Shiva Prasad Kasiviswanathan

109 papers A* 26A 8B 8C 1Misc 3Journal 53Unranked 10

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Quantitative Characterization of Forgetting in Post-Training. CoRR Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
2026	J	`jnl`	Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models. CoRR Krishnakumar Balasubramanian, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan
2026	J	`jnl`	Foundations of Global Consistency Checking with Noisy LLM Oracles. CoRR Paul He, Elke Kirschbaum, Shiva Prasad Kasiviswanathan
2025	J	`jnl`	A Classical View on Benign Overfitting: The Role of Sample Size. CoRR Junhyung Park, Patrick Blöbaum, Shiva Prasad Kasiviswanathan
2025	J	`jnl`	Debiasing Reward Models by Representation Learning with Guarantees. CoRR Ignavier Ng, Patrick Blöbaum, Siddharth Bhandari, Kun Zhang, Shiva Prasad Kasiviswanathan
2025	J	`jnl`	From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples? CoRR Sujai Hiremath, Dominik Janzing, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Kyra Gan
2025	J	`jnl`	Learning to Answer from Correct Demonstrations. CoRR Nirmit Joshi, Gene Li, Siddharth Bhandari, Shiva Prasad Kasiviswanathan, Cong Ma, Nathan Srebro
2025	J	`jnl`	Optimal Transportation and Alignment Between Gaussian Measures. CoRR Sanjit Dandapanthula, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan, Aaditya Ramdas, Ziv Goldfeld
2025	A*	`conf`	QA-Calibration of Language Model Confidence Scores. ICLR Putra Manggala, Atalanti-Anastasia Mastakouri, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2025	J	`jnl`	Training Large Language Models To Reason In Parallel With Global Forking Tokens. CoRR Sheng Jia, Xiao Wang, Shiva Prasad Kasiviswanathan
2025	J	`jnl`	What Causes Postoperative Aspiration? CoRR Supriya Nagesh, Karina Covarrubias, Robert El-Kareh, Shiva Prasad Kasiviswanathan, Nina Mishra
2024	J	`jnl`	Benign Overfitting for Regression with Trained Two-Layer ReLU Networks. CoRR Junhyung Park, Patrick Blöbaum, Shiva Prasad Kasiviswanathan
2024	A	`conf`	Differentially Private Conditional Independence Testing. AISTATS Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2024	J	`jnl`	Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries. Trans. Mach. Learn. Res. Patrick Chao, Patrick Blöbaum, Sapan Kirit Patel, Shiva Prasad Kasiviswanathan
2024	—	`conf`	The PetShop Dataset - Finding Causes of Performance Issues across Microservices. CLeaR Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan
2024	J	`jnl`	β-calibration of Language Model Confidence Scores for Generative QA. CoRR Putra Manggala, Atalanti-Anastasia Mastakouri, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2023	A*	`conf`	Debiasing Conditional Stochastic Optimization. NeurIPS Lie He, Shiva Prasad Kasiviswanathan
2023	J	`jnl`	Debiasing Conditional Stochastic Optimization. CoRR Lie He, Shiva Prasad Kasiviswanathan
2023	J	`jnl`	Differentially Private Conditional Independence Testing. CoRR Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2023	J	`jnl`	Interventional and Counterfactual Inference with Diffusion Models. CoRR Patrick Chao, Patrick Blöbaum, Shiva Prasad Kasiviswanathan
2023	A*	`conf`	Sequential Kernelized Independence Testing. ICML Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2023	J	`jnl`	The PetShop Dataset - Finding Causes of Performance Issues across Microservices. CoRR Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Elke Kirschbaum
2023	A*	`conf`	Thompson Sampling with Diffusion Generative Prior. ICML Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum
2023	J	`jnl`	Thompson Sampling with Diffusion Generative Prior. CoRR Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum
2022	A	`conf`	Balancing utility and scalability in metric differential privacy. UAI Jacob Imola, Shiva Prasad Kasiviswanathan, Stephen White, Abhinav Aggarwal, Nathanael Teissier
