Search Results for author:
Found 32 papers, 4 papers with code
Randomized Algorithms for Comparison-based Search
This paper addresses the problem of finding the nearest neighbor (or one of the $R$-nearest neighbors) of a query object $q$ in a database of $n$ objects, when we can only use a comparison oracle.
Straggler Mitigation in Distributed Optimization Through Data Encoding
Slow running or straggler tasks can significantly reduce computation speed in distributed computation.
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation.
Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
Data privacy is an important concern in machine learning, and is fundamentally at odds with the task of training useful learning models, which typically require the acquisition of large amounts of private user data.
Differentially Private Consensus-Based Distributed Optimization
Consensus-based optimization consists of a set of computational nodes arranged in a graph, each having a local objective that depends on their local data, where in every step nodes take a linear combination of their neighbors' messages, as well as taking a new gradient step.
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations
Communication bottleneck has been identified as a significant issue in distributed optimization of large-scale learning models.
Data Encoding for Byzantine-Resilient Distributed Optimization
In this paper, we propose a method based on data encoding and error correction over real numbers to combat adversarial attacks.
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization
In this paper, we propose and analyze SPARQ-SGD, which is an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models.
On Distributed Quantization for Classification
We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that are gathered from distributed nodes through communication constrained channels.
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
Communication bottleneck has been identified as a significant issue in distributed optimization of large-scale learning models.
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization
In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm for decentralized training of large-scale machine learning models over a network.
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data
In order to be able to apply their filtering procedure in our {\em heterogeneous} data setting where workers compute {\em stochastic} gradients, we derive a new matrix concentration result, which may be of independent interest.
Successive Refinement of Privacy
This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)?
Byzantine-Resilient High-Dimensional Federated Learning
To combat the adversary, we employ an efficient high-dimensional robust mean estimation algorithm from Steinhardt et al.~\cite[ITCS 2018]{Resilience_SCV18} at the server to filter-out corrupt vectors; and to analyze the outlier-filtering procedure, we develop a novel matrix concentration result that may be of independent interest.
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs
We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework.
Quantizing data for distributed learning
In this paper, we propose an alternate approach to learn from distributed data that quantizes data instead of gradients, and can support learning over applications where the size of gradient updates is prohibitive.
QuPeL: Quantized Personalization with Applications to Federated Learning
When each client participating in the (federated) learning process has different requirements of the quantized model (both in value and precision), we formulate a quantized personalization framework by introducing a penalty term for local client objectives against a globally trained model to encourage collaboration.
On the Renyi Differential Privacy of the Shuffle Model
The central question studied in this paper is Renyi Differential Privacy (RDP) guarantees for general discrete local mechanisms in the shuffle privacy model.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy.
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeD that facilitates collective (personalized model compression) training via \textit{knowledge distillation} (KD) among clients who have access to heterogeneous data and resources.
Coded Estimation: Design of Backscatter Array Codes for 3D Orientation Estimation
We consider the problem of estimating the orientation of a 3D object with the assistance of configurable backscatter tags.
Decentralized Multi-Task Stochastic Optimization With Compressed Communications
The paper develops algorithms and obtains performance bounds for two different models of local information availability at the nodes: (i) sample feedback, where each node has direct access to samples of the local random variable to evaluate its local cost, and (ii) bandit feedback, where samples of the random variables are not available, but only the values of the local cost functions at two random points close to the decision are available to each node.
On Leave-One-Out Conditional Mutual Information For Generalization
We derive information theoretic generalization bounds for supervised learning algorithms based on a new measure of leave-one-out conditional mutual information (loo-CMI).
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms.
Differentially Private Stochastic Linear Bandits: (Almost) for Free
In the shuffled model, we also achieve regret of $\tilde{O}(\sqrt{T}+\frac{1}{\epsilon})$ %for small $\epsilon$ as in the central case, while the best previously known algorithm suffers a regret of $\tilde{O}(\frac{1}{\epsilon}{T^{3/5}})$.
HQAlign: Aligning nanopore reads for SV detection using current-level modeling
Moreover, HQAlign improves the alignment rate to 89. 35% from minimap2 85. 64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86. 65% from 83. 48% for nanopore reads alignment to GRCh37 human genome.
Multi-Message Shuffled Privacy in Federated Learning
This also resolves an open question on the optimal trade-off for private vector sum in the MMS model.
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression
Assuming a representation structure for the data generating linear models at the sources and the target domains, we propose a representation transfer based learning method for constructing the target model.
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics.
Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored.
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications.
