Search Results for author:
Found 28 papers, 0 papers with code
Stochastic Controlled Averaging for Federated Learning with Communication Compression
The existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression.
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services
Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items.
Differentially Private One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Minwise hashing (MinHash) is a standard algorithm widely used in the industry, for large-scale search and learning applications with the binary (0/1) Jaccard similarity.
Differential Privacy with Random Projections and Sign Random Projections
Among the presented algorithms, iDP-SignRP is remarkably effective under the setting of ``individual differential privacy'' (iDP), based on sign random projections (SignRP).
Building K-Anonymous User Cohorts with\\ Consecutive Consistent Weighted Sampling (CCWS)
To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity.
OPORP: One Permutation + One Random Projection
We show that the estimation variance is essentially: $(s-1)A + \frac{D-k}{D-1}\frac{1}{k}\left[ (1-\rho^2)^2 -2A\right]$, where $A\geq 0$ is a function of the data ($u, v$).
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression
Moreover, we develop a new analysis of the EF under partial client participation, which is an important scenario in FL.
$k$-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
When designing clustering algorithms, the choice of initial centers is crucial for the quality of the learned clusters.
On Distributed Adaptive Optimization with Gradient Compression
We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm.
Communication-Efficient TeraByte-Scale Model Training Framework for Online Advertising
In this paper, we propose a hardware-aware training workflow that couples the hardware topology into the algorithm design.
C-OPH: Improving the Accuracy of One Permutation Hashing (OPH) with Circulant Permutations
Note that C-MinHash is different from the well-known work on "One Permutation Hashing (OPH)" published in NIPS'12.
Layer-wise and Dimension-wise Locally Adaptive Federated Learning
In the emerging paradigm of Federated Learning (FL), large amount of clients such as mobile devices are used to train possibly high-dimensional models on their respective data.
k-Median Clustering via Metric Embedding: Towards Better Initialization with Privacy
Our method, named the HST initialization, can also be easily extended to the setting of differential privacy (DP) to generate private initial centers.
Revisiting Locality-Sensitive Binary Codes from Random Fourier Features
We show the locality-sensitivity of SignRFF, and propose a new measure, called ranking efficiency, to theoretically compare different Locality-Sensitive Hashing (LSH) methods with practical implications.
C-MinHash: Improving Minwise Hashing with Circulant Permutation
Minwise hashing (MinHash) is an important and practical algorithm for generating random hashes to approximate the Jaccard (resemblance) similarity in massive binary (0/1) data.
Toward Communication Efficient Adaptive Gradient Method
While adaptive gradient methods have been proven effective for training neural nets, the study of adaptive gradient methods in federated learning is scarce.
C-MinHash: Practically Reducing Two Permutations to Just One
That is, one single permutation is used for both the initial pre-processing step to break the structures in the data and the circulant hashing step to generate $K$ hashes.
C-MinHash: Rigorously Reducing $K$ Permutations to Two
Unlike classical MinHash, these $K$ hashes are obviously correlated, but we are able to provide rigorous proofs that we still obtain an unbiased estimate of the Jaccard similarity and the theoretical variance is uniformly smaller than that of the classical MinHash with $K$ independent permutations.
Quantization Algorithms for Random Fourier Features
Closely related to RP, the method of random Fourier features (RFF) has also become popular, for approximating the Gaussian kernel.
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching
Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices.
Randomized Kernel Multi-view Discriminant Analysis
In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-view discriminant analysis (KMvDA).
IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation
Feature selection is an important tool to deal with high dimensional data.
Random Projections with Asymmetric Quantization
The method of random projection has been a popular tool for data compression, similarity search, and machine learning.
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Jaccard similarity is widely used as a distance measure in many machine learning and search applications.
Generalization Error Analysis of Quantized Compressive Learning
In this paper, we consider the learning problem where the projected data is further compressed by scalar quantization, which is called quantized compressive learning.
Zeroth Order Optimization by a Mixture of Evolution Strategies
Perhaps the only methods that enjoy convergence guarantees are the ones that sample the perturbed points uniformly from a unit sphere or from a multivariate Gaussian distribution with an isotropic covariance.
An Optimistic Acceleration of AMSGrad for Nonconvex Optimization
We propose a new variant of AMSGrad, a popular adaptive gradient based optimization algorithm widely used for training deep neural networks.
