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ORCIDShubhendu Trivedi
Person information
- affiliation: Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA, USA
- affiliation (former): Brown University, Institute for Computational and Experimental Research in Mathematics (ICERM), Providence, RI, USA
- affiliation (former, PhD 2018): University of Chicago, IL, USA
- affiliation (former): Toyota Technological Institute at Chicago, IL, USA
- affiliation (former): Worcester Polytechnic Institute, Department of Computer Science, MA, USA
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2020 – today
- 2024
[i22]Mircea Petrache, Shubhendu Trivedi:
Position Paper: Generalized grammar rules and structure-based generalization beyond classical equivariance for lexical tasks and transduction. CoRR abs/2402.01629 (2024)- 2023
- [c17]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. ICLR 2023 - [c16]Zhen Lin, Shubhendu Trivedi, Cao Xiao, Jimeng Sun:
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. ICML 2023: 21182-21203 - [c15]Mircea Petrache, Shubhendu Trivedi:
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance. NeurIPS 2023 - [i21]Zhen Lin, Shubhendu Trivedi, Cao Xiao, Jimeng Sun:
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. CoRR abs/2302.00839 (2023) - [i20]Mircea Petrache, Shubhendu Trivedi:
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance. CoRR abs/2305.17592 (2023) - [i19]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models. CoRR abs/2305.19187 (2023) - 2022
- [j2]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Conformal Prediction Intervals with Temporal Dependence. Trans. Mach. Learn. Res. 2022 (2022) - [c14]Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan:
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? ICLR 2022 - [c13]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Conformal Prediction with Temporal Quantile Adjustments. NeurIPS 2022 - [i18]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. CoRR abs/2202.07679 (2022) - [i17]Zhen Lin, Shubhendu Trivedi, Jimeng Sun
:
Conformal Prediction with Temporal Quantile Adjustments. CoRR abs/2205.09940 (2022) - [i16]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Conformal Prediction Intervals with Temporal Dependence. CoRR abs/2205.12940 (2022) - 2021
- [c12]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models. NeurIPS 2021: 8378-8391 - [i15]Zhen Lin, Nicholas Huang, Camille Avestruz, W. L. Kimmy Wu, Shubhendu Trivedi, João Caldeira, Brian Nord:
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning. CoRR abs/2102.13123 (2021) - [i14]Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Locally Valid and Discriminative Confidence Intervals for Deep Learning Models. CoRR abs/2106.00225 (2021) - [i13]Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan:
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? CoRR abs/2110.07472 (2021) - 2020
- [i12]Shubhendu Trivedi, J. Wang:
The Expected Jacobian Outerproduct: Theory and Empirics. CoRR abs/2006.03550 (2020) - [i11]Suhas Lohit, Shubhendu Trivedi:
Rotation-Invariant Autoencoders for Signals on Spheres. CoRR abs/2012.04474 (2020)
2010 – 2019
- 2019
- [j1]João Caldeira, W. L. K. Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, K. T. Story:
DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks. Astron. Comput. 28: 100307 (2019) - [i10]Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi Kondor:
Deep Learning for Automated Classification and Characterization of Amorphous Materials. CoRR abs/1909.04648 (2019) - [i9]Pramod Kaushik Mudrakarta, Shubhendu Trivedi, Risi Kondor:
Asymmetric Multiresolution Matrix Factorization. CoRR abs/1910.05132 (2019) - [i8]James F. Amundson, James Annis, Camille Avestruz, D. Bowring, João Caldeira, Giuseppe Cerati, Chihway L. Chang, Scott Dodelson, D. Elvira, A. Farahi, Krzysztof L. Genser, Lindsey Gray, Oliver Gutsche, Philip C. Harris, Jamie Kinney, James B. Kowalkowski, Rob Kutschke, S. Mrenna, Brian Nord, A. Para, Kevin Pedro, Gabriel N. Perdue, Alexander Scheinker, Panagiotis Spentzouris, J. St. John, Nhan Tran, Shubhendu Trivedi, Laura Trouille, W. L. K. Wu, C. R. Bom:
Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan". CoRR abs/1911.05796 (2019) - 2018
- [c11]Risi Kondor, Hy Truong Son, Horace Pan, Brandon M. Anderson, Shubhendu Trivedi:
Covariant Compositional Networks For Learning Graphs. ICLR (Workshop) 2018 - [c10]Risi Kondor, Shubhendu Trivedi:
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. ICML 2018: 2752-2760 - [c9]Risi Kondor, Zhen Lin, Shubhendu Trivedi:
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. NeurIPS 2018: 10138-10147 - [i7]Risi Kondor, Hy Truong Son, Horace Pan, Brandon M. Anderson, Shubhendu Trivedi:
Covariant Compositional Networks For Learning Graphs. CoRR abs/1801.02144 (2018) - [i6]Risi Kondor, Shubhendu Trivedi:
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. CoRR abs/1802.03690 (2018) - [i5]Risi Kondor, Zhen Lin, Shubhendu Trivedi:
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. CoRR abs/1806.09231 (2018) - [i4]Shubhendu Trivedi:
Discriminative Learning of Similarity and Group Equivariant Representations. CoRR abs/1808.10078 (2018) - [i3]João Caldeira, W. L. K. Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, K. T. Story:
DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks. CoRR abs/1810.01483 (2018) - 2015
- [i2]Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan:
The Utility of Clustering in Prediction Tasks. CoRR abs/1509.06163 (2015) - 2014
- [c8]Shubhendu Trivedi, David A. McAllester, Greg Shakhnarovich:
Discriminative Metric Learning by Neighborhood Gerrymandering. NIPS 2014: 3392-3400 - [c7]Shubhendu Trivedi, Jialei Wang, Samory Kpotufe, Gregory Shakhnarovich:
A Consistent Estimator of the Expected Gradient Outerproduct. UAI 2014: 819-828 - 2013
- [c6]Fei Song, Shubhendu Trivedi, Yutao Wang, Gábor N. Sárközy, Neil T. Heffernan:
Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. FLAIRS 2013 - 2012
- [c5]Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan:
Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction. EDM 2012: 33-40 - [c4]Zachary A. Pardos, Qing Yang Wang, Shubhendu Trivedi:
The real world significance of performance prediction. EDM 2012: 192-195 - [c3]Zachary A. Pardos, Shubhendu Trivedi, Neil T. Heffernan, Gábor N. Sárközy:
Clustered Knowledge Tracing. ITS 2012: 405-410 - [i1]Gábor N. Sárközy, Fei Song, Endre Szemerédi, Shubhendu Trivedi:
A Practical Regularity Partitioning Algorithm and its Applications in Clustering. CoRR abs/1209.6540 (2012) - 2011
- [c2]Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan:
Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions. AIED 2011: 377-384 - [c1]Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan:
Spectral Clustering in Educational Data Mining. EDM 2011: 129-138
Coauthor Index
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