4 citations · 12 across the 9 of their papers we have counts for
6 papers · 1 filter
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
Sweta Karlekar, Carolina Zheng, Magnus Saebo +5
Many applications seek to optimize LLM outputs at test time by iteratively proposing, scoring, and refining candidates over a discrete output space. Existing methods use a calibrat…
Extremely Greedy Equivalence Search
Achille Nazaret, David Blei
The goal of causal discovery is to learn a directed acyclic graph from data. One of the most well-known methods for this problem is Greedy Equivalence Search (GES). GES searches fo…
Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees
Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret +2
Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute ri…
Estimating the Hallucination Rate of Generative AI
Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu +5
This paper presents a method for estimating the hallucination rate for in-context learning (ICL) with generative AI. In ICL, a conditional generative model (CGM) is prompted with a…
Stable Differentiable Causal Discovery
Achille Nazaret, Justin Hong, Elham Azizi +1
Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this prob…
Data Augmentations for Improved (Large) Language Model Generalization
Amir Feder, Yoav Wald, Claudia Shi +2
The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthc…