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Found 23 papers, 4 papers with code
Streaming Submodular Maximization under a k-Set System Constraint
Moreover, we propose the first streaming algorithms for monotone submodular maximization subject to $k$-extendible and $k$-system constraints.
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments
Navigating autonomously in marine environments including dynamic and static obstacles, and strong flow disturbances, such as in high-flow rivers, poses significant challenges for USVs.
Complementing Semi-Supervised Learning with Uncertainty Quantification
To alleviate this problem semi-supervised learning (SSL) leverages the knowledge of the classifier on the labeled domain and extrapolates it to the unlabeled domain which has a supposedly similar distribution as annotated data.
CTIN: Robust Contextual Transformer Network for Inertial Navigation
Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements.
The Power of Subsampling in Submodular Maximization
We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings.
Generating Structured Adversarial Attacks Using Frank-Wolfe Method
White box adversarial perturbations are generated via iterative optimization algorithms most often by minimizing an adversarial loss on a $\ell_p$ neighborhood of the original image, the so-called distortion set.
Ensemble-based Adversarial Defense Using Diversified Distance Mapping
We propose an ensemble-based defense against adversarial examples using distance map layers (DMLs).
Trace-Norm Adversarial Examples
White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a $l_p$ neighborhood of the original image, the so-called distortion set.
Submodular Maximization in Clean Linear Time
In this paper, we provide the first deterministic algorithm that achieves the tight $1-1/e$ approximation guarantee for submodular maximization under a cardinality (size) constraint while making a number of queries that scales only linearly with the size of the ground set $n$.
On Adversarial Bias and the Robustness of Fair Machine Learning
Optimizing prediction accuracy can come at the expense of fairness.
Regularized Submodular Maximization at Scale
In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$.
Submodular Maximization Through Barrier Functions
In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization.
Streaming Submodular Maximization under a $k$-Set System Constraint
In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization.
Nektar++: enhancing the capability and application of high-fidelity spectral/$hp$ element methods
Nektar++ is an open-source framework that provides a flexible, high-performance and scalable platform for the development of solvers for partial differential equations using the high-order spectral/$hp$ element method.
Mathematical Software Numerical Analysis Numerical Analysis Fluid Dynamics
Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
We show how one can achieve the tight $(1/2)$-approximation guarantee with $O(k)$ shared memory while minimizing not only the required rounds of computations but also the total number of communicated bits.
Adaptive Sequence Submodularity
In many machine learning applications, one needs to interactively select a sequence of items (e. g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e. g., guiding an agent through a series of states).
Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems
To the best of our knowledge, we are the first to provide stochastic and deterministic accelerated extension of APCD algorithms for general nonconvex and nonsmooth problems ensuring that for both bounded delays and unbounded delays every limit point is a critical point.
Eliminating Latent Discrimination: Train Then Mask
How can we control for latent discrimination in predictive models?
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation?
Data Summarization at Scale: A Two-Stage Submodular Approach
The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset.
Do Less, Get More: Streaming Submodular Maximization with Subsampling
In this paper, we develop the first one-pass streaming algorithm for submodular maximization that does not evaluate the entire stream even once.
Comparison Based Learning from Weak Oracles
More specifically, we aim at devising efficient algorithms to locate a target object in a database equipped with a dissimilarity metric via invocation of the weak comparison oracle.
Deletion-Robust Submodular Maximization at Scale
Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation.
