Education
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known in advance.
PragmaticallyLearningfromPedagogical DemonstrationsinMulti-GoalEnvironments
In many situations, a single demonstration may be correctly interpreted as demonstration ofavariety ofgoals -wecallthisgoalambiguity.
Average Individual Fairness: Algorithms, Generalization and Experiments
Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
Neural Information Processing Systems http://nips.cc/
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Recently, large language models (LLMs) ( e.g., GPT -3 and ChatGPT) have shown remarkable zero-shot and few-shot performance on various tasks [
Equal Opportunity in Online Classification with Partial Feedback
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu
Choosing 1p T gives 3.1 Step 1:
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses.