Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions. - Shubhendu Trivedi; Zachary A. Pardos; Neil T. Heffernan (AIED, 2011)

Spectral Clustering in Educational Data Mining. - Shubhendu Trivedi; Zachary A. Pardos; Gábor N. Sárközy; Neil T. Heffernan (EDM, 2011)

The real world significance of performance prediction. - Zachary A. Pardos; Qing Yang Wang; Shubhendu Trivedi (EDM, 2012)

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction. - Shubhendu Trivedi; Zachary A. Pardos; Gábor N. Sárközy; Neil T. Heffernan (EDM, 2012)

Clustered Knowledge Tracing. - Zachary A. Pardos; Shubhendu Trivedi; Neil T. Heffernan; Gábor N. Sárközy (ITS, 2012)

A Practical Regularity Partitioning Algorithm and its Applications in Clustering - Gábor N. Sárközy; Fei Song; Endre Szemerédi; Shubhendu Trivedi (CoRR, 2012)

Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. - Fei Song; Shubhendu Trivedi; Yutao Wang; Gábor N. Sárközy; Neil T. Heffernan (FLAIRS, 2013)

Discriminative Metric Learning by Neighborhood Gerrymandering. - Shubhendu Trivedi; David A. McAllester; Greg Shakhnarovich (NIPS, 2014)

A Consistent Estimator of the Expected Gradient Outerproduct. - Shubhendu Trivedi; Jialei Wang; Samory Kpotufe; Gregory Shakhnarovich (UAI, 2014)

The Utility of Clustering in Prediction Tasks. - Shubhendu Trivedi; Zachary A. Pardos; Neil T. Heffernan (CoRR, 2015)

Covariant Compositional Networks For Learning Graphs. - Risi Kondor; Hy Truong Son; Horace Pan; Brandon M. Anderson; Shubhendu Trivedi (ICLR (Workshop), 2018)

On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. - Risi Kondor; Shubhendu Trivedi (ICML, 2018)

Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. - Risi Kondor; Zhen Lin 001; Shubhendu Trivedi (NeurIPS, 2018)

Discriminative Learning of Similarity and Group Equivariant Representations. - Shubhendu Trivedi (CoRR, 2018)

DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks. - João Caldeira; W. L. K. Wu; Brian Nord; Camille Avestruz; Shubhendu Trivedi; K. T. Story (Astron. Comput., 2019)

Deep Learning for Automated Classification and Characterization of Amorphous Materials. - Kirk Swanson; Shubhendu Trivedi; Joshua Lequieu; Kyle Swanson; Risi Kondor (CoRR, 2019)

Asymmetric Multiresolution Matrix Factorization. - Pramod Kaushik Mudrakarta; Shubhendu Trivedi; Risi Kondor (CoRR, 2019)

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan". - 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; et al. (CoRR, 2019)

The Expected Jacobian Outerproduct: Theory and Empirics. - Shubhendu Trivedi; J. Wang (CoRR, 2020)

Rotation-Invariant Autoencoders for Signals on Spheres. - Suhas Lohit; Shubhendu Trivedi (CoRR, 2020)

Locally Valid and Discriminative Prediction Intervals for Deep Learning Models. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (NeurIPS, 2021)

DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning. - Zhen Lin 001; Nicholas Huang; Camille Avestruz; W. L. Kimmy Wu; Shubhendu Trivedi; João Caldeira; Brian Nord (CoRR, 2021)

Locally Valid and Discriminative Confidence Intervals for Deep Learning Models. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (CoRR, 2021)

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? - Matthew Farrell; Blake Bordelon; Shubhendu Trivedi; Cengiz Pehlevan (ICLR, 2022)

Conformal Prediction with Temporal Quantile Adjustments. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (NeurIPS, 2022)

Conformal Prediction Intervals with Temporal Dependence. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (Trans. Mach. Learn. Res., 2022)

Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (ICLR, 2023)

Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. - Zhen Lin 001; Shubhendu Trivedi; Cao Xiao; Jimeng Sun 001 (ICML, 2023)

Approximation-Generalization Trade-offs under (Approximate) Group Equivariance. - Mircea Petrache; Shubhendu Trivedi (NeurIPS, 2023)

Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (EMNLP, 2024)

Improving Equivariant Model Training via Constraint Relaxation. - Stefanos Pertigkiozoglou; Evangelos Chatzipantazis; Shubhendu Trivedi; Kostas Daniilidis (NeurIPS, 2024)

Position Paper: Generalized grammar rules and structure-based generalization beyond classical equivariance for lexical tasks and transduction. - Mircea Petrache; Shubhendu Trivedi (CoRR, 2024)

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models. - Zhen Lin 001; Shubhendu Trivedi; Jimeng Sun 001 (Trans. Mach. Learn. Res., 2024)

Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks. - Ashwin Samudre; Mircea Petrache; Brian Nord; Shubhendu Trivedi (AISTATS, 2025)

MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels. - Xiaoou Liu; Zhen Lin 001; Longchao Da; Chacha Chen; Shubhendu Trivedi; Hua Wei 001 (CoRR, 2025)

On Universality Classes of Equivariant Networks. - Marco Pacini; Gabriele Santin; Bruno Lepri; Shubhendu Trivedi (CoRR, 2025)

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning. - Shubhendu Trivedi; Brian Nord (CoRR, 2025)

Watermarking Degrades Alignment in Language Models: Analysis and Mitigation. - Apurv Verma; NhatHai Phan; Shubhendu Trivedi (CoRR, 2025)