Shubhendu Trivedi

56 papers A* 10A 3B 4Misc 1Journal 36Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data. CoRR Stefanos Pertigkiozoglou, Mircea Petrache, Shubhendu Trivedi, Kostas Daniilidis
2026	J	`jnl`	Watermarking Degrades Alignment in Language Models: Analysis and Mitigation. Trans. Mach. Learn. Res. Apurv Verma, Hai Phan, Shubhendu Trivedi
2025	J	`jnl`	Automating Sensor Characterization with Bayesian Optimization. CoRR J. Cuevas-Zepeda, C. Chavez, Juan Estrada, J. Noonan, Brian Nord, N. Saffold, Miguel Sofo Haro, R. Spinola e Castro, Shubhendu Trivedi
2025	J	`jnl`	MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels. CoRR Xiaoou Liu, Zhen Lin, Longchao Da, Chacha Chen, Shubhendu Trivedi, Hua Wei
2025	J	`jnl`	On Universality Classes of Equivariant Networks. CoRR Marco Pacini, Gabriele Santin, Bruno Lepri, Shubhendu Trivedi
2025	J	`jnl`	On Universality of Deep Equivariant Networks. CoRR Marco Pacini, Mircea Petrache, Bruno Lepri, Shubhendu Trivedi, Robin Walters
2025	J	`jnl`	On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning. CoRR Shubhendu Trivedi, Brian Nord
2025	A	`conf`	Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks. AISTATS Ashwin Samudre, Mircea Petrache, Brian Nord, Shubhendu Trivedi
2025	J	`jnl`	Watermarking Degrades Alignment in Language Models: Analysis and Mitigation. CoRR Apurv Verma, NhatHai Phan, Shubhendu Trivedi
2024	A*	`conf`	Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation. EMNLP Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2024	J	`jnl`	Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation. CoRR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2024	J	`jnl`	Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models. Trans. Mach. Learn. Res. Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2024	A*	`conf`	Improving Equivariant Model Training via Constraint Relaxation. NeurIPS Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi, Kostas Daniilidis
2024	J	`jnl`	Improving Equivariant Model Training via Constraint Relaxation. CoRR Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi, Kostas Daniilidis
2024	J	`jnl`	Position Paper: Generalized grammar rules and structure-based generalization beyond classical equivariance for lexical tasks and transduction. CoRR Mircea Petrache, Shubhendu Trivedi
2024	J	`jnl`	Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks. CoRR Ashwin Samudre, Mircea Petrache, Brian Nord, Shubhendu Trivedi
2023	A*	`conf`	Approximation-Generalization Trade-offs under (Approximate) Group Equivariance. NeurIPS Mircea Petrache, Shubhendu Trivedi
2023	J	`jnl`	Approximation-Generalization Trade-offs under (Approximate) Group Equivariance. CoRR Mircea Petrache, Shubhendu Trivedi
2023	A*	`conf`	Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. ICML Zhen Lin, Shubhendu Trivedi, Cao Xiao, Jimeng Sun
2023	J	`jnl`	Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. CoRR Zhen Lin, Shubhendu Trivedi, Cao Xiao, Jimeng Sun
2023	J	`jnl`	Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models. CoRR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2023	A*	`conf`	Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. ICLR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2022	A*	`conf`	Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? ICLR Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan
2022	J	`jnl`	Conformal Prediction Intervals with Temporal Dependence. CoRR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2022	J	`jnl`	Conformal Prediction Intervals with Temporal Dependence. Trans. Mach. Learn. Res. Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2022	A*	`conf`	Conformal Prediction with Temporal Quantile Adjustments. NeurIPS Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2022	J	`jnl`	Conformal Prediction with Temporal Quantile Adjustments. CoRR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2022	J	`jnl`	Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. CoRR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2021	J	`jnl`	Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? CoRR Matthew Farrell, Blake Bordelon, Shubhendu Trivedi, Cengiz Pehlevan
2021	J	`jnl`	DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning. CoRR Zhen Lin, Nicholas Huang, Camille Avestruz, W. L. Kimmy Wu, Shubhendu Trivedi, João Caldeira, Brian Nord
2021	J	`jnl`	Locally Valid and Discriminative Confidence Intervals for Deep Learning Models. CoRR Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2021	A*	`conf`	Locally Valid and Discriminative Prediction Intervals for Deep Learning Models. NeurIPS Zhen Lin, Shubhendu Trivedi, Jimeng Sun
2020	J	`jnl`	Rotation-Invariant Autoencoders for Signals on Spheres. CoRR Suhas Lohit, Shubhendu Trivedi
2020	J	`jnl`	The Expected Jacobian Outerproduct: Theory and Empirics. CoRR Shubhendu Trivedi, J. Wang
2019	J	`jnl`	Asymmetric Multiresolution Matrix Factorization. CoRR Pramod Kaushik Mudrakarta, Shubhendu Trivedi, Risi Kondor
2019	J	`jnl`	Deep Learning for Automated Classification and Characterization of Amorphous Materials. CoRR Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi Kondor
2019	J	`jnl`	DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks. Astron. Comput. João Caldeira, W. L. K. Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, K. T. Story
2019	J	`jnl`	Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan". CoRR 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
2018	A*	`conf`	Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. NeurIPS Risi Kondor, Zhen Lin, Shubhendu Trivedi
2018	J	`jnl`	Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network. CoRR Risi Kondor, Zhen Lin, Shubhendu Trivedi
2018	—	`conf`	Covariant Compositional Networks For Learning Graphs. ICLR (Workshop) Risi Kondor, Hy Truong Son, Horace Pan, Brandon M. Anderson, Shubhendu Trivedi
2018	J	`jnl`	Covariant Compositional Networks For Learning Graphs. CoRR Risi Kondor, Hy Truong Son, Horace Pan, Brandon M. Anderson, Shubhendu Trivedi
2018	J	`jnl`	DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks. CoRR João Caldeira, W. L. K. Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, K. T. Story
2018	J	`jnl`	Discriminative Learning of Similarity and Group Equivariant Representations. CoRR Shubhendu Trivedi
2018	A*	`conf`	On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. ICML Risi Kondor, Shubhendu Trivedi
2018	J	`jnl`	On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. CoRR Risi Kondor, Shubhendu Trivedi
2015	J	`jnl`	The Utility of Clustering in Prediction Tasks. CoRR Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan
2014	A	`conf`	A Consistent Estimator of the Expected Gradient Outerproduct. UAI Shubhendu Trivedi, Jialei Wang, Samory Kpotufe, Gregory Shakhnarovich
2014	—	`conf`	Discriminative Metric Learning by Neighborhood Gerrymandering. NIPS Shubhendu Trivedi, David A. McAllester, Greg Shakhnarovich
2013	Misc	`conf`	Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. FLAIRS Fei Song, Shubhendu Trivedi, Yutao Wang, Gábor N. Sárközy, Neil T. Heffernan
2012	J	`jnl`	A Practical Regularity Partitioning Algorithm and its Applications in Clustering CoRR Gábor N. Sárközy, Fei Song, Endre Szemerédi, Shubhendu Trivedi
2012	B	`conf`	Clustered Knowledge Tracing. ITS Zachary A. Pardos, Shubhendu Trivedi, Neil T. Heffernan, Gábor N. Sárközy
2012	B	`conf`	Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction. EDM Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan
2012	B	`conf`	The real world significance of performance prediction. EDM Zachary A. Pardos, Qing Yang Wang, Shubhendu Trivedi
2011	A	`conf`	Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions. AIED Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan
2011	B	`conf`	Spectral Clustering in Educational Data Mining. EDM Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan