bhattamishra, Navin Goyal
2021	J	`jnl`	Learning and Generalization in Overparameterized Normalizing Flows. CoRR Kulin Shah, Amit Deshpande, Navin Goyal
2021	A*	`conf`	Learning and Generalization in RNNs. NeurIPS Abhishek Panigrahi, Navin Goyal
2021	J	`jnl`	Learning and Generalization in RNNs. CoRR Abhishek Panigrahi, Navin Goyal
2020	A*	`conf`	Effect of Activation Functions on the Training of Overparametrized Neural Nets. ICLR Abhishek Panigrahi, Abhishek Shetty, Navin Goyal
2020	—	`conf`	On the Ability and Limitations of Transformers to Recognize Formal Languages. EMNLP (1) Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
2020	J	`jnl`	On the Ability of Self-Attention Networks to Recognize Counter Languages. CoRR Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
2020	B	`conf`	On the Computational Power of Transformers and Its Implications in Sequence Modeling. CoNLL Satwik Bhattamishra, Arkil Patel, Navin Goyal
2020	J	`jnl`	On the Computational Power of Transformers and Its Implications in Sequence Modeling. CoRR Satwik Bhattamishra, Arkil Patel, Navin Goyal
2020	B	`conf`	On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. COLING Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
2020	J	`jnl`	On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. CoRR Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
2020	J	`jnl`	Robust Identifiability in Linear Structural Equation Models of Causal Inference. CoRR Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
2019	J	`jnl`	Better analysis of greedy binary search tree on decomposable sequences. Theor. Comput. Sci. Navin Goyal, Manoj Gupta
2019	J	`jnl`	Effect of Activation Functions on the Training of Overparametrized Neural Nets. CoRR Abhishek Panigrahi, Abhishek Shetty, Navin Goyal
2019	A*	`conf`	Non-Gaussian component analysis using entropy methods. STOC Navin Goyal, Abhishek Shetty
2019	J	`jnl`	Non-Gaussianity of Stochastic Gradient Noise. CoRR Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli
2019	A*	`conf`	Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. COLT Navin Goyal, Abhishek Shetty
2019	J	`jnl`	Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. CoRR Navin Goyal, Abhishek Shetty
2019	A	`conf`	Stability of Linear Structural Equation Models of Causal Inference. UAI Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
2019	J	`jnl`	Stability of Linear Structural Equation Models of Causal Inference. CoRR Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
2018	—	`conf`	Depth separation and weight-width trade-offs for sigmoidal neural networks. ICLR (Workshop) Amit Deshpande, Navin Goyal, Sushrut Karmalkar
2018	J	`jnl`	Non-Gaussian Component Analysis using Entropy Methods. CoRR Navin Goyal, Abhishek Shetty
2017	A*	`conf`	Heavy-Tailed Analogues of the Covariance Matrix for ICA. AAAI Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2017	J	`jnl`	Heavy-Tailed Analogues of the Covariance Matrix for ICA. CoRR Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2017	J	`jnl`	Near-Optimal Regret Bounds for Thompson Sampling. J. ACM Shipra Agrawal, Navin Goyal
2016	J	`jnl`	Better Analysis of GREEDY Binary Search Tree on Decomposable Sequences. CoRR Navin Goyal, Manoj Gupta
2016	A*	`conf`	Non-negative Matrix Factorization under Heavy Noise. ICML Chiranjib Bhattacharyya, Navin Goyal, Ravindran Kannan, Jagdeep Pani
2016	J	`jnl`	On Computing Maximal Independent Sets of Hypergraphs in Parallel. ACM Trans. Parallel Comput. Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan
2015	A*	`conf`	Heavy-Tailed Independent Component Analysis. FOCS Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2015	J	`jnl`	Heavy-tailed Independent Component Analysis. CoRR Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2015	J	`jnl`	Query Complexity of Sampling and Small Geometric Partitions. Comb. Probab. Comput. Navin Goyal, Luis Rademacher, Santosh S. Vempala
2014	A*	`conf`	Annotations for Sparse Data Streams. SODA Amit Chakrabarti, Graham Cormode, Navin Goyal, Justin Thaler
2014	J	`jnl`	Expanders via Random Spanning Trees. SIAM J. Comput. Alan M. Frieze, Navin Goyal, Luis Rademacher, Santosh S. Vempala
2014	A*	`conf`	Fourier PCA and robust tensor decomposition. STOC Navin Goyal, Santosh S. Vempala, Ying Xiao
2014	J	`jnl`	Lower Bounds for the Average and Smoothed Number of Pareto-Optima. Theory Comput. Tobias Brunsch, Navin Goyal, Luis Rademacher, Heiko Röglin
2014	J	`jnl`	On Computing Maximal Independent Sets of Hypergraphs in Parallel. CoRR Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan
2014	B	`conf`	On computing maximal independent sets of hypergraphs in parallel. SPAA Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan
2014	J	`jnl`	Query complexity of sampling and small geometric partitions. CoRR Navin Goyal, Luis Rademacher, Santosh S. Vempala
2014	A*	`conf`	The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. COLT Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss
2013	A*	`conf`	Ad impression forecasting for sponsored search. WWW Abhirup Nath, Shibnath Mukherjee, Prateek Jain, Navin Goyal, Srivatsan Laxman
2013	J	`jnl`	Annotations for Sparse Data Streams CoRR Amit Chakrabarti, Graham Cormode, Navin Goyal, Justin Thaler
2013	J	`jnl`	Deterministic Algorithms for the Lovász Local Lemma. SIAM J. Comput. Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
2013	J	`jnl`	Dynamic vs Oblivious Routing in Network Design. CoRR Navin Goyal, Neil Olver, F. Bruce Shepherd
2013	A*	`conf`	Efficient Learning of Simplices. COLT Joseph Anderson, Navin Goyal, Luis Rademacher
2013	J	`jnl`	Fourier PCA. CoRR Navin Goyal, Santosh S. Vempala, Ying Xiao
2013	A	`conf`	Further Optimal Regret Bounds for Thompson Sampling. AISTATS Shipra Agrawal, Navin Goyal
2013	J	`jnl`	The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. CoRR Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss
2013	J	`jnl`	The VPN Conjecture Is True. J. ACM Navin Goyal, Neil Olver, F. Bruce Shepherd
2013	—	`conf`	Thompson Sampling for Contextual Bandits with Linear Payoffs. ICML (3) Shipra Agrawal, Navin Goyal
2012	A*	`conf`	Analysis of Thompson Sampling for the Multi-armed Bandit Problem. COLT Shipra Agrawal, Navin Goyal
2012	J	`jnl`	Efficient learning of simplices CoRR Navin Goyal, Luis Rademacher
2012	J	`jnl`	Further Optimal Regret Bounds for Thompson Sampling CoRR Shipra Agrawal, Navin Goyal
2012	—	`conf`	Lower Bounds for the Average and Smoothed Number of Pareto Optima. FSTTCS Navin Goyal, Luis Rademacher
2012	J	`jnl`	Thompson Sampling for Contextual Bandits with Linear Payoffs CoRR Shipra Agrawal, Navin Goyal
2011	J	`jnl`	Analysis of Thompson Sampling for the multi-armed bandit problem CoRR Shipra Agrawal, Navin Goyal
2011	J	`jnl`	Dynamic vs. Oblivious Routing in Network Design. Algorithmica Navin Goyal, Neil Olver, F. Bruce Shepherd
2011	J	`jnl`	Lower Bounds for the Average and Smoothed Number of Pareto Optima CoRR Navin Goyal, Luis Rademacher
2011	J	`jnl`	On Dynamic Optimality for Binary Search Trees CoRR Navin Goyal, Manoj Gupta
2010	A*	`conf`	Deterministic Algorithms for the Lovász Local Lemma. SODA Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
2010	—	`conf`	Enhanced flux balance analysis to model metabolic networks. BCB Nishanth Ulhas Nair, Navin Goyal, Nagasuma R. Chandra
2010	J	`jnl`	Rounds vs. Queries Tradeoff in Noisy Computation. Theory Comput. Navin Goyal, Michael E. Saks
2010	J	`jnl`	Satisfiability Thresholds for k-CNF Formula with Bounded Variable Intersections CoRR Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
2009	J	`jnl`	Deterministic Algorithms for the Lovasz Local Lemma CoRR Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
2009	A	`conf`	Dynamic vs. Oblivious Routing in Network Design. ESA Navin Goyal, Neil Olver, F. Bruce Shepherd
2009	A*	`conf`	Expanders via random spanning trees. SODA Navin Goyal, Luis Rademacher, Santosh S. Vempala
2009	A*	`conf`	Learning Convex Bodies is Hard. COLT Luis Rademacher, Navin Goyal
2009	J	`jnl`	Learning convex bodies is hard CoRR Navin Goyal, Luis Rademacher
2008	A	`conf`	Disorder inequality: a combinatorial approach to nearest neighbor search. WSDM Navin Goyal, Yury Lifshits, Hinrich Schütze
2008	J	`jnl`	Expanders via Random Spanning Trees CoRR Navin Goyal, Luis Rademacher, Santosh S. Vempala
2008	J	`jnl`	Lower Bounds for the Noisy Broadcast Problem. SIAM J. Comput. Navin Goyal, Guy Kindler, Michael E. Saks
2008	A*	`conf`	The vpn conjecture is true. STOC Navin Goyal, Neil Olver, F. Bruce Shepherd
2007	J	`jnl`	An Algorithmic Approach to the Identification of Rigid Domains in Proteins. Algorithmica Vicky Choi, Navin Goyal
2006	B	`conf`	An Efficient Approximation Algorithm for Point Pattern Matching Under Noise. LATIN Vicky Choi, Navin Goyal
2006	A*	`conf`	Lower bounds for circuits with MOD_m gates. FOCS Arkadev Chattopadhyay, Navin Goyal, Pavel Pudlák, Denis Thérien
2006	J	`jnl`	The Graham-Knowlton Problem Revisited. Theory Comput. Syst. Navin Goyal, Sachin Lodha, S. Muthukrishnan
2005	J	`jnl`	A parallel search game. Random Struct. Algorithms Navin Goyal, Michael E. Saks
2005	J	`jnl`	An Efficient Approximation Algorithm for Point Pattern Matching Under Noise CoRR Vicky Choi, Navin Goyal
2005	A*	`conf`	Lower Bounds for the Noisy Broadcast Problem. FOCS Navin Goyal, Guy Kindler, Michael E. Saks
2005	A*	`conf`	Rounds vs queries trade-off in noisy computation. SODA Navin Goyal, Michael E. Saks
2004	B	`conf`	A Combinatorial Shape Matching Algorithm for Rigid Protein Docking. CPM Vicky Choi, Navin Goyal
2003	A*	`conf`	Optimal Bandwidth Reservation Schedule in Cellular Network. INFOCOM Samrat Ganguly, B. R. Badrinath, Navin Goyal
2003	A	`conf`	Optimal Separation of EROW and CROWPRAMs. CCC Navin Goyal, Michael E. Saks, Srinivasan Venkatesh