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Found 19 papers, 0 papers with code
Optimization on Pareto sets: On a theory of multi-objective optimization
But as the set of Pareto optimal vectors can be very large, we further consider a more practically significant Pareto-constrained optimization problem, where the goal is to optimize a preference function constrained to the Pareto set.
Online covariance estimation for stochastic gradient descent under Markovian sampling
We investigate the online overlapping batch-means covariance estimator for Stochastic Gradient Descent (SGD) under Markovian sampling.
Fairness Uncertainty Quantification: How certain are you that the model is fair?
We provide online multiplier bootstrap method to estimate the asymptotic covariance to construct online CI.
Sketch2FullStack: Generating Skeleton Code of Full Stack Website and Application from Sketch using Deep Learning and Computer Vision
As a result, the efficiency of the development team is significantly reduced when it comes to converting UI wireframes and database schemas into an actual working system.
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
We study stochastic optimization algorithms for constrained nonconvex stochastic optimization problems with Markovian data.
Plagiarism Detection in the Bengali Language: A Text Similarity-Based Approach
Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only.
Predictive Closed-Loop Service Automation in O-RAN based Network Slicing
To cater to the dynamic service requirements of these verticals and meet the required quality-of-service (QoS) mentioned in the service-level agreement (SLA), network slices need to be isolated through dedicated elements and resources.
Predictive Price-Performance Optimization for Serverless Query Processing
We present an efficient, parametric modeling framework for predictive resource allocations, focusing on the amount of computational resources, that can optimize for a range of price-performance objectives for data analytics in serverless query processing settings.
Phoebe: A Learning-based Checkpoint Optimizer
To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer.
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data
In this work, we establish risk bounds for the Empirical Risk Minimization (ERM) with both dependent and heavy-tailed data-generating processes.
Escaping Saddle-Point Faster under Interpolation-like Conditions
We next analyze Stochastic Cubic-Regularized Newton (SCRN) algorithm under interpolation-like conditions, and show that the oracle complexity to reach an $\epsilon$-local-minimizer under interpolation-like conditions, is $O(1/\epsilon^{2. 5})$.
Escaping Saddle-Points Faster under Interpolation-like Conditions
We next analyze Stochastic Cubic-Regularized Newton (SCRN) algorithm under interpolation-like conditions, and show that the oracle complexity to reach an $\epsilon$-local-minimizer under interpolation-like conditions, is $\tilde{\mathcal{O}}(1/\epsilon^{2. 5})$.
Intelligent O-RAN for Beyond 5G and 6G Wireless Networks
Building on the principles of openness and intelligence, there has been a concerted global effort from the operators towards enhancing the radio access network (RAN) architecture.
Online and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization
We establish sub-linear regret bounds on the proposed notions of regret in both the online and bandit setting.
Suspicion-Free Adversarial Attacks on Clustering Algorithms
To the best of our knowledge, this is the first work that generates spill-over adversarial samples without the knowledge of the true metric ensuring that the perturbed sample is not an outlier, and theoretically proves the above.
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization
In this paper, motivated by online reinforcement learning problems, we propose and analyze bandit algorithms for both general and structured nonconvex problems with nonstationary (or dynamic) regret as the performance measure, in both stochastic and non-stochastic settings.
Stochastic Zeroth-order Discretizations of Langevin Diffusions for Bayesian Inference
Our theoretical contributions extend the practical applicability of sampling algorithms to the noisy black-box and high-dimensional settings.
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models
We first provide a strong (iterative) black-box adversarial attack that can craft adversarial samples which will be incorrectly clustered irrespective of the choice of clustering algorithm.
