Learning Mixture

Learning mixtures focuses on identifying and characterizing underlying components within complex datasets where observations arise from multiple sources or distributions. Current research emphasizes robust algorithms capable of handling outliers, noise, and limited data, often employing techniques like mixture models (e.g., Gaussian mixtures, mixtures of experts), spectral methods, and diffusion models. These advancements are crucial for diverse applications, including image processing, time series analysis, and causal inference, where disentangling mixed signals is essential for accurate modeling and prediction. The development of efficient and theoretically sound algorithms for learning mixtures continues to be a significant area of investigation.

Papers

November 9, 2024

Learning Mixtures of Experts with EM
Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi
Language Model Machine Learning Model Gradient Descent Expert Knowledge Expectation Maximization Learning Mixture Kullback Leibler

October 31, 2024

Learning Mixtures of Unknown Causal Interventions
Abhinav Kumar, Kirankumar Shiragur, Caroline Uhler
Causal Graph Causal Direction Learning Mixture Markov Equivalence Class Interventional Data Node Intervention

July 22, 2024

Robust Mixture Learning when Outliers Overwhelm Small Groups
Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters, Gleb Novikov, Amartya Sanyal, David Steurer, Fanny Yang
Input Mixture Learning Mixture List Decodable Outlier Event

July 15, 2024

Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering
Francesco Di Sario, Riccardo Renzulli, Enzo Tartaglione, Marco Grangetto
High Quality NeRF SLAM Reconstruction Quality Learning Mixture Scene Decomposition Fast Rendering Gated Mixture

June 11, 2024

Efficient Mixture Learning in Black-Box Variational Inference
Alexandra Hotti, Oskar Kviman, Ricky Molén, Víctor Elvira, Jens Lagergren
Variational Autoencoder Learning Mixture Variational Distribution Black Box Variational Inference SOTA Segmentation

June 3, 2024

Finding Optimally Robust Data Mixtures via Concave Maximization
Anvith Thudi, Chris J. Maddison
Robust Optimization Convex Concave Learning Mixture Data Mixture Mixture Weight

May 23, 2024

ULTRA-MC: A Unified Approach to Learning Mixtures of Markov Chains via Hitting Times
Fabian Spaeh, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
Gradient Descent Unified Framework Mixture Component Markov Chain Learning Mixture Continuous Time Markov Chain Hitting Time

April 29, 2024

Learning Mixtures of Gaussians Using Diffusion Models
Khashayar Gatmiry, Jonathan Kelner, Holden Lee
Diffusion Model RSD Difference of Gaussian Gaussian Mixture Learning Mixture

October 18, 2023

SQ Lower Bounds for Learning Mixtures of Linear Classifiers
Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun
Linear Classifier Multivariate Gaussian Input Mixture Learning Mixture Optimal Separation Statistical Query Lower Bound

October 4, 2023

Reinforcement Learning-based Mixture of Vision Transformers for Video Violence Recognition
Hamid Mohammadi, Ehsan Nazerfard, Tahereh Firoozi
Vision Transformer Positive Reinforcement Learning Mixture Violence Recognition

July 9, 2023

Class-Incremental Mixture of Gaussians for Deep Continual Learning
Lukasz Korycki, Bartosz Krawczyk
Continual Learning Gradient Based RSD Difference of Gaussian Discriminative Feature COntinual Learning Framework Learning Mixture Continual Learning Model Time Varying Objective

July 3, 2023

Learning Mixtures of Gaussians Using the DDPM Objective
Kulin Shah, Sitan Chen, Adam Klivans
Diffusion Model Gradient Descent RSD Difference of Gaussian Gaussian Mixture Model Distribution Learning Learning Mixture Score Estimation

June 26, 2023

Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
Bruno Régaldo-Saint Blancard, Michael Eickenberg
Image Denoising Wavelet Based Signal Reconstruction Learning Mixture Signal Recovery

May 23, 2023

Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
Bach Kim, Jose Dolz, Pierre-Marc Jodoin, Christian Desrosiers
Mixup Method Effective Approach Brain Segmentation Interactive Image Segmentation Learning Mixture Patient Specific Segmentation

May 6, 2023

Learning Mixtures of Gaussians with Censored Data
Wai Ming Tai, Bryon Aragam
RSD Difference of Gaussian Gaussian Mixture Statistical Learning Finite Sample Guarantee Censored Time to Event Gaussian Data Learning Mixture

February 22, 2023

Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecasting
Tian Guo
Mixture Model Probabilistic Forecast Learning Mixture Mixture Encoder

February 9, 2023

Learning Mixtures of Markov Chains with Quality Guarantees
Fabian Spaeh, Charalampos E. Tsourakakis
Markov Chain Singular Value Decomposition Learning Mixture EM Algorithm

November 17, 2022

Learning Mixtures of Markov Chains and MDPs
Chinmaya Kausik, Kevin Tan, Ambuj Tewari
Markov Decision Process Spectral Clustering Markov Chain Pairwise Distance Learning Mixture Multiple Trajectory

November 14, 2022

MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
Chang-Bin Jeon, Hyeongi Moon, Keunwoo Choi, Ben Sangbae Chon, Kyogu Lee
Singing Voice Music Source Separation Evaluation Datasets Learning Mixture Singing Voice Separation

October 14, 2022

Provable Subspace Identification Under Post-Nonlinear Mixtures
Qi Lyu, Xiao Fu
Mixture Model Independent Component Analysis Learning Mixture Subspace Identification