Navin Goyal

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 44

Top Topics

Structural Equation Models

Formal Languages

Causal Inference

Top Venues

Publications

Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically. CoRR (2024)
Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn
InversionView: A General-Purpose Method for Reading Information from Neural Activations. CoRR (2024)
Madhur Panwar, Kabir Ahuja, Navin Goyal
In-Context Learning through the Bayesian Prism. ICLR (2024)
Kabir Ahuja, Madhur Panwar, Navin Goyal
In-Context Learning through the Bayesian Prism. CoRR (2023)
Michael Hahn, Navin Goyal
A Theory of Emergent In-Context Learning as Implicit Structure Induction. CoRR (2023)
Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani
Guiding Language Models of Code with Global Context using Monitors. CoRR (2023)
Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani
Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context. NeurIPS (2023)
Ayush Agrawal, Siddhartha Gadgil, Navin Goyal, Ashvni Narayanan, Anand Tadipatri
Towards a Mathematics Formalisation Assistant using Large Language Models. CoRR (2022)
Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
Robust identifiability in linear structural equation models of causal inference. UAI (2022)
Ankur Sikarwar, Arkil Patel, Navin Goyal
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. CoRR (2022)
Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal
Revisiting the Compositional Generalization Abilities of Neural Sequence Models. ACL (2) (2022)
Ankur Sikarwar, Arkil Patel, Navin Goyal
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. EMNLP (2022)
Kulin Shah, Amit Deshpande, Navin Goyal
Learning and Generalization in Overparameterized Normalizing Flows. AISTATS (2022)
Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal
Revisiting the Compositional Generalization Abilities of Neural Sequence Models. CoRR (2022)
Abhishek Panigrahi, Navin Goyal
Learning and Generalization in RNNs. NeurIPS (2021)
Arkil Patel, Satwik Bhattamishra, Navin Goyal
Are NLP Models really able to Solve Simple Math Word Problems? NAACL-HLT (2021)
Arkil Patel, Satwik Bhattamishra, Navin Goyal
Are NLP Models really able to Solve Simple Math Word Problems? CoRR (2021)
Vishesh Agarwal, Somak Aditya, Navin Goyal
Analyzing the Nuances of Transformers' Polynomial Simplification Abilities. CoRR (2021)
Abhishek Panigrahi, Navin Goyal
Learning and Generalization in RNNs. CoRR (2021)
Kulin Shah, Amit Deshpande, Navin Goyal
Learning and Generalization in Overparameterized Normalizing Flows. CoRR (2021)
Abhishek Panigrahi, Abhishek Shetty, Navin Goyal
Effect of Activation Functions on the Training of Overparametrized Neural Nets. ICLR (2020)
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. CoRR (2020)
Satwik Bhattamishra, Arkil Patel, Navin Goyal
On the Computational Power of Transformers and Its Implications in Sequence Modeling. CoNLL (2020)
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
On the Ability and Limitations of Transformers to Recognize Formal Languages. EMNLP (1) (2020)
Satwik Bhattamishra, Arkil Patel, Navin Goyal
On the Computational Power of Transformers and Its Implications in Sequence Modeling. CoRR (2020)
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
On the Ability of Self-Attention Networks to Recognize Counter Languages. CoRR (2020)
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. COLING (2020)
Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
Robust Identifiability in Linear Structural Equation Models of Causal Inference. CoRR (2020)
Navin Goyal, Abhishek Shetty
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. CoRR (2019)
Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
Stability of Linear Structural Equation Models of Causal Inference. CoRR (2019)
Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli
Non-Gaussianity of Stochastic Gradient Noise. CoRR (2019)
Abhishek Panigrahi, Abhishek Shetty, Navin Goyal
Effect of Activation Functions on the Training of Overparametrized Neural Nets. CoRR (2019)
Navin Goyal, Abhishek Shetty
Non-Gaussian component analysis using entropy methods. STOC (2019)
Navin Goyal, Manoj Gupta
Better analysis of greedy binary search tree on decomposable sequences. Theor. Comput. Sci. 776 (2019)
Navin Goyal, Abhishek Shetty
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. COLT (2019)
Karthik Abinav Sankararaman, Anand Louis, Navin Goyal
Stability of Linear Structural Equation Models of Causal Inference. UAI (2019)
Navin Goyal, Abhishek Shetty
Non-Gaussian Component Analysis using Entropy Methods. CoRR (2018)
Amit Deshpande, Navin Goyal, Sushrut Karmalkar
Depth separation and weight-width trade-offs for sigmoidal neural networks. ICLR (Workshop) (2018)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Analogues of the Covariance Matrix for ICA. CoRR (2017)
Shipra Agrawal, Navin Goyal
Near-Optimal Regret Bounds for Thompson Sampling. J. ACM 64 (5) (2017)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Analogues of the Covariance Matrix for ICA. AAAI (2017)
Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan
On Computing Maximal Independent Sets of Hypergraphs in Parallel. ACM Trans. Parallel Comput. 3 (1) (2016)
Chiranjib Bhattacharyya, Navin Goyal, Ravindran Kannan, Jagdeep Pani
Non-negative Matrix Factorization under Heavy Noise. ICML (2016)
Navin Goyal, Manoj Gupta
Better Analysis of GREEDY Binary Search Tree on Decomposable Sequences. CoRR (2016)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-tailed Independent Component Analysis. CoRR (2015)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Independent Component Analysis. FOCS (2015)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Query Complexity of Sampling and Small Geometric Partitions. Comb. Probab. Comput. 24 (5) (2015)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Query complexity of sampling and small geometric partitions. CoRR (2014)
Amit Chakrabarti, Graham Cormode, Navin Goyal, Justin Thaler
Annotations for Sparse Data Streams. SODA (2014)
Alan M. Frieze, Navin Goyal, Luis Rademacher, Santosh S. Vempala
Expanders via Random Spanning Trees. SIAM J. Comput. 43 (2) (2014)
Navin Goyal, Santosh S. Vempala, Ying Xiao
Fourier PCA and robust tensor decomposition. STOC (2014)
Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan
On computing maximal independent sets of hypergraphs in parallel. SPAA (2014)
Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. COLT (2014)
Tobias Brunsch, Navin Goyal, Luis Rademacher, Heiko Röglin
Lower Bounds for the Average and Smoothed Number of Pareto-Optima. Theory Comput. 10 (2014)
Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan
On Computing Maximal Independent Sets of Hypergraphs in Parallel. CoRR (2014)
Joseph Anderson, Navin Goyal, Luis Rademacher
Efficient Learning of Simplices. COLT (2013)
Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. CoRR (2013)
Shipra Agrawal, Navin Goyal
Thompson Sampling for Contextual Bandits with Linear Payoffs. ICML (3) (2013)
Navin Goyal, Santosh S. Vempala, Ying Xiao
Fourier PCA. CoRR (2013)
Shipra Agrawal, Navin Goyal
Further Optimal Regret Bounds for Thompson Sampling. AISTATS (2013)
Abhirup Nath, Shibnath Mukherjee, Prateek Jain, Navin Goyal, Srivatsan Laxman
Ad impression forecasting for sponsored search. WWW (2013)
Navin Goyal, Neil Olver, F. Bruce Shepherd
Dynamic vs Oblivious Routing in Network Design. CoRR (2013)
Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
