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Nicolas Beltran-Velez

6 accepted papers

2026

Inference-time Physics Alignment of Video Generative Models with Latent World Models

CVPR 2026

State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems fr

Cited by 0SourcecodeScholar
2025

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective

ICLR 2025poster

This work is about estimating when a conditional generative model (CGM) can solve an in-context learning (ICL) problem. An in-context learning (ICL) problem comprises a CGM, a dataset, and a prediction task. The CGM could be a multi-modal foundation model; the dataset, a collection of patient histor…

Cited by 1SourcePDFScholar
2024

Estimating the Hallucination Rate of Generative AI

NeurIPS 2024poster

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 dataset and a prediction question and asked to generate a response. One interpretation of ICL assumes that the CGM comp…

Cited by 6SourcePDFScholar
2024

Hypothesis Testing the Circuit Hypothesis in LLMs

NeurIPS 2024poster

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented. One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. But how can we evaluate this hypothesis? In this paper,…

2024

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages

ICML 2024poster

This paper proposes a step toward approximate Bayesian inference in on-policy actor-critic deep reinforcement learning. It is implemented through three changes to the Asynchronous Advantage Actor-Critic (A3C) algorithm: (1) applying a ReLU function to advantage estimates, (2) spectral normalization…

2024

Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees

NeurIPS 2024poster

Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson di…