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Found 20 papers, 3 papers with code
UFID: A Unified Framework for Input-level Backdoor Detection on Diffusion Models
Diffusion Models are vulnerable to backdoor attacks, where malicious attackers inject backdoors by poisoning some parts of the training samples during the training stage.
Learning the Topology and Behavior of Discrete Dynamical Systems
On the positive side, we present efficient learning methods under the PAC model when the underlying graph of the dynamical system belongs to some classes.
Sample Complexity of Opinion Formation on Networks
In this paper, we initialize the study of sample complexity in opinion formation to solve this problem.
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
In this paper, we study the problem of causal effect estimation with treatment entangled in a graph.
Spatial-Temporal Networks for Antibiogram Pattern Prediction
In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future.
Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems
Fixed points of such dynamical systems represent configurations to which the system converges.
High-resolution synthetic residential energy use profiles for the United States
However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare.
Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays
However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem.
Differentially Private Partial Set Cover with Applications to Facility Location
Using our algorithm for Partial Set Cover as a subroutine, we give a differentially private (bicriteria) approximation algorithm for a facility location problem which generalizes $k$-center/$k$-supplier with outliers.
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response
In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2.
Differentially Private Community Detection for Stochastic Block Models
To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection.
Fair Disaster Containment via Graph-Cut Problems
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.
Differentially Private Densest Subgraph Detection
We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private.
Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model
The problem of finding dense components of a graph is a widely explored area in data analysis, with diverse applications in fields and branches of study including community mining, spam detection, computer security and bioinformatics.
Mapping Network States Using Connectivity Queries
In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain.
PAC-Learning for Strategic Classification
In this paper, we generalize both of these through a unified framework for strategic classification, and introduce the notion of strategic VC-dimension (SVC) to capture the PAC-learnability in our general strategic setup.
Models for COVID-19 Pandemic: A Comparative Analysis
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years.
Stochastic Optimization and Learning for Two-Stage Supplier Problems
The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint.
Data Structures and Algorithms
Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability
Improving the explainability of the results from machine learning methods has become an important research goal.
