Search Results for author:
Found 8 papers, 5 papers with code
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
Identifying how much a model ${\widehat{p}}_{\theta}(Y|X)$ knows about the stochastic real-world process $p(Y|X)$ it was trained on is important to ensure it avoids producing incorrect or "hallucinated" answers or taking unsafe actions.
A density estimation perspective on learning from pairwise human preferences
Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in training large language models (LLMs), and has been the subject of much research.
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Large language models show impressive results at predicting structured text such as code, but also commonly introduce errors and hallucinations in their output.
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
We give generalization bounds for downstream linear prediction using our Kernel PCA representation, and show empirically on a set of synthetic tasks that applying Kernel PCA to contrastive learning models can indeed approximately recover the Markov chain eigenfunctions, although the accuracy depends on the kernel parameterization as well as on the augmentation strength.
Learning Generalized Gumbel-max Causal Mechanisms
To perform counterfactual reasoning in Structural Causal Models (SCMs), one needs to know the causal mechanisms, which provide factorizations of conditional distributions into noise sources and deterministic functions mapping realizations of noise to samples.
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
Denoising diffusion probabilistic models (DDPMs) have shown impressive results on sequence generation by iteratively corrupting each example and then learning to map corrupted versions back to the original.
Structured Denoising Diffusion Models in Discrete State-Spaces
Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. 2021, by going beyond corruption processes with uniform transition probabilities.
Learning Graph Structure With A Finite-State Automaton Layer
In practice, edges are used both to represent intrinsic structure (e. g., abstract syntax trees of programs) and more abstract relations that aid reasoning for a downstream task (e. g., results of relevant program analyses).
