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Navin Goyal
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2020 – today
- 2024
- [c43]Madhur Panwar, Kabir Ahuja, Navin Goyal:
In-Context Learning through the Bayesian Prism. ICLR 2024 - [i41]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 abs/2404.16367 (2024) - [i40]Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn:
InversionView: A General-Purpose Method for Reading Information from Neural Activations. CoRR abs/2405.17653 (2024) - 2023
- [c42]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 - [i39]Michael Hahn, Navin Goyal:
A Theory of Emergent In-Context Learning as Implicit Structure Induction. CoRR abs/2303.07971 (2023) - [i38]Kabir Ahuja, Madhur Panwar, Navin Goyal:
In-Context Learning through the Bayesian Prism. CoRR abs/2306.04891 (2023) - [i37]Lakshya A. Agrawal
, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani:
Guiding Language Models of Code with Global Context using Monitors. CoRR abs/2306.10763 (2023) - 2022
- [c41]Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal:
Revisiting the Compositional Generalization Abilities of Neural Sequence Models. ACL (2) 2022: 424-434 - [c40]Kulin Shah, Amit Deshpande, Navin Goyal:
Learning and Generalization in Overparameterized Normalizing Flows. AISTATS 2022: 9430-9504 - [c39]Ankur Sikarwar
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. EMNLP 2022: 648-669 - [c38]Karthik Abinav Sankararaman, Anand Louis, Navin Goyal:
Robust identifiability in linear structural equation models of causal inference. UAI 2022: 1728-1737 - [i36]Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal:
Revisiting the Compositional Generalization Abilities of Neural Sequence Models. CoRR abs/2203.07402 (2022) - [i35]Ankur Sikarwar, Arkil Patel, Navin Goyal:
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. CoRR abs/2210.12786 (2022) - [i34]Ayush Agrawal, Siddhartha Gadgil, Navin Goyal, Ashvni Narayanan, Anand Tadipatri:
Towards a Mathematics Formalisation Assistant using Large Language Models. CoRR abs/2211.07524 (2022) - 2021
- [c37]Arkil Patel, Satwik Bhattamishra, Navin Goyal:
Are NLP Models really able to Solve Simple Math Word Problems? NAACL-HLT 2021: 2080-2094 - [c36]Abhishek Panigrahi, Navin Goyal:
Learning and Generalization in RNNs. NeurIPS 2021: 21112-21124 - [i33]Arkil Patel, Satwik Bhattamishra, Navin Goyal:
Are NLP Models really able to Solve Simple Math Word Problems? CoRR abs/2103.07191 (2021) - [i32]Vishesh Agarwal, Somak Aditya, Navin Goyal:
Analyzing the Nuances of Transformers' Polynomial Simplification Abilities. CoRR abs/2104.14095 (2021) - [i31]Abhishek Panigrahi, Navin Goyal:
Learning and Generalization in RNNs. CoRR abs/2106.00047 (2021) - [i30]Kulin Shah, Amit Deshpande, Navin Goyal:
Learning and Generalization in Overparameterized Normalizing Flows. CoRR abs/2106.10535 (2021) - 2020
- [c35]Satwik Bhattamishra, Kabir Ahuja, Navin Goyal:
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. COLING 2020: 1481-1494 - [c34]Satwik Bhattamishra, Arkil Patel, Navin Goyal:
On the Computational Power of Transformers and Its Implications in Sequence Modeling. CoNLL 2020: 455-475 - [c33]Satwik Bhattamishra, Kabir Ahuja, Navin Goyal:
