Evaluation Guide Learning Theory 
Beth's Blog: How Nonprofits Can Use Social Media
FEBRUARY 18, 2014
Using the ADDIE for designing your workshop, you arrive at the “E” or evaluation. ” While a participant survey is an important piece of your evaluation, it is critical to incorporate a holistic reflection of your workshop. There are two different methods to evaluate your training. Use Learning Theory.
ASU Lodestar Center
OCTOBER 16, 2020
This call spurred the increasing demand for program evaluation. In your organization, this may look like negative attitudes toward evaluation, poor research designs and collecting data but not using the data. The root problem here is poor evaluation capacity. The root problem here is poor evaluation capacity.
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Beth's Blog: How Nonprofits Can Use Social Media
JANUARY 27, 2014
Designing and delivering a training to a nonprofit audience is not about extreme content delivery or putting together a PowerPoint and answering questions. If you want to get results, you need to think about instructional design and learning theory. And, there is no shortage of learning theories and research.
Google Research AI blog
APRIL 30, 2023
If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics.
Stanford AI Lab Blog
DECEMBER 6, 2021
Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z.
The AI Alignment Forum
FEBRUARY 7, 2025
Here we're sharing a reference guide , created as part of that RFP, which describes what projects we'd like to see across 21 research directions in technical AI safety. We think this adversarial style of evaluation and iteration is necessary to ensure an AI system has a low probability of catastrophic failure.
Stanford AI Lab Blog
APRIL 25, 2022
List of Accepted Papers Autonomous Reinforcement Learning: Formalism and Benchmarking Authors : Archit Sharma*, Kelvin Xu*, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn Contact : architsh@stanford.edu Links: Paper | Website Keywords : reinforcement learning, continual learning, reset-free reinforcement learning MetaShift: (..)
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