Publications (11)
ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems
Daryl Chang, Yi Wu, Jennifer She +2
Recommender systems often must maximize a primary objective while ensuring secondary ones satisfy minimum thresholds, or "guardrails." This is critical for maintaining a consistent…
Evaluating Gemini in an arena for learning
LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla +34
Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-t…
Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
Yi Wu, Daryl Chang, Jennifer She +3
We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm
Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt +3
We consider minimizing a smooth function subject to a summation constraint over its variables. By exploiting a connection between the greedy 2-coordinate update for this problem an…
Adversarial Computation of Optimal Transport Maps
Jacob Leygonie, Jennifer She, Amjad Almahairi +2
Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT). Generative adversarial networks (GANs) ar…