On the Ability and Limitations of Transformers to Recognize Formal Languages. EMNLP (1) 2020: 7096-7116 - [c32]Abhishek Panigrahi, Abhishek Shetty, Navin Goyal:
Effect of Activation Functions on the Training of Overparametrized Neural Nets. ICLR 2020 - [i29]Satwik Bhattamishra, Arkil Patel, Navin Goyal:
On the Computational Power of Transformers and Its Implications in Sequence Modeling. CoRR abs/2006.09286 (2020) - [i28]Karthik Abinav Sankararaman, Anand Louis, Navin Goyal:
Robust Identifiability in Linear Structural Equation Models of Causal Inference. CoRR abs/2007.06869 (2020) - [i27]Satwik Bhattamishra, Kabir Ahuja, Navin Goyal:
On the Ability of Self-Attention Networks to Recognize Counter Languages. CoRR abs/2009.11264 (2020) - [i26]Satwik Bhattamishra, Kabir Ahuja, Navin Goyal:
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages. CoRR abs/2011.03965 (2020)
2010 – 2019
- 2019
- [j14]Navin Goyal, Manoj Gupta
Better analysis of greedy binary search tree on decomposable sequences. Theor. Comput. Sci. 776: 19-42 (2019) - [c31]Navin Goyal, Abhishek Shetty:
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. COLT 2019: 1519-1561 - [c30]Navin Goyal, Abhishek Shetty:
Non-Gaussian component analysis using entropy methods. STOC 2019: 840-851 - [c29]Karthik Abinav Sankararaman, Anand Louis, Navin Goyal:
Stability of Linear Structural Equation Models of Causal Inference. UAI 2019: 323-333 - [i25]Karthik Abinav Sankararaman, Anand Louis, Navin Goyal:
Stability of Linear Structural Equation Models of Causal Inference. CoRR abs/1905.06836 (2019) - [i24]Navin Goyal, Abhishek Shetty:
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature. CoRR abs/1907.10531 (2019) - [i23]Abhishek Panigrahi, Abhishek Shetty, Navin Goyal:
Effect of Activation Functions on the Training of Overparametrized Neural Nets. CoRR abs/1908.05660 (2019) - [i22]Abhishek Panigrahi, Raghav Somani
Non-Gaussianity of Stochastic Gradient Noise. CoRR abs/1910.09626 (2019) - 2018
- [c28]Amit Deshpande, Navin Goyal, Sushrut Karmalkar:
Depth separation and weight-width trade-offs for sigmoidal neural networks. ICLR (Workshop) 2018 - [i21]Navin Goyal, Abhishek Shetty:
Non-Gaussian Component Analysis using Entropy Methods. CoRR abs/1807.04936 (2018) - 2017
- [j13]Shipra Agrawal, Navin Goyal:
Near-Optimal Regret Bounds for Thompson Sampling. J. ACM 64(5): 30:1-30:24 (2017) - [c27]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Analogues of the Covariance Matrix for ICA. AAAI 2017: 1712-1718 - [i20]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher:
Heavy-Tailed Analogues of the Covariance Matrix for ICA. CoRR abs/1702.06976 (2017) - 2016
- [j12]Ioana Oriana Bercea
On Computing Maximal Independent Sets of Hypergraphs in Parallel. ACM Trans. Parallel Comput. 3(1): 5:1-5:13 (2016) - [c26]Chiranjib Bhattacharyya, Navin Goyal, Ravindran Kannan, Jagdeep Pani:
Non-negative Matrix Factorization under Heavy Noise. ICML 2016: 1426-1434 - [i19]Navin Goyal, Manoj Gupta:
Better Analysis of GREEDY Binary Search Tree on Decomposable Sequences. CoRR abs/1604.06997 (2016) - 2015
- [j11]Navin Goyal, Luis Rademacher
Query Complexity of Sampling and Small Geometric Partitions. Comb. Probab. Comput. 24(5): 733-753 (2015) - [c25]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Independent Component Analysis. FOCS 2015: 290-309 - [i18]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher:
Heavy-tailed Independent Component Analysis. CoRR abs/1509.00727 (2015) - 2014
- [j10]Alan M. Frieze
Expanders via Random Spanning Trees. SIAM J. Comput. 43(2): 497-513 (2014) - [j9]Tobias Brunsch, Navin Goyal, Luis Rademacher
Lower Bounds for the Average and Smoothed Number of Pareto-Optima. Theory Comput. 10: 237-256 (2014) - [c24]Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. COLT 2014: 1135-1164 - [c23]Amit Chakrabarti, Graham Cormode
Annotations for Sparse Data Streams. SODA 2014: 687-706 - [c22]Ioana Oriana Bercea
On computing maximal independent sets of hypergraphs in parallel. SPAA 2014: 42-50 - [c21]Navin Goyal, Santosh S. Vempala, Ying Xiao:
Fourier PCA and robust tensor decomposition. STOC 2014: 584-593 - [i17]Ioana Oriana Bercea, Navin Goyal, David G. Harris, Aravind Srinivasan:
On Computing Maximal Independent Sets of Hypergraphs in Parallel. CoRR abs/1405.1133 (2014) - [i16]Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Query complexity of sampling and small geometric partitions. CoRR abs/1411.3799 (2014) - 2013
- [j8]Navin Goyal, Neil Olver
The VPN Conjecture Is True. J. ACM 60(3): 17:1-17:17 (2013) - [j7]Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
Deterministic Algorithms for the Lovász Local Lemma. SIAM J. Comput. 42(6): 2132-2155 (2013) - [c20]Shipra Agrawal, Navin Goyal:
Further Optimal Regret Bounds for Thompson Sampling. AISTATS 2013: 99-107 - [c19]Joseph Anderson, Navin Goyal, Luis Rademacher
Efficient Learning of Simplices. COLT 2013: 1020-1045 - [c18]Shipra Agrawal, Navin Goyal:
Thompson Sampling for Contextual Bandits with Linear Payoffs. ICML (3) 2013: 127-135 - [c17]Abhirup Nath, Shibnath Mukherjee, Prateek Jain, Navin Goyal, Srivatsan Laxman:
Ad impression forecasting for sponsored search. WWW 2013: 943-952 - [i15]Amit Chakrabarti, Graham Cormode, Navin Goyal, Justin Thaler:
Annotations for Sparse Data Streams. CoRR abs/1304.3816 (2013) - [i14]Navin Goyal, Santosh S. Vempala, Ying Xiao:
Fourier PCA. CoRR abs/1306.5825 (2013) - [i13]Navin Goyal, Neil Olver, F. Bruce Shepherd:
Dynamic vs Oblivious Routing in Network Design. CoRR abs/1309.4140 (2013) - [i12]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 abs/1311.2891 (2013) - 2012
- [c16]Navin Goyal, Luis Rademacher
Lower Bounds for the Average and Smoothed Number of Pareto Optima. FSTTCS 2012: 58-69 - [c15]Shipra Agrawal, Navin Goyal:
Analysis of Thompson Sampling for the Multi-armed Bandit Problem. COLT 2012: 39.1-39.26 - [i11]Shipra Agrawal, Navin Goyal:
Thompson Sampling for Contextual Bandits with Linear Payoffs. CoRR abs/1209.3352 (2012) - [i10]Shipra Agrawal, Navin Goyal:
Further Optimal Regret Bounds for Thompson Sampling. CoRR abs/1209.3353 (2012) - [i9]Navin Goyal, Luis Rademacher:
Efficient learning of simplices. CoRR abs/1211.2227 (2012) - 2011
- [j6]Navin Goyal, Neil Olver
Dynamic vs. Oblivious Routing in Network Design. Algorithmica 61(1): 161-173 (2011) - [i8]Navin Goyal, Manoj Gupta:
On Dynamic Optimality for Binary Search Trees. CoRR abs/1102.4523 (2011) - [i7]Navin Goyal, Luis Rademacher:
Lower Bounds for the Average and Smoothed Number of Pareto Optima. CoRR abs/1107.3876 (2011) - [i6]Shipra Agrawal, Navin Goyal:
