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Found 26 papers, 5 papers with code
A Short Review for Ontology Learning from Text: Stride from Shallow Learning, Deep Learning to Large Language Models Trend
It analyzes the methodologies and limitations of shallow-learning-based and deep-learning-based techniques for ontology learning, and provides comprehensive knowledge for the frontier work of using large language models to enhance ontology learning.
OneBit: Towards Extremely Low-bit Large Language Models
Model quantification uses low bit-width values to represent the weight matrices of models, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs.
Efficient Sparse Least Absolute Deviation Regression with Differential Privacy
To fast solve the non-smooth loss under a given privacy budget, we develop a Fast Robust And Privacy-Preserving Estimation (FRAPPE) algorithm for least absolute deviation regression.
Vision-language Assisted Attribute Learning
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization.
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
In this paper, we develop an online robust policy evaluation procedure, and establish the limiting distribution of our estimator, based on its Bahadur representation.
Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
Communication games, which we refer to as incomplete information games that heavily depend on natural language communication, hold significant research value in fields such as economics, social science, and artificial intelligence.
Pluggable Neural Machine Translation Models via Memory-augmented Adapters
Although neural machine translation (NMT) models perform well in the general domain, it remains rather challenging to control their generation behavior to satisfy the requirement of different users.
Distributed Semi-Supervised Sparse Statistical Inference
The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters.
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
Stochastic gradient descent with momentum (SGDM) has been widely used in many machine learning and statistical applications.
Super-Resolution Information Enhancement For Crowd Counting
As the proposed method requires SR labels, we further propose a Super-Resolution Crowd Counting dataset (SR-Crowd).
Distributed Estimation and Inference for Semi-parametric Binary Response Models
The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems.
Majority Vote for Distributed Differentially Private Sign Selection
Simulation studies are conducted to demonstrate the effectiveness of our proposed method.
Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach
Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications.
Malicious Mode Attack on EV Coordinated Charging Load and MIADRC Defense Strategy
Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability of the power system, which considers the impact factors of both mode damping and disturbance compensation.
Structured DropConnect for Uncertainty Inference in Image Classification
For image classification tasks, we propose a structured DropConnect (SDC) framework to model the output of a deep neural network by a Dirichlet distribution.
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures.
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms
Privacy-preserving data analysis becomes prevailing in recent years.
Neural Interactive Collaborative Filtering
Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.
Median Matrix Completion: from Embarrassment to Optimality
In this paper, we consider matrix completion with absolute deviation loss and obtain an estimator of the median matrix.
Distributed High-dimensional Regression Under a Quantile Loss Function
This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise.
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
Though reinforcement learning~(RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile and difficult to model, which typically consists of both instant feedback~(e. g. clicks, ordering) and delayed feedback~(e. g. dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation.
Attention-aware Multi-stroke Style Transfer
Neural style transfer has drawn considerable attention from both academic and industrial field.
Distributed Inference for Linear Support Vector Machine
In this paper, we propose a multi-round distributed linear-type (MDL) estimator for conducting inference for linear SVM.
First-order Newton-type Estimator for Distributed Estimation and Inference
The key component in our method is the proposal of a computationally efficient estimator of $\Sigma^{-1} w$, where $\Sigma$ is the population Hessian matrix and $w$ is any given vector.
Quantile Regression Under Memory Constraint
This paper proposes a computationally efficient method, which only requires an initial QR estimator on a small batch of data and then successively refines the estimator via multiple rounds of aggregations.
Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm.
