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2020 – today
- 2024
[j10]Elizabeth Newman, Lior Horesh, Haim Avron
, Misha E. Kilmer:
Stable tensor neural networks for efficient deep learning. Frontiers Big Data 7 (2024)- [j9]Anthony P. Austin, Lior Horesh, Vassilis Kalantzis:
A Rational Filtering Algorithm for Sequences of Shifted Symmetric Linear Systems with Applications to Frequency Response Analysis. SIAM J. Sci. Comput. 46(6): 3552- (2024) - [c30]Vasileios Kalantzis, Shashanka Ubaru, Chai Wah Wu, Georgios Kollias, Lior Horesh:
Asynchronous Randomized Trace Estimation. AISTATS 2024: 4294-4302 - [c29]Vishal Pallagani, Bharath C. Muppasani, Kaushik Roy, Francesco Fabiano, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit P. Sheth:
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). ICAPS 2024: 432-444 - [c28]Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis, Lior Horesh:
Topological data analysis on noisy quantum computers. ICLR 2024 - [c27]Georgios Kollias, Vassilis Kalantzis, Lior Horesh, Shashanka Ubaru, Panagiotis A. Traganitis:
Counting Triangles of Graphs via Matrix Partitioning. MLSP 2024: 1-6 - [i40]Vishal Pallagani, Kaushik Roy, Bharath Muppasani, Francesco Fabiano, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit P. Sheth:
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS). CoRR abs/2401.02500 (2024) - [i39]Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru, Tayfun Gokmen, Chai Wah Wu, Anshul Gupta, Haim Avron, Tomasz Nowicki, Malte J. Rasch, O. Murat Onen, Vanessa López-Marrero, Effendi Leobandung, Yasuteru Kohda, Wilfried Haensch, Lior Horesh:
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR abs/2401.13754 (2024) - [i38]Liron Mor-Yosef, Shashanka Ubaru, Lior Horesh, Haim Avron:
Multivariate trace estimation using quantum state space linear algebra. CoRR abs/2405.01098 (2024) - [i37]Vassilis Kalantzis, Yuanzhe Xi, Lior Horesh, Yousef Saad:
Randomized linear solvers for computational architectures with straggling workers. CoRR abs/2407.01098 (2024) - [i36]Soumyadip Ghosh, Lior Horesh, Vassilis Kalantzis, Yingdong Lu, Tomasz Nowicki:
Regenerative Ulam-von Neumann Algorithm: An Innovative Markov chain Monte Carlo Method for Matrix Inversion. CoRR abs/2407.16661 (2024) - [i35]Lior Horesh, Vasileios Kalantzis, Yingdong Lu, Tomasz Nowicki:
On The Variance of Schatten p-Norm Estimation with Gaussian Sketching Matrices. CoRR abs/2410.16455 (2024) - 2023
- [j8]Vassilis Kalantzis, Lior Horesh:
Enhanced algebraic substructuring for symmetric generalized eigenvalue problems. Numer. Linear Algebra Appl. 30(2) (2023) - [c26]Marianna Bergamaschi Ganapini, Francesco Fabiano, Lior Horesh, Andrea Loreggia, Nicholas Mattei, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Value-based Fast and Slow AI Nudging. ETHAICS@IJCAI 2023 - [c25]Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu, Anshul Gupta, Shashanka Ubaru, Tayfun Gokmen, Lior Horesh:
Solving Sparse Linear Systems via Flexible GMRES with In-Memory Analog Preconditioning. HPEC 2023: 1-7 - [c24]Vishal Pallagani, Bharath Muppasani, Biplav Srivastava, Francesca Rossi, Lior Horesh, Keerthiram Murugesan, Andrea Loreggia, Francesco Fabiano, Rony Joseph, Yathin Kethepalli:
Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers. IJCAI 2023: 7158-7162 - [e1]Marianna Bergamaschi Ganapini, Lior Horesh, Luís C. Lamb, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Proceedings of the Thinking Fast and Slow and Other Cognitive Theories in AI, a AAAI 2022 Fall Symposium, Westin Arlington Gateway in Arlington, Virginia, November 17-19, 2022. CEUR Workshop Proceedings 3332, CEUR-WS.org 2023 [contents] - [i34]Francesco Fabiano, Vishal Pallagani, Marianna Bergamaschi Ganapini, Lior Horesh, Andrea Loreggia, Keerthiram Murugesan, Francesca Rossi, Biplav Srivastava:
Fast and Slow Planning. CoRR abs/2303.04283 (2023) - [i33]Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Biplav Srivastava, Lior Horesh, Francesco Fabiano, Andrea Loreggia:
