34 citations · 163 across the 27 of their papers we have counts for
33 papers
Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
Evelyn Duesterwald, Benjamin Elder, Lilian Ngweta +2
Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains largely unaddressed. When given…
Counting Triangles of Graphs via Randomized Trace Estimation with Incomplete Matrix-Vector Products
Soumyadip Ghosh, Lior Horesh, Vasileios Kalantzis +3
Counting triangles in graphs is a fundamental operation in network analysis, underpinning metrics such as clustering coefficients and serving as a signal for community detection, l…
Analysis of Power Iteration Algorithm with Partially Observed Matrix-vector Products
Soumyadip Ghosh, Lior Horesh, Vassilis Kalantzis +3
We consider the problem of computing the dominant eigenvector of a symmetric matrix via the power iteration algorithm subject to constraints in the computation of matrix-vector pr…
Exact Symmetry as Algebra: Group-Structured Tensors that Enforce Physical Selection Rules
Paulina Hoyos, Shashanka Ubaru, Dongsung Huh +5
Symmetry determines which physical responses of molecules and crystals are allowed and underlies many models used throughout the physical sciences. Yet several widely used machine-…
Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates
Ramón Nartallo-Kaluarachchi, Shashanka Ubaru, Małgorzata J Zimoń +4
Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inherit from imperfect reward estima…
Fast Linear Solvers via AI-Tuned Markov Chain Monte Carlo-based Matrix Inversion
Anton Lebedev, Won Kyung Lee, Soumyadip Ghosh +7
Large, sparse linear systems are pervasive in modern science and engineering, and Krylov subspace solvers are an established means of solving them. Yet convergence can be slow for…
Transformers Learn Faster with Semantic Focus
Parikshit Ram, Kenneth L. Clarkson, Tim Klinger +2
Various forms of sparse attention have been explored to mitigate the quadratic computational and memory cost of the attention mechanism in transformers. We study sparse transformer…
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
Arpan Mukherjee, Shashanka Ubaru, Keerthiram Murugesan +2
This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving…
Comparing quantum and classical Monte Carlo algorithms for estimating Betti numbers of clique complexes
Ismail Yunus Akhalwaya, Ahmed Bhayat, Adam Connolly +4
Several quantum and classical Monte Carlo algorithms for Betti Number Estimation (BNE) on clique complexes have recently been proposed, though it is unclear how their performances…
Multivariate trace estimation using quantum state space linear algebra
Liron Mor Yosef, Shashanka Ubaru, Lior Horesh +1
In this paper, we present a quantum algorithm for approximating multivariate traces, i.e. the traces of matrix products. Our research is motivated by the extensive utility of multi…
On Efficient Solutions of General Structured Markov Processes in Quantum Computational Environments
Vasileios Kalantzis, Mark S. Squillante, Shashanka Ubaru
We study from a theoretical viewpoint the fundamental problem of efficiently computing the stationary distribution of general classes of structured Markov processes. In strong cont…
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation
Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru +12
Numerical computation is essential to many areas of artificial intelligence (AI), whose computing demands continue to grow dramatically, yet their continued scaling is jeopardized…
Capacity Analysis of Vector Symbolic Architectures
Kenneth L. Clarkson, Shashanka Ubaru, Elizabeth Yang
Hyperdimensional computing (HDC) is a biologically-inspired framework which represents symbols with high-dimensional vectors, and uses vector operations to manipulate them. The ens…
Topological data analysis on noisy quantum computers
Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson +6
Topological data analysis (TDA) is a powerful technique for extracting complex and valuable shape-related summaries of high-dimensional data. However, the computational demands of…
Randomized matrix-free quadrature: unified and uniform bounds for stochastic Lanczos quadrature and the kernel polynomial method
Tyler Chen, Thomas Trogdon, Shashanka Ubaru
We analyze randomized matrix-free quadrature algorithms for spectrum and spectral sum approximation. The algorithms studied include the kernel polynomial method and stochastic Lanc…
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
Paz Fink Shustin, Shashanka Ubaru, Małgorzata J. Zimoń +4
Learning data representations under uncertainty is an important task that emerges in numerous scientific computing and data analysis applications. However, uncertainty quantificati…
Representation of the Fermionic Boundary Operator
Ismail Yunus Akhalwaya, Yang-Hui He, Lior Horesh +4
The boundary operator is a linear operator that acts on a collection of high-dimensional binary points (simplices) and maps them to their boundaries. This boundary map is one of th…
Efficient Scaling of Dynamic Graph Neural Networks
Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, Saurabh Raje +3
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this…
Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
Shashanka Ubaru, Ismail Yunus Akhalwaya, Mark S. Squillante +2
Quantum computing offers the potential of exponential speedups for certain classical computations. Over the last decade, many quantum machine learning (QML) algorithms have been pr…
Analysis of stochastic Lanczos quadrature for spectrum approximation
Tyler Chen, Thomas Trogdon, Shashanka Ubaru
The cumulative empirical spectral measure (CESM) of a symmetric matrix is defined as the fraction of eigenvalues of…
Sparse graph based sketching for fast numerical linear algebra
Dong Hu, Shashanka Ubaru, Alex Gittens +3
In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such a…
Projection techniques to update the truncated SVD of evolving matrices
Vassilis Kalantzis, Georgios Kollias, Shashanka Ubaru +3
This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such…
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription
Shashanka Ubaru, Lior Horesh, Guy Cohen
In this study, we address three important challenges related to disease transmissions such as the COVID-19 pandemic, namely, (a) providing an early warning to likely exposed indivi…
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar +1
In modern multilabel classification problems, each data instance belongs to a small number of classes from a large set of classes. In other words, these problems involve learning v…