Leveraging Reviews - Learning to Price with Buyer and Seller Uncertainty
Explore pricing strategies in online marketplaces using customer reviews and AI. Learn how sellers can optimize prices while helping buyers estimate product value through relevant feedback.
Interior Point Methods
Explore advanced interior point methods for optimization, covering key ideas, path following analysis, and specialized algorithms like Renegar's and regularized John ellipse barrier.
Optimal Learning from Data
Explore optimal learning from data, including error recovery, discrete optimization, and handling noisy measurements. Insights on deep learning and stochastic settings provided.
Discrete Optimization-Aided Structured Learning at Scale - IPAM at UCLA
Explore discrete optimization techniques for structured learning, featuring algorithmic aspects, statistical setups, and approaches to feature selection and learning trees.
Machine Learning Inside MIP Solvers - IPAM at UCLA
Explore machine learning applications in MIP solvers, focusing on cutting planes and numerical stability. Gain insights into enhancing solver performance for general MIP benchmarks.
Integer Optimization for Predictive & Prescriptive Analytics in High Stakes Domains
Explore integer optimization techniques for developing interpretable, robust, and fair decision-tree models to assist high-stakes decision-making in social services and healthcare domains.
Learning for Decision-Making Under Uncertainty - IPAM at UCLA
Explore data-driven methods for uncertainty in decision-making, including mean robust optimization and techniques to reshape uncertainty sets, with applications in portfolio optimization and inventory management.
Introduction to Parameterized Algorithms and Applications
Explore parameterized complexity and its applications in discrete optimization, focusing on LP-guided techniques, Lenstra's algorithm, and structured ILPs using Graver bases.
Introduction to Interior Point Methods for Discrete Optimization, Lecture I
Explore interior point methods for optimization, their recent advancements, and applications in solving various continuous and combinatorial problems efficiently.
Recent Progress on Submodular Function Minimization
Explore recent advancements in submodular function minimization algorithms, leveraging convex optimization methods to address open questions and improve efficiency in discrete optimization.
Information Complexity of Mixed-Integer Convex Optimization
Explore information complexity in mixed-integer convex optimization, including oracle-based bounds and partial first-order information impacts, with insights from Professor Amitabh Basu.
Mixed-Integer Programming: 65 Years of History and the Artificial Intelligence Challenge - DS4DM Coffee Talk
Explore the evolution of Mixed-Integer Programming, from its 1958 roots to modern AI integration, and its impact on solving diverse optimization problems.
Some Approaches for Solving the Discretely-Constrained Mixed Complementarity Problem (DC-MCP)
Explore approaches for solving discretely-constrained mixed complementarity problems in energy and transportation, combining integer restrictions with MCP systems and considering equity aspects.
Aaron Sidford- Introduction to Interior Point Methods for Discrete Optimization, Lecture III
Explore interior point methods for discrete optimization, covering theory, advancements, and applications in maximum flow, bipartite matching, and linear programming.
Aaron Sidford: Introduction to Interior Point Methods for Discrete Optimization, Lecture II
Explore interior point methods for discrete optimization, covering theory, advances, and applications in flow, matching, and linear programming problems.
Quantum Algorithms for Optimization - Quantum Colloquium
Explore quantum algorithms for optimization, including discrete and continuous problems, with insights on potential speedups and limitations in quantum computing applications.
Can ML Help in Solving Cargo Capacity Management Booking Control Problems
Explore ML applications in cargo capacity management, focusing on booking control problems and predicting solution costs for discrete optimization in revenue management.
Machine Learning for MIP Solving - IPAM at UCLA
Explore machine learning techniques for solving Mixed Integer Programming problems, enhancing optimization algorithms in discrete mathematics and artificial intelligence applications.
Introduction to Parameterized Algorithms, Lecture I
Gentle introduction to parameterized complexity, focusing on basic techniques like branching, color coding, and kernelization. Explores connections to linear programming and discrete optimization.
Michal Pilipczuk: Introduction to Parameterized Algorithms, Lecture II
Explore parameterized complexity, focusing on LP-related methods. Learn branching, color coding, kernelization, and dynamic programming techniques for algorithm design and optimization.
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Explore neural set function extensions for integrating discrete functions into neural networks, focusing on continuous domain extensions and high-dimensional representations for combinatorial optimization.