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Yu-Lin Wei

7 accepted papers

2026

CO3: CONTRASTING CONCEPTS COMPOSE BETTER

ICLR 2026poster

We propose to improve multi-concept prompt fidelity in text-to-image diffusion models. We begin with common failure cases—prompts like “a cat and a clock” that sometimes yields images where one concept is missing, faint, or colliding awkwardly with another. We hypothesize that this happens when the…

Cited by 0SourceScholar
2026

Personalized Image Generation via Human-in-the-loop Bayesian Optimization

ICML 2026poster

Imagine Alice has a specific image $x^\ast$ in her mind, say, the view of the street in which she grew up during her childhood. To generate that exact image, she guides a generative model with multiple rounds of prompting and arrives at an image $x^{p*}$. Although $x^{p*}$ is reasonably close to $x^…

Cited by 0SourceScholar
2025

Can NeRFs "See" without Cameras?

NeurIPS 2025poster

Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how optical rays bring color information from a 3D object to the camera pixels. Radio frequency (RF) or audio signals can also…

Cited by 0SourceScholar
2025

Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo

ICASSP 2025accepted

This paper considers the problem of estimating chirp parameters from a noisy mixture of chirps. While a rich body of work exists in this area, challenges remain when extending these techniques to chirps of higher order polynomials. We formulate this as a non-convex optimization problem and propose a…

Cited by 0SourceScholar
2025

Kernel Learning for Sample Constrained Black-Box Optimization

AAAI 2025technical

Black box optimization (BBO) focuses on optimizing unknown functions in high-dimensional spaces. In many applications, sampling the unknown function is expensive, imposing a tight sample budget.Ongoing work is making progress on reducing the sample budget by learning the shape/structure of the funct…

Cited by 0SourcePDFScholar
2024

Sample-Constrained Black Box Optimization for Audio Personalization

AAAI 2024technical

We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter h*, which applied to any music or speech, will maximize the user’s satisfaction. This is a black-box optimization problem since the user’s satisfaction function is unknown. Substantive work h…

Cited by 0SourcePDFScholar