Understanding the Capabilities of Large Language Models for Automated Planning. CoRR abs/2305.16151 (2023) - [i32]Marianna Bergamaschi Ganapini, Francesco Fabiano, Lior Horesh, Andrea Loreggia, Nicholas Mattei, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Value-based Fast and Slow AI Nudging. CoRR abs/2307.07628 (2023) - [i31]Ryan Cory-Wright, Bachir El Khadir, Cristina Cornelio, Sanjeeb Dash, Lior Horesh:
AI Hilbert: From Data and Background Knowledge to Automated Scientific Discovery. CoRR abs/2308.09474 (2023) - [i30]Chai Wah Wu, Mark S. Squillante, Vasileios Kalantzis, Lior Horesh:
Stable iterative refinement algorithms for solving linear systems. CoRR abs/2309.07865 (2023) - 2022
- [j7]Vasileios Kalantzis, Mark S. Squillante, Shashanka Ubaru, Lior Horesh:
On Quantum Algorithms for Random Walks in the Nonnegative Quarter Plane. SIGMETRICS Perform. Evaluation Rev. 50(2): 42-44 (2022) - [c23]Songtao Lu, Xiaodong Cui, Mark S. Squillante, Brian Kingsbury, Lior Horesh:
Decentralized Bilevel Optimization for Personalized Client Learning. ICASSP 2022: 5543-5547 - [c22]Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, Honghao Lin, David P. Woodruff:
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. ICML 2022: 3879-3900 - [c21]Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Thinking Fast and Slow in AI: The Role of Metacognition. LOD (2) 2022: 502-509 - [c20]Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jonathan Lenchner, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Combining Fast and Slow Thinking for Human-like and Efficient Decisions in Constrained Environments. NeSy 2022: 171-185 - [c19]Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark S. Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong:
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization. NeurIPS 2022 - [c18]Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh:
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. ECML/PKDD (1) 2022: 675-692 - [c17]Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed adversarial training to robustify deep neural networks at scale. UAI 2022: 2353-2363 - [i29]Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Taher Rahgooy, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments. CoRR abs/2201.07050 (2022) - [i28]Ismail Yunus Akhalwaya, Yang-Hui He, Lior Horesh, Vishnu Jejjala, William Kirby, Kugendran Naidoo, Shashanka Ubaru:
Efficient Quantum Computation of the Fermionic Boundary Operator. CoRR abs/2201.11510 (2022) - [i27]Paz Fink Shustin, Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Haim Avron:
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty. CoRR abs/2202.05063 (2022) - [i26]Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale. CoRR abs/2206.06257 (2022) - [i25]Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis, Lior Horesh:
Towards Quantum Advantage on Noisy Quantum Computers. CoRR abs/2209.09371 (2022) - [i24]Wei Zheng Teo, Marco Carmosino, Lior Horesh:
Creating quantum-resistant classical-classical OWFs from quantum-classical OWFs. CoRR abs/2209.10146 (2022) - [i23]Yunhao Wang, Tianyuan Zheng, Lior Horesh:
From String Detection to Orthogonal Vector Problem. CoRR abs/2209.11452 (2022) - [i22]Kenneth L. Clarkson, Cristina Cornelio, Sanjeeb Dash, Joao Goncalves, Lior Horesh, Nimrod Megiddo:
Bayesian Experimental Design for Symbolic Discovery. CoRR abs/2211.15860 (2022) - [i21]Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, Andrea Loreggia:
Plansformer: Generating Symbolic Plans using Transformers. CoRR abs/2212.08681 (2022) - 2021
- [j6]Shashanka Ubaru, Lior Horesh, Guy Cohen:
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription. J. Biomed. Informatics 122: 103901 (2021) - [j5]Vassilis Kalantzis, Yuanzhe Xi, Lior Horesh:
Fast Randomized Non-Hermitian Eigensolvers Based on Rational Filtering and Matrix Partitioning. SIAM J. Sci. Comput. 43(5): S791-S815 (2021) - [c16]Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Basar, Lior Horesh:
Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning. AAAI 2021: 8767-8775 - [c15]Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate, Jonathan Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, Biplav Srivastava:
Thinking Fast and Slow in AI. AAAI 2021: 15042-15046 - [c14]Francesco Fabiano, Marianna Bergamaschi Ganapini, Lior Horesh, Andrea Loreggia, Keerthiram Murugesan, Vishal Pallagani, Francesca Rossi, Biplav Srivastava:
Epistemic Planning in a Fast and Slow Setting. TFSOCTAI@AAAI Fall Symposium 2021 - [c13]Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jonathan Lenchner, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments. TFSOCTAI@AAAI Fall Symposium 2021 - [c12]Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu, Tayfun Gokmen, Haim Avron:
Solving sparse linear systems with approximate inverse preconditioners on analog devices. HPEC 2021: 1-7 - [c11]Dong Hu, Shashanka Ubaru, Alex Gittens, Kenneth L. Clarkson, Lior Horesh, Vassilis Kalantzis:
Sparse Graph Based Sketching for Fast Numerical Linear Algebra. ICASSP 2021: 3255-3259 - [c10]Songtao Lu, Naweed Khan, Ismail Yunus Akhalwaya, Ryan Riegel, Lior Horesh, Alexander G. Gray:
Training Logical Neural Networks by Primal-Dual Methods for Neuro-Symbolic Reasoning. ICASSP 2021: 5559-5563 - [c9]Vasileios Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh, Kenneth L. Clarkson:
Projection techniques to update the truncated SVD of evolving matrices with applications. ICML 2021: 5236-5246 - [c8]Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron:
Dynamic Graph Convolutional Networks Using the Tensor M-Product. SDM 2021: 729-737 - [i20]Dong Hu, Shashanka Ubaru, Alex Gittens, Kenneth L. Clarkson, Lior Horesh, Vassilis Kalantzis:
Sparse graph based sketching for fast numerical linear algebra. CoRR abs/2102.05758 (2021) - [i19]Vassilis Kalantzis, Yuanzhe Xi, Lior Horesh:
Fast randomized non-Hermitian eigensolver based on rational filtering and matrix partitioning. CoRR abs/2103.05128 (2021) - [i18]Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu:
Solving sparse linear systems with approximate inverse preconditioners on analog devices. CoRR abs/2107.06973 (2021) - [i17]Francesco Fabiano, Biplav Srivastava, Jonathan Lenchner, Lior Horesh, Francesca Rossi, Marianna Bergamaschi Ganapini:
E-PDDL: A Standardized Way of Defining Epistemic Planning Problems. CoRR abs/2107.08739 (2021) - [i16]Shashanka Ubaru, Ismail Yunus Akhalwaya, Mark S. Squillante, Kenneth L. Clarkson, Lior Horesh:
Quantum Topological Data Analysis with Linear Depth and Exponential Speedup. CoRR abs/2108.02811 (2021) - [i15]Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler R. Josephson, Joao Goncalves, Kenneth L. Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh:
Integration of Data and Theory for Accelerated Derivable Symbolic Discovery. CoRR abs/2109.01634 (2021) - [i14]Marianna Bergamaschi Ganapini, Murray Campbell, Francesco Fabiano, Lior Horesh, Jon Lenchner, Andrea Loreggia, Nicholas Mattei, Francesca Rossi, Biplav Srivastava, Kristen Brent Venable:
Thinking Fast and Slow in AI: the Role of Metacognition. CoRR abs/2110.01834 (2021) - 2020
- [i13]Misha E. Kilmer, Lior Horesh, Haim Avron, Elizabeth Newman:
Tensor-Tensor Products for Optimal Representation and Compression. CoRR abs/2001.00046 (2020) - [i12]Vernon Austel, Cristina Cornelio, Sanjeeb Dash, Joao Goncalves, Lior Horesh, Tyler R. Josephson, Nimrod Megiddo:
Symbolic Regression using Mixed-Integer Nonlinear Optimization. CoRR abs/2006.06813 (2020) - [i11]Shashanka Ubaru, Lior Horesh, Guy Cohen:
Dynamic graph based epidemiological model for COVID-19 contact tracing data analysis and optimal testing prescription. CoRR abs/2009.04971 (2020) - [i10]Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate, Jon Lenchner, Nick Linck, Andrea Loreggia, Keerthiram Murugesan, Nicholas Mattei, Francesca Rossi, Biplav Srivastava:
