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ORCIDNakul Verma
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2020 – today
- 2024
[i16]Daniel Jiwoong Im, Kevin Zhang, Nakul Verma, Kyunghyun Cho:
Using Deep Autoregressive Models as Causal Inference Engines. CoRR abs/2409.18581 (2024)- 2023
- [c17]Iddo Drori, Sarah J. Zhang, Zad Chin, Reece Shuttleworth, Albert Lu, Linda Chen, Bereket Birbo, Michele He, Pedro Lantigua, Sunny Tran, Gregory Hunter, Bo Feng, Newman Cheng, Roman Wang, Yann Hicke, Saisamrit Surbehera, Arvind Raghavan, Alexander E. Siemenn, Nikhil Singh, Jayson Lynch, Avi Shporer, Nakul Verma, Tonio Buonassisi, Armando Solar-Lezama:
A Dataset for Learning University STEM Courses at Scale and Generating Questions at a Human Level. AAAI 2023: 15921-15929 - [c16]Narutatsu Ri, Fei-Tzin Lee, Nakul Verma:
Contrastive Loss is All You Need to Recover Analogies as Parallel Lines. RepL4NLP@ACL 2023: 164-173 - [i15]Narutatsu Ri, Fei-Tzin Lee, Nakul Verma:
Contrastive Loss is All You Need to Recover Analogies as Parallel Lines. CoRR abs/2306.08221 (2023) - 2022
- [c15]Leonard Tang, Elizabeth Ke, Nikhil Singh, Bo Feng, Derek Austin, Nakul Verma, Iddo Drori:
Solving Probability and Statistics Problems by Probabilistic Program Synthesis at Human Level and Predicting Solvability. AIED (2) 2022: 612-615 - [c14]Lampros Flokas, Weiyuan Wu, Jiannan Wang, Nakul Verma, Eugene Wu:
How I stopped worrying about training data bugs and started complaining. DEEM@SIGMOD 2022: 1:1-1:5 - [c13]Lampros Flokas, Weiyuan Wu, Yejia Liu, Jiannan Wang, Nakul Verma, Eugene Wu:
Complaint-Driven Training Data Debugging at Interactive Speeds. SIGMOD Conference 2022: 369-383 - [i14]Xiao Yu, Nakul Verma:
Improving Model Training via Self-learned Label Representations. CoRR abs/2209.04528 (2022) - 2021
- [c12]Hengrui Xing, Ansaf Salleb-Aouissi, Nakul Verma:
Automated Symbolic Law Discovery: A Computer Vision Approach. AAAI 2021: 660-668 - [i13]Iddo Drori, Nakul Verma:
Solving Linear Algebra by Program Synthesis. CoRR abs/2111.08171 (2021) - [i12]Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori:
Solving Probability and Statistics Problems by Program Synthesis. CoRR abs/2111.08267 (2021) - [i11]Fei-Tzin Lee, Chris Kedzie, Nakul Verma, Kathleen R. McKeown:
An analysis of document graph construction methods for AMR summarization. CoRR abs/2111.13993 (2021) - [i10]Iddo Drori, Sunny Tran, Roman Wang, Newman Cheng, Kevin Liu, Leonard Tang, Elizabeth Ke, Nikhil Singh, Taylor L. Patti, Jayson Lynch, Avi Shporer, Nakul Verma, Eugene Wu, Gilbert Strang:
A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More. CoRR abs/2112.15594 (2021) - 2020
- [j3]Britton A. Sauerbrei
, Jian-Zhong Guo, Jeremy D. Cohen, Matteo Mischiati, Wendy Guo, Mayank Kabra, Nakul Verma, Brett Mensh, Kristin Branson, Adam W. Hantman:
Cortical pattern generation during dexterous movement is input-driven. Nat. 577(7790): 386-391 (2020) - [c11]Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau:
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. EC 2020: 679-681 - [i9]Bo Cowgill, Fabrizio Dell'Acqua, Samuel Deng, Daniel Hsu, Nakul Verma, Augustin Chaintreau:
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. CoRR abs/2012.02394 (2020)
2010 – 2019
- 2019
- [i8]Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon, Nakul Verma:
Noise-tolerant fair classification. CoRR abs/1901.10837 (2019) - [i7]Max Aalto, Nakul Verma:
Metric Learning on Manifolds. CoRR abs/1902.01738 (2019) - [i6]Daniel Jiwoong Im, Yibo Jiang, Nakul Verma:
Model-Agnostic Meta-Learning using Runge-Kutta Methods. CoRR abs/1910.07368 (2019) - [i5]Yibo Jiang, Nakul Verma:
Meta-Learning to Cluster. CoRR abs/1910.14134 (2019) - 2018
- [i4]Daniel Jiwoong Im, Nakul Verma, Kristin Branson:
Stochastic Neighbor Embedding under f-divergences. CoRR abs/1811.01247 (2018) - 2017
- [j2]Samory Kpotufe, Nakul Verma:
Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality. J. Mach. Learn. Res. 18: 44:1-44:29 (2017) - 2015
- [c10]Nakul Verma, Kristin Branson:
Sample Complexity of Learning Mahalanobis Distance Metrics. NIPS 2015: 2584-2592 - [i3]Nakul Verma, Kristin Branson:
Sample complexity of learning Mahalanobis distance metrics. CoRR abs/1505.02729 (2015) - 2013
- [j1]Nakul Verma:
Distance preserving embeddings for general n-dimensional manifolds. J. Mach. Learn. Res. 14(1): 2415-2448 (2013) - [c9]Bojan Milosevic, Jinseok Yang, Nakul Verma, Sameer S. Tilak, Piero Zappi, Elisabetta Farella, Luca Benini, Tajana Simunic Rosing:
Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization. MSWiM 2013: 247-254 - 2012
- [b1]Nakul Verma:
Learning from data with low intrinsic dimension. University of California, San Diego, USA, 2012 - [c8]Nakul Verma, Dhruv Mahajan, Sundararajan Sellamanickam, Vinod Nair:
Learning hierarchical similarity metrics. CVPR 2012: 2280-2287 - [c7]Celal Ziftci, Nima Nikzad, Nakul Verma, Piero Zappi, Elizabeth S. Bales, Ingolf Krueger, William G. Griswold:
Citisense: mobile air quality sensing for individuals and communities. SPLASH 2012: 23-24 - [c6]Nima Nikzad, Nakul Verma, Celal Ziftci, Elizabeth S. Bales, Nichole Quick, Piero Zappi, Kevin Patrick, Sanjoy Dasgupta, Ingolf Krueger, Tajana Simunic Rosing, William G. Griswold:
CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system. Wireless Health 2012: 11:1-11:8 - [c5]Nakul Verma:
Distance Preserving Embeddings for General n-Dimensional Manifolds. COLT 2012: 32.1-32.28 - [i2]Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta:
Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? CoRR abs/1205.2609 (2012) - [i1]Sanjoy Dasgupta, Daniel J. Hsu, Nakul Verma:
A concentration theorem for projections. CoRR abs/1206.6813 (2012) - 2011
- [c4]Boris Babenko, Nakul Verma, Piotr Dollár, Serge J. Belongie:
Multiple Instance Learning with Manifold Bags. ICML 2011: 81-88
2000 – 2009
- 2009
- [c3]Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta:
Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? UAI 2009: 565-574 - 2007
- [c2]Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma:
Learning the structure of manifolds using random projections. NIPS 2007: 473-480 - 2006
- [c1]Sanjoy Dasgupta, Daniel J. Hsu, Nakul Verma:
A Concentration Theorem for Projections. UAI 2006
Coauthor Index
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