Analysis of Thompson Sampling for the multi-armed bandit problem. CoRR abs/1111.1797 (2011) - 2010
- [j5]Navin Goyal, Michael E. Saks:
Rounds vs. Queries Tradeoff in Noisy Computation. Theory Comput. 6(1): 113-134 (2010) - [c14]Nishanth Ulhas Nair
Enhanced flux balance analysis to model metabolic networks. BCB 2010: 358-361 - [c13]Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler
Deterministic Algorithms for the Lovász Local Lemma. SODA 2010: 992-1004 - [i5]Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler:
Satisfiability Thresholds for k-CNF Formula with Bounded Variable Intersections. CoRR abs/1006.3030 (2010)
2000 – 2009
- 2009
- [c12]Luis Rademacher, Navin Goyal:
Learning Convex Bodies is Hard. COLT 2009 - [c11]Navin Goyal, Neil Olver
Dynamic vs. Oblivious Routing in Network Design. ESA 2009: 277-288 - [c10]Navin Goyal, Luis Rademacher
Expanders via random spanning trees. SODA 2009: 576-585 - [i4]Navin Goyal, Luis Rademacher:
Learning convex bodies is hard. CoRR abs/0904.1227 (2009) - [i3]Karthekeyan Chandrasekaran, Navin Goyal, Bernhard Haeupler:
Deterministic Algorithms for the Lovasz Local Lemma. CoRR abs/0908.0375 (2009) - 2008
- [j4]Navin Goyal, Guy Kindler, Michael E. Saks:
Lower Bounds for the Noisy Broadcast Problem. SIAM J. Comput. 37(6): 1806-1841 (2008) - [c9]Navin Goyal, Neil Olver, F. Bruce Shepherd:
The vpn conjecture is true. STOC 2008: 443-450 - [c8]Navin Goyal, Yury Lifshits, Hinrich Schütze:
Disorder inequality: a combinatorial approach to nearest neighbor search. WSDM 2008: 25-32 - [i2]Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Expanders via Random Spanning Trees. CoRR abs/0807.1496 (2008) - 2007
- [j3]Vicky Choi, Navin Goyal:
An Algorithmic Approach to the Identification of Rigid Domains in Proteins. Algorithmica 48(4): 343-362 (2007) - 2006
- [j2]Navin Goyal, Sachin Lodha, S. Muthukrishnan:
The Graham-Knowlton Problem Revisited. Theory Comput. Syst. 39(3): 399-412 (2006) - [c7]Arkadev Chattopadhyay, Navin Goyal, Pavel Pudlák, Denis Thérien:
Lower bounds for circuits with MOD_m gates. FOCS 2006: 709-718 - [c6]Vicky Choi, Navin Goyal:
An Efficient Approximation Algorithm for Point Pattern Matching Under Noise. LATIN 2006: 298-310 - 2005
- [j1]Navin Goyal, Michael E. Saks:
A parallel search game. Random Struct. Algorithms 27(2): 227-234 (2005) - [c5]Navin Goyal, Guy Kindler, Michael E. Saks:
Lower Bounds for the Noisy Broadcast Problem. FOCS 2005: 40-52 - [c4]Navin Goyal, Michael E. Saks:
Rounds vs queries trade-off in noisy computation. SODA 2005: 632-639 - [i1]Vicky Choi, Navin Goyal:
An Efficient Approximation Algorithm for Point Pattern Matching Under Noise. CoRR abs/cs/0506019 (2005) - 2004
- [c3]Vicky Choi, Navin Goyal:
A Combinatorial Shape Matching Algorithm for Rigid Protein Docking. CPM 2004: 285-296 - 2003
- [c2]Navin Goyal, Michael E. Saks, Srinivasan Venkatesh:
Optimal Separation of EROW and CROWPRAMs. CCC 2003: 93- - [c1]Samrat Ganguly, B. R. Badrinath, Navin Goyal:
Optimal Bandwidth Reservation Schedule in Cellular Network. INFOCOM 2003: 1591-1602
Coauthor Index
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last updated on 2024-08-08 20:10 CEST by the dblp team
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