2022	A*	`conf`	On Measuring Causal Contributions via do-interventions. ICML Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
2022	A	`conf`	Reconstructing Test Labels from Noisy Loss Functions. AISTATS Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier
2022	J	`jnl`	Sequential Kernelized Independence Testing. CoRR Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2022	A*	`conf`	Uplifting Bandits. NeurIPS Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton
2022	J	`jnl`	Uplifting Bandits. CoRR Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton
2021	A*	`conf`	Collaborative Causal Discovery with Atomic Interventions. NeurIPS Raghavendra Addanki, Shiva Prasad Kasiviswanathan
2021	J	`jnl`	Collaborative Causal Discovery with Atomic Interventions. CoRR Raghavendra Addanki, Shiva Prasad Kasiviswanathan
2021	A*	`conf`	Federated Learning under Arbitrary Communication Patterns. ICML Dmitrii Avdiukhin, Shiva Prasad Kasiviswanathan
2021	A*	`conf`	Label Inference Attacks from Log-loss Scores. ICML Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier
2021	J	`jnl`	Label Inference Attacks from Log-loss Scores. CoRR Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier
2021	J	`jnl`	On Codomain Separability and Label Inference from (Noisy) Loss Functions. CoRR Abhinav Aggarwal, Shiva Prasad Kasiviswanathan, Zekun Xu, Oluwaseyi Feyisetan, Nathanael Teissier
2021	A	`conf`	SGD with low-dimensional gradients with applications to private and distributed learning. UAI Shiva Prasad Kasiviswanathan
2020	A	`conf`	Contextual Online False Discovery Rate Control. AISTATS Shiyun Chen, Shiva Prasad Kasiviswanathan
2020	A*	`conf`	Efficient Intervention Design for Causal Discovery with Latents. ICML Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco
2020	J	`jnl`	Efficient Intervention Design for Causal Discovery with Latents. CoRR Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco
2020	J	`jnl`	Subsampled Rényi Differential Privacy and Analytical Moments Accountant. J. Priv. Confidentiality Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan
2019	J	`jnl`	Restricted Isometry Property under High Correlations. CoRR Shiva Prasad Kasiviswanathan, Mark Rudelson
2019	A	`conf`	Subsampled Renyi Differential Privacy and Analytical Moments Accountant. AISTATS Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan
2018	A*	`conf`	Network Approximation using Tensor Sketching. IJCAI Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
2018	A*	`conf`	Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression. COLT Shiva Prasad Kasiviswanathan, Mark Rudelson
2018	A*	`conf`	Semi-Supervised Learning on Data Streams via Temporal Label Propagation. ICML Tal Wagner, Sudipto Guha, Shiva Prasad Kasiviswanathan, Nina Mishra
2018	J	`jnl`	Subsampled Rényi Differential Privacy and Analytical Moments Accountant. CoRR Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan
2018	A*	`conf`	Verifying Properties of Binarized Deep Neural Networks. AAAI Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, Toby Walsh
2017	J	`jnl`	Compressed Sparse Linear Regression. CoRR Shiva Prasad Kasiviswanathan, Mark Rudelson
2017	J	`jnl`	Deep Neural Network Approximation using Tensor Sketching. CoRR Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
2017	A*	`conf`	Private Incremental Regression. PODS Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin
2017	J	`jnl`	Private Incremental Regression. CoRR Shiva Prasad Kasiviswanathan, Kobbi Nissim, Hongxia Jin
2017	—	`conf`	Simple Black-Box Adversarial Attacks on Deep Neural Networks. CVPR Workshops Nina Narodytska, Shiva Prasad Kasiviswanathan
2017	J	`jnl`	Verifying Properties of Binarized Deep Neural Networks. CoRR Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, Toby Walsh
