Introduction to Optimization From Structured Samples For Coverage And Influence Functions
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03 Data Driven Optimization
Structural Optimization and Machine Learning for Simulation with Dr. Raghavendra Sivapuram
Learning Augmentation Network via Influence Functions
Optimisation-based sampling approaches for hierarchical Bayesian inference
Making Hard Decisions: From Influence Diagrams to Optimization | Olli Herrala | JuliaCon 2023
Data-Centric Understanding of Policy Behavior and Performance with Influence Functions - 10.31.2025
The Foundation Model Revolution for Structured Data | Frank Hutter, Prior Labs | RAISE Summit 2026
Stanford AA222/CS361 Engineering Design Optimization I Probabilistic Surrogate Optimization
Jiaqi Zhang (MIT): Active Learning for Optimal Intervention Design in Causal Models
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Last Updated: September 29, 2026
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2022 Data-driven Optimization Workshop: Daniel Paulin University of Oxford, UK. Quantum Machine Learning MOOC, created by Peter Wittek from the University of Toronto in Spring 2019. Lecture 31: ... We met with one of our mentors, Dr. Raghavendra Sivapuram, and he talked to us about Authors: Donghoon Lee, Hyunsin Park, Trung Pham, Chang D. Yoo Description: Data augmentation can impact the generalization ... Tiangang Cui Monash University, Australia. We present the Decision Programming framework for solving multi-stage stochastic problems. The problem is first formulated as ... Abstract: In robot imitation learning, policies are trained to match the behavior distribution of demonstrations, not to maximize ... Frank Hutter, Founder & CEO of Prior Labs, on bringing the foundation model revolution to In this lecture for Stanford's AA 222 / CS 361 Engineering Design Speaker: Jiaqi Zhang (MIT) Title: Active Learning for Optimal Intervention Design in Causal Models Abstract: Sequential ... QuantFish instructor Dr. Sarah Depaoli explains the Mplus output for a dynamic We often get asked what we do and heck, we often ask ourselves "what exactly is it that we do?" Here, I talk about what
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