Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
Adapting to Online Label Shift with Provable Guarantees Yong Bai
Neural Information Processing Systems
The non-stationarity nature and the lack of supervision make the problem challenging to be tackled.
Country:
- Asia > Japan > Honshū > Kantō > Tokyo Metropolis Prefecture > Tokyo (0.14)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Asia > China > Jiangsu Province > Nanjing (0.04)
- Asia > Japan > Honshū > Kantō > Chiba Prefecture > Chiba (0.04)
Genre:
- Research Report > New Finding (0.67)
- Research Report > Experimental Study (0.46)
Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Data Science (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.46)
Country:
- South America > Argentina > Pampas > Buenos Aires F.D. > Buenos Aires (0.04)
- North America > United States > Washington > King County > Bellevue (0.04)
- North America > United States > Oregon (0.04)
Country:
- North America > United States > Ohio > Franklin County > Columbus (0.04)
- North America > United States > Ohio > Delaware County > Delaware (0.04)
- North America > United States > Delaware > New Castle County > Newark (0.04)
- (2 more...)
Industry:
- Information Technology > Security & Privacy (1.00)
- Banking & Finance (1.00)
- Law (0.93)
Technology:
Optimizing Data Collection for Machine Learning
Neural Information Processing Systems
Optimizing Data Collection for Machine Learning
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.95)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.46)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.32)
Optimizing Data Collection for Machine Learning
Neural Information Processing Systems
Optimizing Data Collection for Machine Learning
Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.68)
Dynamical Wasserstein Barycenters for Time-series Modeling
Neural Information Processing Systems
Many time series can be modeled as a sequence of segments representing high-level discrete states, such as running and walking in a human activity application.
Country:
- Asia > Japan > Honshū > Kansai > Osaka Prefecture > Osaka (0.04)
- North America > United States > New Jersey > Mercer County > Princeton (0.04)
- North America > United States > New Jersey > Hudson County > Hoboken (0.04)
- (3 more...)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.46)
Country:
- North America > Canada > Ontario > Toronto (0.14)
- North America > United States > California > San Francisco County > San Francisco (0.14)
- North America > United States > California > Los Angeles County > Los Angeles (0.14)
- (11 more...)
Genre:
- Research Report > Experimental Study (0.68)
- Research Report > New Finding (0.46)
Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.68)
Country:
- North America > United States > California > Los Angeles County > Los Angeles (0.14)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- Europe > Finland > Uusimaa > Helsinki (0.04)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.93)
A Generative Nonparametric Bayesian Model for Whole Genomes
Alan N. Amin, Eli N. Weinstein, Debora S. Marks
Neural Information Processing Systems
Measuring and making DNA is central to modern biology and biomedicine.
Industry:
- Health & Medicine > Pharmaceuticals & Biotechnology (1.00)
- Health & Medicine > Therapeutic Area > Oncology (0.93)
Technology:
Automated Dynamic Mechanism Design
Neural Information Processing Systems
For environments with large time horizons, we show that the principal's optimal
Technology:
- Information Technology > Game Theory (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.67)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.46)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Model-Based Reasoning (0.42)