138 citations · 494 across the 31 of their papers we have counts for
6 papers · 1 filter
Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
Kaushal Paneri, Michael Munje, Kailash Singh Maurya +2
Growing scale of recommender systems require extensive tuning to respond to market dynamics and system changes. We address the challenge of tuning a large-scale ads recommendation…
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?
Ying Fan, Jingling Li, Adith Swaminathan +2
We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-O…
The Importance of Directional Feedback for LLM-based Optimizers
Allen Nie, Ching-An Cheng, Andrey Kolobov +1
We study the potential of using large language models (LLMs) as an interactive optimizer for solving maximization problems in a text space using natural language and numerical feed…
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Ching-An Cheng, Allen Nie, Adith Swaminathan
We study a class of optimization problems motivated by automating the design and update of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorc…
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
Christine Herlihy, Jennifer Neville, Tobias Schnabel +1
We explore the use of Large Language Model (LLM-based) chatbots to power recommender systems. We observe that the chatbots respond poorly when they encounter under-specified reques…
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks
Jiacen Xu, Jack W. Stokes, Geoff McDonald +5
Large language models (LLMs) have demonstrated impressive results on natural language tasks, and security researchers are beginning to employ them in both offensive and defensive s…