Optimal Separation of EROW and CROWPRAMs. - Navin Goyal; Michael E. Saks; Srinivasan Venkatesh 001 (CCC, 2003)

Optimal Bandwidth Reservation Schedule in Cellular Network. - Samrat Ganguly; B. R. Badrinath; Navin Goyal (INFOCOM, 2003)

A Combinatorial Shape Matching Algorithm for Rigid Protein Docking. - Vicky Choi; Navin Goyal (CPM, 2004)

Rounds vs queries trade-off in noisy computation. - Navin Goyal; Michael E. Saks (SODA, 2005)

A parallel search game. - Navin Goyal; Michael E. Saks (Random Struct. Algorithms, 2005)

Lower bounds for circuits with MOD_m gates. - Arkadev Chattopadhyay; Navin Goyal; Pavel Pudlák; Denis Thérien (FOCS, 2006)

An Efficient Approximation Algorithm for Point Pattern Matching Under Noise. - Vicky Choi; Navin Goyal (LATIN, 2006)

The Graham-Knowlton Problem Revisited. - Navin Goyal; Sachin Lodha; S. Muthukrishnan 001 (Theory Comput. Syst., 2006)

An Algorithmic Approach to the Identification of Rigid Domains in Proteins. - Vicky Choi; Navin Goyal (Algorithmica, 2007)

Disorder inequality: a combinatorial approach to nearest neighbor search. - Navin Goyal; Yury Lifshits; Hinrich Schütze (WSDM, 2008)

Lower Bounds for the Noisy Broadcast Problem. - Navin Goyal; Guy Kindler; Michael E. Saks (SIAM J. Comput., 2008)

Learning Convex Bodies is Hard. - Luis Rademacher; Navin Goyal (COLT, 2009)

Expanders via random spanning trees. - Navin Goyal; Luis Rademacher; Santosh S. Vempala (SODA, 2009)

Enhanced flux balance analysis to model metabolic networks. - Nishanth Ulhas Nair; Navin Goyal; Nagasuma R. Chandra (BCB, 2010)

Satisfiability Thresholds for k-CNF Formula with Bounded Variable Intersections - Karthekeyan Chandrasekaran; Navin Goyal; Bernhard Haeupler (CoRR, 2010)

Rounds vs. Queries Tradeoff in Noisy Computation. - Navin Goyal; Michael E. Saks (Theory Comput., 2010)

Dynamic vs. Oblivious Routing in Network Design. - Navin Goyal; Neil Olver; F. Bruce Shepherd (Algorithmica, 2011)

On Dynamic Optimality for Binary Search Trees - Navin Goyal; Manoj Gupta 002 (CoRR, 2011)

Lower Bounds for the Average and Smoothed Number of Pareto Optima. - Navin Goyal; Luis Rademacher (FSTTCS, 2012)

Efficient learning of simplices - Navin Goyal; Luis Rademacher (CoRR, 2012)

Analysis of Thompson Sampling for the Multi-armed Bandit Problem. - Shipra Agrawal 001; Navin Goyal (COLT, 2012)

Further Optimal Regret Bounds for Thompson Sampling. - Shipra Agrawal 001; Navin Goyal (AISTATS, 2013)

Efficient Learning of Simplices. - Joseph Anderson; Navin Goyal; Luis Rademacher (COLT, 2013)

Thompson Sampling for Contextual Bandits with Linear Payoffs. - Shipra Agrawal 001; Navin Goyal (ICML (3), 2013)

Ad impression forecasting for sponsored search. - Abhirup Nath; Shibnath Mukherjee; Prateek Jain 002; Navin Goyal; Srivatsan Laxman (WWW, 2013)

Fourier PCA. - Navin Goyal; Santosh S. Vempala; Ying Xiao 003 (CoRR, 2013)

The VPN Conjecture Is True. - Navin Goyal; Neil Olver; F. Bruce Shepherd (J. ACM, 2013)

Deterministic Algorithms for the Lovász Local Lemma. - Karthekeyan Chandrasekaran; Navin Goyal; Bernhard Haeupler (SIAM J. Comput., 2013)

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, 2014)

Annotations for Sparse Data Streams. - Amit Chakrabarti; Graham Cormode; Navin Goyal; Justin Thaler (SODA, 2014)

Fourier PCA and robust tensor decomposition. - Navin Goyal; Santosh S. Vempala; Ying Xiao 003 (STOC, 2014)

Expanders via Random Spanning Trees. - Alan M. Frieze; Navin Goyal; Luis Rademacher; Santosh S. Vempala (SIAM J. Comput., 2014)

Lower Bounds for the Average and Smoothed Number of Pareto-Optima. - Tobias Brunsch; Navin Goyal; Luis Rademacher; Heiko Röglin (Theory Comput., 2014)

Heavy-Tailed Independent Component Analysis. - Joseph Anderson; Navin Goyal; Anupama Nandi; Luis Rademacher (FOCS, 2015)

Query Complexity of Sampling and Small Geometric Partitions. - Navin Goyal; Luis Rademacher; Santosh S. Vempala (Comb. Probab. Comput., 2015)