2016	A*	`conf`	Efficient Private Empirical Risk Minimization for High-dimensional Learning. ICML Shiva Prasad Kasiviswanathan, Hongxia Jin
2016	A*	`conf`	Private spatial data aggregation in the local setting. ICDE Rui Chen, Haoran Li, A. K. Qin, Shiva Prasad Kasiviswanathan, Hongxia Jin
2016	J	`jnl`	Simple Black-Box Adversarial Perturbations for Deep Networks. CoRR Nina Narodytska, Shiva Prasad Kasiviswanathan
2016	A*	`conf`	Streaming spectral clustering. ICDE Shinjae Yoo, Hao Huang, Shiva Prasad Kasiviswanathan
2015	A*	`conf`	Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications. AAAI Shengping Zhang, Shiva Prasad Kasiviswanathan, Pong C. Yuen, Mehrtash Harandi
2015	—	`conf`	Spectral Norm of Random Kernel Matrices with Applications to Privacy. APPROX-RANDOM Shiva Prasad Kasiviswanathan, Mark Rudelson
2015	J	`jnl`	Spectral Norm of Random Kernel Matrices with Applications to Privacy. CoRR Shiva Prasad Kasiviswanathan, Mark Rudelson
2015	J	`jnl`	Streaming Anomaly Detection Using Randomized Matrix Sketching. Proc. VLDB Endow. Hao Huang, Shiva Prasad Kasiviswanathan
2015	A	`conf`	Unsupervised Feature Selection on Data Streams. CIKM Hao Huang, Shinjae Yoo, Shiva Prasad Kasiviswanathan
2014	J	`jnl`	Approximately Counting Embeddings into Random Graphs. Comb. Probab. Comput. Martin Fürer, Shiva Prasad Kasiviswanathan
2014	J	`jnl`	Bounds on the sample complexity for private learning and private data release. Mach. Learn. Amos Beimel, Hai Brenner, Shiva Prasad Kasiviswanathan, Kobbi Nissim
2014	J	`jnl`	On the 'Semantics' of Differential Privacy: A Bayesian Formulation. J. Priv. Confidentiality Shiva Prasad Kasiviswanathan, Adam D. Smith
2013	J	`jnl`	An exponential time 2-approximation algorithm for bandwidth. Theor. Comput. Sci. Martin Fürer, Serge Gaspers, Shiva Prasad Kasiviswanathan
2013	B	`conf`	Analyzing Graphs with Node Differential Privacy. TCC Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
2013	Misc	`conf`	Fast online L1-dictionary learning algorithms for novel document detection. ICASSP Shiva Prasad Kasiviswanathan
2013	J	`jnl`	Novel document detection for massive data streams using distributed dictionary learning. IBM J. Res. Dev. Shiva Prasad Kasiviswanathan, Gao Cong, Prem Melville, Richard D. Lawrence
2013	A*	`conf`	The Power of Linear Reconstruction Attacks. SODA Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith
2012	A*	`conf`	Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani
2012	—	`conf`	Online L1-Dictionary Learning with Application to Novel Document Detection. NIPS Shiva Prasad Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville
2012	J	`jnl`	Spanners for geometric intersection graphs with applications. J. Comput. Geom. Martin Fürer, Shiva Prasad Kasiviswanathan
2012	J	`jnl`	The Power of Linear Reconstruction Attacks CoRR Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith
2011	J	`jnl`	Bandwidth provisioning in infrastructure-based wireless networks employing directional antennas. Pervasive Mob. Comput. Shiva Prasad Kasiviswanathan, Bo Zhao, Sudarshan Vasudevan, Bhuvan Urgaonkar
2011	—	`conf`	Efficient placement of directional antennas in infrastructure-based wireless networks. MILCOM Feng Pan, Shiva Prasad Kasiviswanathan
2011	A	`conf`	Emerging topic detection using dictionary learning. CIKM Shiva Prasad Kasiviswanathan, Prem Melville, Arindam Banerjee, Vikas Sindhwani
2011	A*	`conf`	Geography-based analysis of the Internet infrastructure. INFOCOM Shiva Prasad Kasiviswanathan, Stephan J. Eidenbenz, Guanhua Yan
2011	A*	`conf`	The Rigidity Transition in Random Graphs. SODA Shiva Prasad Kasiviswanathan, Cristopher Moore, Louis Theran
2011	J	`jnl`	What Can We Learn Privately? SIAM J. Comput. Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
