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07/21/2023
Methodologies for Improving Modern Industrial Recommender Systems
Recommender system (RS) is an established technology with successful app...
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05/24/2023
RefGPT: Reference -> Truthful Customized Dialogues Generation by GPTs and for GPTs
General chat models, like ChatGPT, have attained impressive capability t...
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02/09/2023
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint
Online platforms often incentivize consumers to improve user engagement ...
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08/04/2022
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents
We study multi-agent reinforcement learning (MARL) with centralized trai...
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04/06/2022
Federated Reinforcement Learning with Environment Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem in which n a...
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12/21/2019
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epide...
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10/21/2019
Communication Efficient Decentralized Training with Multiple Local Updates
Communication efficiency plays a significant role in decentralized optim...
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09/24/2019
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping
Random feature mapping (RFM) is a popular method for speeding up kernel ...
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09/24/2019
Matrix Sketching for Secure Collaborative Machine Learning
Collaborative machine learning (ML), also known as federated ML, allows ...
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07/04/2019
On the Convergence of FedAvg on Non-IID Data
Federated learning enables a large amount of edge computing devices to l...
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02/13/2019
Do Subsampled Newton Methods Work for High-Dimensional Data?
Subsampled Newton methods approximate Hessian matrices through subsampli...
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11/06/2018
OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix m...
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06/03/2018
Alchemist: An Apache Spark <=> MPI Interface
The Apache Spark framework for distributed computation is popular in the...
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05/30/2018
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
Apache Spark is a popular system aimed at the analysis of large data set...
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03/21/2018
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
Over the course of the past decade, a variety of randomized algorithms h...
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10/11/2017
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
Conventional seismic techniques for detecting the subsurface geologic fe...
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09/11/2017
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
For distributed computing environments, we consider the canonical machin...
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08/06/2017
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
In recent years, randomized methods for numerical linear algebra have re...
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06/09/2017
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Kernel k-means clustering can correctly identify and extract a far more ...
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02/16/2017
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
We address the statistical and optimization impacts of using classical s...
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12/26/2014
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion
Low-rank matrix completion is an important problem with extensive real-w...
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03/30/2014
Sharpened Error Bounds for Random Sampling Based â„“_2 Regression
Given a data matrix X ∈ R^n× d and a response vector y ∈ R^n, suppose n>...
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10/04/2012
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
The CUR matrix decomposition is an important extension of Nyström approx...
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04/19/2012
