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Oliver Wang

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5 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Conformal Prediction under Lévy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

Liviu Aolaritei
Julie Zhu
Oliver Wang
Michael Jordan
Youssef Marzouk

Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using Lévy-Prokhorov (LP) ambiguity sets, which capture both local and global perturbations. We provide a self-contained overview of LP ambiguity sets and their connections to popular metrics such as Wasserstein and Total Variation. We show that the link between conformal prediction and LP ambiguity sets is a natural one: by propagating the LP ambiguity set through the scoring function, we reduce complex high-dimensional distribution shifts to manageable one-dimensional distribution shifts, enabling exact quantification of worst-case quantiles and coverage. Building on this analysis, we construct robust conformal prediction intervals that remain valid under distribution shifts, explicitly linking LP parameters to interval width and confidence levels. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.

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TMLR Journal 2024 Journal Article

Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models

Cristina Nader Vasconcelos
Abdullah Rashwan
Austin Waters
Trevor Walker
Keyang Xu
Jimmy Yan
Rui Qian
Yeqing Li

We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components.The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment vs. high resolution rendering. We first demonstrate the benefits of scaling a Shallow UNet, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high resolution end-to-end models, while preserving the integrity of the pre-trained representation,stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes.Vermeer, our full pipeline model trained with internal datasets to produce 1024×1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.

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NeurIPS Conference 2020 Conference Paper

Swapping Autoencoder for Deep Image Manipulation

Taesung Park
Jun-Yan Zhu
Oliver Wang
Jingwan Lu
Eli Shechtman
Alexei Efros
Richard Zhang

Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image into two independent components and enforce that any swapped combination maps to a realistic image. In particular, we encourage the components to represent structure and texture, by enforcing one component to encode co-occurrent patch statistics across different parts of the image. As our method is trained with an encoder, finding the latent codes for a new input image becomes trivial, rather than cumbersome. As a result, our method enables us to manipulate real input images in various ways, including texture swapping, local and global editing, and latent code vector arithmetic. Experiments on multiple datasets show that our model produces better results and is substantially more efficient compared to recent generative models.

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NeurIPS Conference 2018 Conference Paper

Self-Supervised Generation of Spatial Audio for 360° Video

Pedro Morgado
Nuno Nvasconcelos
Timothy Langlois
Oliver Wang

We introduce an approach to convert mono audio recorded by a 360° video camera into spatial audio, a representation of the distribution of sound over the full viewing sphere. Spatial audio is an important component of immersive 360° video viewing, but spatial audio microphones are still rare in current 360° video production. Our system consists of end-to-end trainable neural networks that separate individual sound sources and localize them on the viewing sphere, conditioned on multi-modal analysis from the audio and 360° video frames. We introduce several datasets, including one filmed ourselves, and one collected in-the-wild from YouTube, consisting of 360° videos uploaded with spatial audio. During training, ground truth spatial audio serves as self-supervision and a mixed down mono track forms the input to our network. Using our approach we show that it is possible to infer the spatial localization of sounds based only on a synchronized 360° video and the mono audio track.

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NeurIPS Conference 2017 Conference Paper

Toward Multimodal Image-to-Image Translation

Jun-Yan Zhu
Richard Zhang
Deepak Pathak
Trevor Darrell
Alexei Efros
Oliver Wang
Eli Shechtman

Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity.

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