2010	Misc	`conf`	Bandwidth Provisioning in Infrastructure-Based Wireless Networks Employing Directional Antennas. ICDCN Shiva Prasad Kasiviswanathan, Bo Zhao, Sudarshan Vasudevan, Bhuvan Urgaonkar
2010	B	`conf`	Bounds on the Sample Complexity for Private Learning and Private Data Release. TCC Amos Beimel, Shiva Prasad Kasiviswanathan, Kobbi Nissim
2010	B	`conf`	Explicit Spatial Scattering for Load Balancing in Conservatively Synchronized Parallel Discrete Event Simulations. PADS Sunil Thulasidasan, Shiva Prasad Kasiviswanathan, Stephan J. Eidenbenz, Philip Romero
2010	B	`conf`	Matrix Interdiction Problem. CPAIOR Shiva Prasad Kasiviswanathan, Feng Pan
2010	A*	`conf`	The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. STOC Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith, Jonathan R. Ullman
2010	J	`jnl`	The rigidity transition in random graphs. CoRR Shiva Prasad Kasiviswanathan, Cristopher Moore, Louis Theran
2009	J	`jnl`	An Exponential Time 2-Approximation Algorithm for Bandwidth CoRR Martin Fürer, Serge Gaspers, Shiva Prasad Kasiviswanathan
2009	—	`conf`	An Exponential Time 2-Approximation Algorithm for Bandwidth. IWPEC Martin Fürer, Serge Gaspers, Shiva Prasad Kasiviswanathan
2009	Misc	`conf`	Designing systems for large-scale, discrete-event simulations: Experiences with the FastTrans parallel microsimulator. HiPC Sunil Thulasidasan, Shiva Prasad Kasiviswanathan, Stephan J. Eidenbenz, Emanuele Galli, Susan M. Mniszewski, Philip Romero
2008	J	`jnl`	A Note on Differential Privacy: Defining Resistance to Arbitrary Side Information CoRR Shiva Prasad Kasiviswanathan, Adam D. Smith
2008	J	`jnl`	Approximately Counting Embeddings into Random Graphs CoRR Martin Fürer, Shiva Prasad Kasiviswanathan
2008	—	`conf`	Approximately Counting Embeddings into Random Graphs. APPROX-RANDOM Martin Fürer, Shiva Prasad Kasiviswanathan
2008	J	`jnl`	Composition Attacks and Auxiliary Information in Data Privacy CoRR Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, Adam D. Smith
2008	A*	`conf`	Composition attacks and auxiliary information in data privacy. KDD Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, Adam D. Smith
2008	A*	`conf`	What Can We Learn Privately? FOCS Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
2008	J	`jnl`	What Can We Learn Privately? CoRR Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
2007	C	`conf`	Algorithms for Counting 2-SatSolutions and Colorings with Applications. AAIM Martin Fürer, Shiva Prasad Kasiviswanathan
2007	—	`conf`	Exact Max 2-Sat: Easier and Faster. SOFSEM (1) Martin Fürer, Shiva Prasad Kasiviswanathan
2007	B	`conf`	Faster Approximation of Distances in Graphs. WADS Piotr Berman, Shiva Prasad Kasiviswanathan
2007	B	`conf`	Packing to angles and sectors. SPAA Piotr Berman, Jieun K. Jeong, Shiva Prasad Kasiviswanathan, Bhuvan Urgaonkar
2007	B	`conf`	Spanners for Geometric Intersection Graphs. WADS Martin Fürer, Shiva Prasad Kasiviswanathan
2006	B	`conf`	Approximate Distance Queries in Disk Graphs. WAOA Martin Fürer, Shiva Prasad Kasiviswanathan
2006	J	`jnl`	Combinatorics of TCP reordering. J. Comb. Optim. Anders Hansson, Gabriel Istrate, Shiva Prasad Kasiviswanathan
2006	J	`jnl`	Packing to angles and sectors. Electron. Colloquium Comput. Complex. Piotr Berman, Jieun K. Jeong, Shiva Prasad Kasiviswanathan, Bhuvan Urgaonkar
2006	J	`jnl`	Spanners for Geometric Intersection Graphs CoRR Martin Fürer, Shiva Prasad Kasiviswanathan
2005	J	`jnl`	Algorithms for Counting 2-SAT Solutions and Colorings with Applications Electron. Colloquium Comput. Complex. Martin Fürer, Shiva Prasad Kasiviswanathan
2005	—	`conf`	Approximately Counting Perfect Matchings in General Graphs. ALENEX/ANALCO Martin Fürer, Shiva Prasad Kasiviswanathan
2004	—	`conf`	An Almost Linear Time Approximation Algorithm for the Permanen of a Random (0-1) Matrix. FSTTCS Martin Fürer, Shiva Prasad Kasiviswanathan