Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

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The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Benchmarking of scATAC-seq protocols demonstrates the effects of data quality on downstream analyses. ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Vehicle sharing systems (VSSs) allow users to rent vehicles for a short period of time, in a more flexible and convenient manner compared to the traditional vehicle rental services. The long-term VSS subscription replaces the need for contract signing for ...

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

It has recently been speculated that long-time average quantities of hyperchaotic dissipative systems may be approximated by weighted sums over unstable invariant tori embedded in the attractor, analogous to equivalent sums over periodic orbits, which are ...

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

Coverage-guided greybox fuzzers rely on control-flow coverage feedback to explore a target program and uncover bugs. Compared to control-flow coverage, data-flow coverage offers a more fine-grained approximation of program behavior. Data-flow coverage capt ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

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Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices

Robust Collaborative Learning with Linear Gradient Overhead

Systematic benchmarking of single-cell ATAC-sequencing protocols

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Demand-based operations of vehicle sharing systems

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

Predicting chaotic statistics with unstable invariant tori

Probabilistic methods for neural combinatorial optimization

DatAFLow: Toward a Data-Flow-Guided Fuzzer

Scalable and Privacy-Preserving Federated Principal Component Analysis

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Sparse Autoencoders for Speech Modeling and Recognition