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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…

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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^…

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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…

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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…

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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…

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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…

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