Mixture Model

Mixture models are statistical tools used to represent complex data distributions as a combination of simpler component distributions, aiming to improve modeling accuracy and interpretability. Current research focuses on developing efficient algorithms for parameter estimation (e.g., EM algorithms, gradient methods, and moment methods), exploring novel architectures like mixtures of experts and incorporating mixture models into various applications such as object pose estimation, time series prediction, and noisy label learning. The widespread applicability of mixture models across diverse fields, from materials science to machine learning, highlights their significance in providing flexible and powerful tools for data analysis and modeling.

Papers

February 24, 2024

Multimodal Instruction Tuning with Conditional Mixture of LoRA
Ying Shen, Zhiyang Xu, Qifan Wang, Yu Cheng, Wenpeng Yin, Lifu Huang
Multimodal Large Language Model Instruction Tuning Mixture Model Multimodal Task Multimodal Instruction Cross Modal Task

February 23, 2024

Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models
Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser, Patrick Thiran
Lower Bound Mixture Model Gaussian Mixture Optimal Rate Hard Clustering Multinomial Mixture

February 22, 2024

February 16, 2024

February 8, 2024

Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models
Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa, Vaibhav Rajan
Gaussian Mixture Model Mixture Model Python Library Parsimonious Machine Learning Model Based Clustering

January 31, 2024

Mitigating the Influence of Distractor Tasks in LMs with Prior-Aware Decoding
Raymond Douglas, Andis Draguns, Tomáš Gavenčiak
Language Model Instruction Following Mixture Model Right Problem Prior Model Context Extrapolation

December 29, 2023

Exact Consistency Tests for Gaussian Mixture Filters using Normalized Deviation Squared Statistics
Nisar Ahmed, Luke Burks, Kailah Cabral, Alyssa Bekai Rose
Mixture Model Gaussian Mixture Bayesian Filtering Consistency Check

December 27, 2023

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling
Hengrui Zhang, Jie Chen, James M. Rondinelli, Wei Chen
Mixture Component Molecular Graph Known Molecule Mixture Model Material Discovery Virtual Screening Chemical Space

December 22, 2023

Learning Rich Rankings
Arjun Seshadri, Stephen Ragain, Johan Ugander
Maximum Likelihood Mixture Model Ranking Model Learning to Rank Choice Model Multinomial Logit

December 9, 2023

Revisiting RIP guarantees for sketching operators on mixture models
Ayoub Belhadji, Rémi Gribonval
Mixture Model Feature Operator Sparse Mixture High Probability Guarantee Random Sketching Stochastic Operator

December 7, 2023

Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation
Xiaoyu Lin, Laurent Girin, Xavier Alameda-Pineda
Mixture Component Mixture Model Separation Performance Trajectory Model Dynamical Variational Variable Generative Model Trajectory Label

December 3, 2023

Learn2Extend: Extending sequences by retaining their statistical properties with mixture models
Dimitris Vartziotis, George Dasoulas, Florian Pausinger
Sequence of Sequence Mixture Model Point Process Random Sequence Statistical Property

November 30, 2023

Can semi-supervised learning use all the data effectively? A lower bound perspective
Alexandru Ţifrea, Gizem Yüce, Amartya Sanyal, Fanny Yang
Raw Data Semi Supervised Learning Supervised Learning Unsupervised Learning Visual Perspective Mixture Model

November 29, 2023

A Stochastic-Geometrical Framework for Object Pose Estimation based on Mixture Models Avoiding the Correspondence Problem
Wolfgang Hoegele
Computer Vision Pose Estimation Mixture Model Object Pose Estimation Robust Pose Estimation Correspondence Matching Stochastic Geometry Feature Density

November 27, 2023

Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks
Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
Mixture Model Speaker Information Diarization System Acoustic Sensor Network Meeting Transcript

November 14, 2023

Mixture Model

Papers

Multimodal Instruction Tuning with Conditional Mixture of LoRA

Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models

Transition State Clustering for Interaction Segmentation and Learning

DynGMA: a robust approach for learning stochastic differential equations from data

A Novel Multivariate Skew-Normal Mixture Model and Its Application in Path-Planning for Very-Large-Scale Robotic Systems

A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership

Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models

Mitigating the Influence of Distractor Tasks in LMs with Prior-Aware Decoding

Exact Consistency Tests for Gaussian Mixture Filters using Normalized Deviation Squared Statistics

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

Learning Rich Rankings

Revisiting RIP guarantees for sketching operators on mixture models

Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation

Learn2Extend: Extending sequences by retaining their statistical properties with mixture models

Can semi-supervised learning use all the data effectively? A lower bound perspective

A Stochastic-Geometrical Framework for Object Pose Estimation based on Mixture Models Avoiding the Correspondence Problem

Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks

On semi-supervised estimation using exponential tilt mixture models

Transformers can optimally learn regression mixture models

Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series