Tag: control
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Conditional neural control variates for variance reduction in Bayesian inverse problems
Conditional neural control variates for variance reduction in Bayesian inverse problems arXiv:2602.21357v1 Announce Type: new Abstract: Bayesian inference for inverse problems involves computing expectations under posterior distributions — e.g., posterior means, variances, or predictive quantities — typically via Monte Carlo (MC) estimation. When the quantity of interest varies significantly under the posterior, accurate estimates demand…
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Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems
Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems With some hints for good numerics The post Overcoming Nonsmoothness and Control Chattering in Nonconvex Optimal Control Problems appeared first on Towards Data Science. Willem Esterhuizen Go to original source
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Optimal Control of the Future via Prospective Foraging
Optimal Control of the Future via Prospective Foraging arXiv:2511.08717v1 Announce Type: new Abstract: Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in either reinforcement learning or online learning. While powerful, these frameworks for learning are mathematically distinct from Probably Approximately Correct (PAC) learning, which…
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How to Control a Robot with Python
How to Control a Robot with Python 3D simulations and movement control with PyBullet The post How to Control a Robot with Python appeared first on Towards Data Science. Mauro Di Pietro Go to original source
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How to perform synthetic control for multiple treated units? What are the things to keep in mind while performing it? Also, what python package i could use? Also have questions about metrics
How to perform synthetic control for multiple treated units? What are the things to keep in mind while performing it? Also, what python package i could use? Also have questions about metrics Hi I have never done Synthetic control, i want to work on a small project (like small data. My task is to find…
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Model Predictive Control Basics
Model Predictive Control Basics A hands-on tutorial with Python and CasADi The post Model Predictive Control Basics appeared first on Towards Data Science. Willem Esterhuizen Go to original source
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Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity
Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity arXiv:2505.24097v1 Announce Type: new Abstract: Calibrating blackbox machine learning models to achieve risk control is crucial to ensure reliable decision-making. A rich line of literature has been studying how to calibrate a model so that its predictions satisfy explicit finite-sample statistical guarantees under a fixed,…
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Nuclear Microreactor Control with Deep Reinforcement Learning
Nuclear Microreactor Control with Deep Reinforcement Learning arXiv:2504.00156v1 Announce Type: cross Abstract: The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for…
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Automate Supply Chain Analytics Workflows with AI Agents using n8n
Automate Supply Chain Analytics Workflows with AI Agents using n8n Why build things the hard way when you can design them the smart way? As a Supply Chain Data Scientist, I’ve explored various frameworks like LangChain and LangGraph to build AI agents using Python. Leveraging LLMs with LangChain for Supply Chain Analytics — A Control Tower Powered by…
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Post Launch Evaluation of Policies in a High-Dimensional Setting
Post Launch Evaluation of Policies in a High-Dimensional Setting arXiv:2501.00119v1 Announce Type: new Abstract: A/B tests, also known as randomized controlled experiments (RCTs), are the gold standard for evaluating the impact of new policies, products, or decisions. However, these tests can be costly in terms of time and resources, potentially exposing users, customers, or other…
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Synthetic Control Sample for Before and After A/B Test
Synthetic Control Sample for Before and After A/B Test Learn a simple way to use linear regression to create a synthetic control sample for your A/B test Continue reading on Towards Data Science » Gustavo R Santos Go to original source
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Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate arXiv:2412.11257v1 Announce Type: new Abstract: Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization. A natural alternative is to replace simulation with machine learning or surrogate prediction, though this…