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Found 33 papers, 10 papers with code
On the Convergence of FedAvg on Non-IID Data
In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex and smooth problems, where $T$ is the number of SGDs.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT
Therefore, we propose a method called RefGPT to generate enormous truthful and customized dialogues without worrying about factual errors caused by the model hallucination.
Federated Reinforcement Learning with Environment Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction.
A Practical Guide to Randomized Matrix Computations with MATLAB Implementations
In recent years, a bunch of randomized algorithms have been devised to make matrix computations more scalable.
OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage.
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction
To mitigate the catastrophic forgetting issue, we design the consistency regularization loss to make better use of the pseudo-labels and jointly train the model with both unsupervised and supervised data.
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration
In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction.
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method.
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
In particular, there is a bias-variance trade-off in sketched MRR that is not present in sketched LSR.
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
As a more practical alternative, we propose a bootstrap method to compute a posteriori error estimates for randomized LS algorithms.
Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage.
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
In recent years, randomized methods for numerical linear algebra have received growing interest as a general approach to large-scale problems.
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
This work analyzes the application of this paradigm to kernel $k$-means clustering, and shows that applying the linear $k$-means clustering algorithm to $\frac{k}{\epsilon} (1 + o(1))$ features constructed using a so-called rank-restricted Nystr\"om approximation results in cluster assignments that satisfy a $1 + \epsilon$ approximation ratio in terms of the kernel $k$-means cost function, relative to the guarantee provided by the same algorithm without the use of the Nystr\"om method.
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
The Nystr\"om method is a special instance of our fast model and is approximation to the prototype model.
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
In this paper we conduct in-depth studies of an SPSD matrix approximation model and establish strong relative-error bounds.
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion
Low-rank matrix completion is an important problem with extensive real-world applications.
Sharpened Error Bounds for Random Sampling Based $\ell_2$ Regression
Given a data matrix $X \in R^{n\times d}$ and a response vector $y \in R^{n}$, suppose $n>d$, it costs $O(n d^2)$ time and $O(n d)$ space to solve the least squares regression (LSR) problem.
Efficient Algorithms and Error Analysis for the Modified Nystrom Method
Recently, a variant of the Nystr\"om method called the modified Nystr\"om method has demonstrated significant improvement over the standard Nystr\"om method in approximation accuracy, both theoretically and empirically.
Improving CUR Matrix Decomposition and the Nyström Approximation via Adaptive Sampling
The CUR matrix decomposition and the Nystr\"{o}m approximation are two important low-rank matrix approximation techniques.
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
The CUR matrix decomposition is an important extension of Nyström approximation to a general matrix.
Do Subsampled Newton Methods Work for High-Dimensional Data?
Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information.
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping
On the one hand, our theories are based on weak and valid assumptions.
Matrix Sketching for Secure Collaborative Machine Learning
Collaborative learning allows participants to jointly train a model without data sharing.
Communication-Efficient Local Decentralized SGD Methods
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication.
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.
Fast Generalized Matrix Regression with Applications in Machine Learning
Fast matrix algorithms have become the fundamental tools of machine learning in big data era.
Communication-Efficient Distributed SVD via Local Power Iterations
As a practical surrogate of OPT, sign-fixing, which uses a diagonal matrix with $\pm 1$ entries as weights, has better computation complexity and stability in experiments.
FedPower: Privacy-Preserving Distributed Eigenspace Estimation
The low communication power and the possible privacy breaches of data make the computation of eigenspace challenging.
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents
To tackle this problem, we propose a special network architecture with a few-shot learning algorithm that allows the number of agents to vary during centralized training.
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint
Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution.
Methodologies for Improving Modern Industrial Recommender Systems
Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more.
