2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
Ramon Viñas Torné, Sílvia Fàbregas Salazar, Soyon Park +4
Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensional settings where large-scale i…
Meta-RL Induces Exploration in Language Agents
Yulun Jiang, Liangze Jiang, Damien Teney +2
Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the…
Large (Vision) Language Models are Unsupervised In-Context Learners
Artyom Gadetsky, Andrei Atanov, Yulun Jiang +4
Recent advances in large language and vision-language models have enabled zero-shot inference, allowing models to solve new tasks without task-specific training. Various adaptation…
Let Go of Your Labels with Unsupervised Transfer
Artyom Gadetsky, Yulun Jiang, Maria Brbic
Foundation vision-language models have enabled remarkable zero-shot transferability of the pre-trained representations to a wide range of downstream tasks. However, to solve a new…
The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
Artyom Gadetsky, Maria Brbic
We present HUME, a simple model-agnostic framework for inferring human labeling of a given dataset without any external supervision. The key insight behind our approach is that cla…