Fair Recommendations with Limited Sensitive Attributes - A Distributionally Robust Optimization Approach
Explore distributionally robust optimization for fair recommendations with limited sensitive attributes in RecSys, enhancing fairness in machine learning applications.
Data-Driven Decision Making with Entropic Risk Measure
Explore data-driven decision-making using entropic risk measure. Learn bias correction techniques and apply them to project selection, portfolio optimization, and insurance pricing.
Building Reliable Machine Learning Systems - Challenges and Approaches
Exploring methods to build reliable machine learning systems robust to failures across subpopulations and environments, including influence functions, distributionally robust optimization, and real-world benchmarks.
The Role of Convexity in Data-Driven Decision-Making
Explore data-driven decision-making through distributionally robust optimization, focusing on computation, statistics, and real-time implementation aspects. Gain insights into online optimization applications.
Nonconvex Optimization in Matrix Optimization and Distributionally Robust Optimization
Explore nonconvex optimization in matrix and distributionally robust contexts, covering algorithms, complexity, and applications in machine learning with Stephen Wright.
An AI with an Agenda - How Our Cognitive Biases Leak Into Machine Learning
Explores how human cognitive biases can infiltrate AI and machine learning models, offering strategies to mitigate these issues and create more objective, ethical algorithms.
Optimal Transport for Distributionally Robust Optimization and Applications in Machine Learning
Explore optimal transport in distributionally robust optimization, focusing on its applications in machine learning, distribution shifts, and responsible AI. Gain insights into solving challenging optimization problems.
Robust Optimization and Generalization
Explore robust optimization's history and its modern machine learning applications, focusing on performance certification and practical challenges in ML systems.