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Found 26 papers, 6 papers with code
Federated Learning with Reduced Information Leakage and Computation
Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data.
Fair Classifiers that Abstain without Harm
To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner.
Professional Basketball Player Behavior Synthesis via Planning with Diffusion
To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling.
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts
We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data.
DensePure: Understanding Diffusion Models towards Adversarial Robustness
By using the highest density point in the conditional distribution as the reversed sample, we identify the robust region of a given instance under the diffusion model's reverse process.
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks
In this paper, we present the first study of security issues of MSF-based perception in AD systems.
Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?
Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm.
How Do Fair Decisions Fare in Long-term Qualification?
Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions.
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Several works have shown this vulnerability via adversarial attacks, but existing approaches on improving the robustness of DRL under this setting have limited success and lack for theoretical principles.
Fairness in Learning-Based Sequential Decision Algorithms: A Survey
However, in practice most decision-making processes are of a sequential nature, where decisions made in the past may have an impact on future data.
Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms
It can be shown that the privacy-accuracy tradeoff can be improved significantly compared with conventional ADMM.
Characterizing Attacks on Deep Reinforcement Learning
To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions.
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over time, and exacerbate representation disparity in the long run.
Distributed Learning of Average Belief Over Networks Using Sequential Observations
This paper addresses the problem of distributed learning of average belief with sequential observations, in which a network of $n>1$ agents aim to reach a consensus on the average value of their beliefs, by exchanging information only with their neighbors.
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
In this paper, we aim to characterize adversarial examples based on spatial context information in semantic segmentation.
MeshAdv: Adversarial Meshes for Visual Recognition
Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications.
Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms
Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems.
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed on local data with the output exchanged among neighbors in an iterative fashion.
Spatially Transformed Adversarial Examples
Perturbations generated through spatial transformation could result in large $\mathcal{L}_p$ distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems.
Generating Adversarial Examples with Adversarial Networks
A challenge to explore adversarial robustness of neural networks on MNIST.
An Online Approach to Dynamic Channel Access and Transmission Scheduling
A natural remedy is a learning framework, which has also been extensively studied in the same context, but a typical learning algorithm in this literature seeks only the best static policy, with performance measured by weak regret, rather than learning a good dynamic channel access policy.
Group Learning and Opinion Diffusion in a Broadcast Network
We analyze the following group learning problem in the context of opinion diffusion: Consider a network with $M$ users, each facing $N$ options.
Online Learning in a Contract Selection Problem
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution.
Optimal Adaptive Learning in Uncontrolled Restless Bandit Problems
In an uncontrolled restless bandit problem, there is a finite set of arms, each of which when pulled yields a positive reward.
Online Algorithms for the Multi-Armed Bandit Problem with Markovian Rewards
The player receives a state-dependent reward each time it plays an arm.
