15 citations · 57 across the 29 of their papers we have counts for
29 papers
Connecting Quantum Computing with Classical Stochastic Simulation
Jose Blanchet, Mark S. Squillante, Mario Szegedy +1
This tutorial paper introduces quantum approaches to Monte Carlo computation with applications in computational finance. We outline the basics of quantum computing using Grover's a…
Stable Iterative Solvers for Ill-conditioned Linear Systems
Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu
Iterative solvers for large-scale linear systems such as Krylov subspace methods can diverge when the linear system is ill-conditioned, thus significantly reducing the applicabilit…
On Mixed-Precision Iterative Methods and Analysis for Nearly Completely Decomposable Markov Processes
Vasileios Kalantzis, Mark S. Squillante, Chai Wah Wu
In this paper we consider the problem of computing the stationary distribution of nearly completely decomposable Markov processes, a well-established area in the classical theory o…
Breaking through the classical Shannon entropy limit: A new frontier through logical semantics
Luis A. Lastras, Barry M. Trager, Jonathan Lenchner +4
Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key asp…
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
Weiqin Chen, Mark S. Squillante, Chai Wah Wu +1
We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as…
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…
Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models
Sanjeeb Dash, Soumyadip Ghosh, Joao Goncalves +1
Building explainable machine learning models is crucial for human users to be able to interpret the proposed statistical relationship obtained from the training data. Mixed-integer…
Stable iterative refinement algorithms for solving linear systems
Chai Wah Wu, Mark S. Squillante, Vasileios Kalantzis +1
Iterative refinement (IR) is a popular scheme for solving a linear system of equations based on gradually improving the accuracy of an initial approximation. Originally developed t…
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes
Peizhong Ju, Sen Lin, Mark S. Squillante +2
Transfer learning is a useful technique for achieving improved performance and reducing training costs by leveraging the knowledge gained from source tasks and applying it to targe…
Towards a Unification of Logic and Information Theory
Luis A. Lastras, Barry Trager, Jonathan Lenchner +5
Today, the vast majority of the world's digital information is represented using the fundamental assumption, introduced by Claude Shannon in 1948, that ``...the semantic aspects of…
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…
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality
Xuhui Zhang, Jose Blanchet, Soumyadip Ghosh +1
We study the problem of transfer learning, observing that previous efforts to understand its information-theoretic limits do not fully exploit the geometric structure of the source…
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…
Solving sparse linear systems with approximate inverse preconditioners on analog devices
Vasileios Kalantzis, Anshul Gupta, Lior Horesh +3
Sparse linear system solvers are computationally expensive kernels that lie at the heart of numerous applications. This paper proposes a flexible preconditioning framework to subst…
Unbiased Gradient Estimation for Distributionally Robust Learning
Soumyadip Ghosh, Mark Squillante
Seeking to improve model generalization, we consider a new approach based on distributionally robust learning (DRL) that applies stochastic gradient descent to the outer minimizati…
Optimal Scheduling Control in Fluid Models of General Input-Queued Switches
Yingdong Lu, Mark S. Squillante, Tonghoon Suk
Most of the early input-queued switch research focused on establishing throughput optimality of the max-weight scheduling policy, with some recent research showing that max-weight…
On Heavy-Traffic Optimal Scaling of -Weighted MaxWeight Scheduling in Input-Queued Switches
Yingdong Lu, Siva Theja Maguluri, Mark S. Squillante +1
We consider the optimal control of input-queued switches under a cost-weighted variant of MaxWeight scheduling, for which we establish theoretical properties that include showing t…
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framew…
Optimising capacity allocation in networks of stochastic loss systems: A functional-form approach
Brendan Patch, Mark S. Squillante, Peter M. Van de Ven
Motivated by a wide variety of applications, this paper introduces a general class of networks of stochastic loss systems in which congestion renders lost revenue due to customers…
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
Tsui-Wei Weng, Pin-Yu Chen, Lam M. Nguyen +3
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important…
Efficient Stochastic Gradient Descent for Learning with Distributionally Robust Optimization
Soumyadip Ghosh, Mark Squillante, Ebisa Wollega
Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization. These min-max formu…
A General Family of Robust Stochastic Operators for Reinforcement Learning
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors. V…
Control of Time-Varying Epidemic-Like Stochastic Processes and Their Mean-Field Limits
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
The optimal control of epidemic-like stochastic processes is important both historically and for emerging applications today, where it can be especially important to include time-v…