Thinking Fast and Slow in AI. CoRR abs/2010.06002 (2020) - [i9]Vassilis Kalantzis, Georgios Kollias, Shashanka Ubaru, Athanasios N. Nikolakopoulos, Lior Horesh, Kenneth L. Clarkson:
Projection techniques to update the truncated SVD of evolving matrices. CoRR abs/2010.06392 (2020) - [i8]Nadiia Chepurko, Kenneth L. Clarkson, Lior Horesh, David P. Woodruff:
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra. CoRR abs/2011.04125 (2020) - [i7]Tom Achache, Lior Horesh, John A. Smolin:
Denoising quantum states with Quantum Autoencoders - Theory and Applications. CoRR abs/2012.14714 (2020)
2010 – 2019
- 2019
- [i6]Murphy Yuezhen Niu, Lior Horesh, Isaac Chuang:
Recurrent Neural Networks in the Eye of Differential Equations. CoRR abs/1904.12933 (2019) - [i5]Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron:
Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs. CoRR abs/1910.07643 (2019) - [i4]Shaokai Lin, Zichuan Wang, Lior Horesh:
Communication over Continuous Quantum Secure Dialogue using Einstein-Podolsky-Rosen States. CoRR abs/1910.08135 (2019) - 2018
- [j4]Gal Shulkind, Lior Horesh, Haim Avron:
Experimental Design for Nonparametric Correction of Misspecified Dynamical Models. SIAM/ASA J. Uncertain. Quantification 6(2): 880-906 (2018) - [i3]Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer:
Stable Tensor Neural Networks for Rapid Deep Learning. CoRR abs/1811.06569 (2018) - 2017
- [c7]Elizabeth Newman, Misha E. Kilmer, Lior Horesh:
Image classification using local tensor singular value decompositions. CAMSAP 2017: 1-5 - [i2]Elizabeth Newman, Misha E. Kilmer, Lior Horesh:
Image classification using local tensor singular value decompositions. CoRR abs/1706.09693 (2017) - 2015
- [c6]Sergiy Zhuk, Stephen Moore, Alberto Costa Nogueira Jr., Andrew A. Rawlinson, Tigran T. Tchrakian, Lior Horesh, Aleksandr Y. Aravkin, Albert Akhriev:
Source estimation for wave equations with uncertain parameters. ECC 2015: 266-270 - [c5]Stephen Moore, Devi Sudheer Chunduri, Sergiy Zhuk, Tigran T. Tchrakian, Ewout van den Berg, Albert Akhriev, Alberto Costa Nogueira Jr., Andrew A. Rawlinson, Lior Horesh:
Semi-discrete Matrix-Free Formulation of 3D Elastic Full Waveform Inversion Modeling. Euro-Par 2015: 507-518 - [c4]Haim Avron, Lior Horesh:
Community Detection Using Time-Dependent Personalized PageRank. ICML 2015: 1795-1803 - 2013
- [c3]Tara N. Sainath, Lior Horesh, Brian Kingsbury, Aleksandr Y. Aravkin, Bhuvana Ramabhadran:
Accelerating Hessian-free optimization for Deep Neural Networks by implicit preconditioning and sampling. ASRU 2013: 303-308 - [i1]Tara N. Sainath, Lior Horesh, Brian Kingsbury, Aleksandr Y. Aravkin, Bhuvana Ramabhadran:
Improving training time of Hessian-free optimization for deep neural networks using preconditioning and sampling. CoRR abs/1309.1508 (2013) - 2011
- [j3]Lior Horesh, Eldad Haber:
A Second Order Discretization of Maxwell's Equations in the Quasi-Static Regime on OcTree Grids. SIAM J. Sci. Comput. 33(5): 2805-2822 (2011) - 2010
- [c2]Dimitri Kanevsky, Avishy Carmi, Lior Horesh, Pini Gurfil, Bhuvana Ramabhadran, Tara N. Sainath:
Kalman filtering for compressed sensing. FUSION 2010: 1-8
2000 – 2009
- 2008
- [j2]Juan-Felipe P. J. Abascal, Simon R. Arridge, David Atkinson, Raya Horesh, Lorenzo Fabrizi, Marzia De Lucia, Lior Horesh, Richard H. Bayford, David S. Holder:
Use of anisotropic modelling in electrical impedance tomography; Description of method and preliminary assessment of utility in imaging brain function in the adult human head. NeuroImage 43(2): 258-268 (2008) - 2007
- [j1]O. Gilad, Lior Horesh, David S. Holder:
Design of electrodes and current limits for low frequency electrical impedance tomography of the brain. Medical Biol. Eng. Comput. 45(7): 621-633 (2007) - 2005
- [c1]Juan Fritschy, Lior Horesh, David S. Holder, Richard H. Bayford:
Applications of GRID in Clinical Neurophysiology and Electrical Impedance Tomography of Brain Function. HealthGrid 2005: 138-145
Coauthor Index
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