Non-negative Matrix Factorization under Heavy Noise. - Chiranjib Bhattacharyya; Navin Goyal; Ravindran Kannan; Jagdeep Pani (ICML, 2016)

On Computing Maximal Independent Sets of Hypergraphs in Parallel. - Ioana Oriana Bercea; Navin Goyal; David G. Harris 001; Aravind Srinivasan (ACM Trans. Parallel Comput., 2016)

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

Near-Optimal Regret Bounds for Thompson Sampling. - Shipra Agrawal 001; Navin Goyal (J. ACM, 2017)

Depth separation and weight-width trade-offs for sigmoidal neural networks. - Amit Deshpande 001; Navin Goyal; Sushrut Karmalkar (ICLR (Workshop), 2018)

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

Non-Gaussian component analysis using entropy methods. - Navin Goyal; Abhishek Shetty (STOC, 2019)

Stability of Linear Structural Equation Models of Causal Inference. - Karthik Abinav Sankararaman; Anand Louis; Navin Goyal (UAI, 2019)

Non-Gaussianity of Stochastic Gradient Noise. - Abhishek Panigrahi; Raghav Somani; Navin Goyal; Praneeth Netrapalli (CoRR, 2019)

Better analysis of greedy binary search tree on decomposable sequences. - Navin Goyal; Manoj Gupta 002 (Theor. Comput. Sci., 2019)

On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. - Satwik Bhattamishra; Kabir Ahuja; Navin Goyal (COLING, 2020)

On the Computational Power of Transformers and Its Implications in Sequence Modeling. - Satwik Bhattamishra; Arkil Patel; Navin Goyal (CoNLL, 2020)

On the Ability and Limitations of Transformers to Recognize Formal Languages. - Satwik Bhattamishra; Kabir Ahuja; Navin Goyal (EMNLP (1), 2020)

Effect of Activation Functions on the Training of Overparametrized Neural Nets. - Abhishek Panigrahi; Abhishek Shetty; Navin Goyal (ICLR, 2020)

On the Ability of Self-Attention Networks to Recognize Counter Languages. - Satwik Bhattamishra; Kabir Ahuja; Navin Goyal (CoRR, 2020)

Are NLP Models really able to Solve Simple Math Word Problems? - Arkil Patel; Satwik Bhattamishra; Navin Goyal (NAACL-HLT, 2021)

Learning and Generalization in RNNs. - Abhishek Panigrahi; Navin Goyal (NeurIPS, 2021)

Analyzing the Nuances of Transformers' Polynomial Simplification Abilities. - Vishesh Agarwal; Somak Aditya; Navin Goyal (CoRR, 2021)

Revisiting the Compositional Generalization Abilities of Neural Sequence Models. - Arkil Patel; Satwik Bhattamishra; Phil Blunsom; Navin Goyal (ACL (2), 2022)

Learning and Generalization in Overparameterized Normalizing Flows. - Kulin Shah; Amit Deshpande 001; Navin Goyal (AISTATS, 2022)

When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. - Ankur Sikarwar; Arkil Patel; Navin Goyal (EMNLP, 2022)

Robust identifiability in linear structural equation models of causal inference. - Karthik Abinav Sankararaman; Anand Louis; Navin Goyal (UAI, 2022)

Towards a Mathematics Formalisation Assistant using Large Language Models. - Ayush Agrawal; Siddhartha Gadgil; Navin Goyal; Ashvni Narayanan; Anand Tadipatri (CoRR, 2022)

Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context. - Lakshya A. Agrawal; Aditya Kanade 001; Navin Goyal; Shuvendu K. Lahiri; Sriram K. Rajamani (NeurIPS, 2023)

A Theory of Emergent In-Context Learning as Implicit Structure Induction. - Michael Hahn 001; Navin Goyal (CoRR, 2023)

Guiding Language Models of Code with Global Context using Monitors. - Lakshya A. Agrawal; Aditya Kanade 001; Navin Goyal; Shuvendu K. Lahiri; Sriram K. Rajamani (CoRR, 2023)

In-Context Learning through the Bayesian Prism. - Madhur Panwar; Kabir Ahuja; Navin Goyal (ICLR, 2024)

InversionView: A General-Purpose Method for Reading Information from Neural Activations. - Xinting Huang; Madhur Panwar; Navin Goyal; Michael Hahn 001 (NeurIPS, 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 (CoRR, 2024)

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

Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers. - Kabir Ahuja; Vidhisha Balachandran; Madhur Panwar; Tianxing He; Noah A. Smith; Navin Goyal; Yulia Tsvetkov (Trans. Assoc. Comput. Linguistics, 2025)

Characterizing Deep Research: A Benchmark and Formal Definition. - Abhinav Java; Ashmit Khandelwal; Sukruta Prakash Midigeshi; Aaron Halfaker; Amit Deshpande; Navin Goyal; Ankur Gupta; Nagarajan Natarajan; Amit Sharma (CoRR, 2025)