arXiv subjects

Neel Guha

Publications and source records attributed to Neel Guha.

23 records · Page 2Linked to original sources

arXiv2019

Machine Learning for AC Optimal Power Flow

We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.

cs.LG↗

arXiv2019

Model Aggregation via Good-Enough Model Spaces

In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to communication, privacy, or storage constraints. Existing distributed optimization methods for learning global models in these settings typically aggregate local updates from each node in an iterative fashion. However, these approaches require many rounds of communication between nodes, and assume that updates can be synchronously shared across a connected network. In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global model by carefully intersecting the sets of "good-enough" models across each node. Our approach utilizes minimal communication and does not require sharing of data between nodes. We present methods for learning both convex models and neural networks within this framework and discuss how small samples of held-out data can be used for post-learning fine-tuning. In experiments on image and medical datasets, our approach on average improves upon other baseline aggregation techniques such as ensembling or model averaging by as much as 15 points (accuracy).

cs.LG↗

arXiv2019

One-Shot Federated Learning

We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing on ensemble learning and knowledge aggregation - achieves an average relative gain of 51.5% in AUC over local baselines and comes within 90.1% of the (unattainable) global ideal. We discuss these methods and identify several promising directions of future work.

cs.LG↗

arXiv2017

Semantic Identification of Web Browsing Sessions

We introduce a semantic identification attack, in which an adversary uses semantic signals about the pages visited in one browsing session to identify other browsing sessions launched by the same user. Current user fingerprinting methods fail when a single machine is used by multiple users (e.g., in cybercafes or spaces with public computers) as these methods fingerprint devices, not individuals. We demonstrate how an adversary can employ a SIA to successfully fingerprint users on public or shared machines and identify them across browsing sessions. We additionally describe and evaluate possible countermeasures to prevent identification.

cs.CR↗

arXiv2016

Semantic Identification Attacks on Web Browsing

We introduce a Semantic Identification Attack, in which an adversary uses semantic signals about the pages visited in one browsing session to identify other browsing sessions launched by the same user. This attack allows an adver- sary to determine if two browsing sessions originate from the same user regardless of any measures taken by the user to disguise their browser or network. We use the MSNBC Anonymous Browsing data set, which contains a large set of user visits (labeled by category) to implement such an attack and show that even very coarse semantic information is enough to identify users. We discuss potential counter- measures users can take to defend against this attack.

cs.CR↗