Deterministic Algorithms for the Lovász Local Lemma. SIAM J. Comput. 42 (6) (2013)
Amit Chakrabarti, Graham Cormode, Navin Goyal, Justin Thaler
Annotations for Sparse Data Streams CoRR (2013)
Navin Goyal, Neil Olver, F. Bruce Shepherd
The VPN Conjecture Is True. J. ACM 60 (3) (2013)
Shipra Agrawal, Navin Goyal
Further Optimal Regret Bounds for Thompson Sampling CoRR (2012)
Shipra Agrawal, Navin Goyal
Thompson Sampling for Contextual Bandits with Linear Payoffs CoRR (2012)
Navin Goyal, Luis Rademacher
Efficient learning of simplices CoRR (2012)
Shipra Agrawal, Navin Goyal
Analysis of Thompson Sampling for the Multi-armed Bandit Problem. COLT (2012)
Navin Goyal, Luis Rademacher
Lower Bounds for the Average and Smoothed Number of Pareto Optima. FSTTCS (2012)
Shipra Agrawal, Navin Goyal
Analysis of Thompson Sampling for the multi-armed bandit problem CoRR (2011)
Navin Goyal, Manoj Gupta
On Dynamic Optimality for Binary Search Trees CoRR (2011)
Navin Goyal, Luis Rademacher
Lower Bounds for the Average and Smoothed Number of Pareto Optima CoRR (2011)
Navin Goyal, Neil Olver, F. Bruce Shepherd
Dynamic vs. Oblivious Routing in Network Design. Algorithmica 61 (1) (2011)
Nishanth Ulhas Nair, Navin Goyal, Nagasuma R. Chandra
Enhanced flux balance analysis to model metabolic networks. BCB (2010)
Navin Goyal, Michael E. Saks
Rounds vs. Queries Tradeoff in Noisy Computation. Theory Comput. 6 (1) (2010)
Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
Satisfiability Thresholds for k-CNF Formula with Bounded Variable Intersections CoRR (2010)
Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
Deterministic Algorithms for the Lovász Local Lemma. SODA (2010)
Navin Goyal, Neil Olver, F. Bruce Shepherd
Dynamic vs. Oblivious Routing in Network Design. ESA (2009)
Navin Goyal, Luis Rademacher
Learning convex bodies is hard CoRR (2009)
Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
Deterministic Algorithms for the Lovasz Local Lemma CoRR (2009)
Luis Rademacher, Navin Goyal
Learning Convex Bodies is Hard. COLT (2009)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Expanders via random spanning trees. SODA (2009)
Navin Goyal, Yury Lifshits, Hinrich Schütze
Disorder inequality: a combinatorial approach to nearest neighbor search. WSDM (2008)
Navin Goyal, Neil Olver, F. Bruce Shepherd
The vpn conjecture is true. STOC (2008)
Navin Goyal, Guy Kindler, Michael E. Saks
Lower Bounds for the Noisy Broadcast Problem. SIAM J. Comput. 37 (6) (2008)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Expanders via Random Spanning Trees CoRR (2008)
Vicky Choi, Navin Goyal
An Algorithmic Approach to the Identification of Rigid Domains in Proteins. Algorithmica 48 (4) (2007)
Navin Goyal, Sachin Lodha, S. Muthukrishnan
The Graham-Knowlton Problem Revisited. Theory Comput. Syst. 39 (3) (2006)
Arkadev Chattopadhyay, Navin Goyal, Pavel Pudlák, Denis Thérien
Lower bounds for circuits with MOD_m gates. FOCS (2006)
Vicky Choi, Navin Goyal
An Efficient Approximation Algorithm for Point Pattern Matching Under Noise. LATIN (2006)
Navin Goyal, Michael E. Saks
Rounds vs queries trade-off in noisy computation. SODA (2005)
Navin Goyal, Michael E. Saks
A parallel search game. Random Struct. Algorithms 27 (2) (2005)
Navin Goyal, Guy Kindler, Michael E. Saks
Lower Bounds for the Noisy Broadcast Problem. FOCS (2005)
Vicky Choi, Navin Goyal
An Efficient Approximation Algorithm for Point Pattern Matching Under Noise CoRR (2005)
Vicky Choi, Navin Goyal
A Combinatorial Shape Matching Algorithm for Rigid Protein Docking. CPM (2004)
Navin Goyal, Michael E. Saks, Srinivasan Venkatesh
Optimal Separation of EROW and CROWPRAMs. Computational Complexity Conference (2003)
Samrat Ganguly, B. R. Badrinath, Navin Goyal
Optimal Bandwidth Reservation Schedule in Cellular Network. INFOCOM (2003)