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David Belanger

10 accepted papers

2022

Constrained Discrete Black-Box Optimization using Mixed-Integer Programming

Theodore P Papalexopoulos, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma, David Belanger
ICML 2022spotlight

Discrete black-box optimization problems are challenging for model-based optimization (MBO) algorithms, such as Bayesian optimization, due to the size of the search space and the need to satisfy combinatorial constraints. In particular, these methods require repeatedly solving a complex discrete glo…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2020

Model-based reinforcement learning for biological sequence design

Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell
ICLR 2020poster

The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations. In re…

Cited by 167Source ↗Scholar ↗
2020

Population-Based Black-Box Optimization for Biological Sequence Design

Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy +2
ICML 2020poster

The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences — a setting…

Cited by 67Source ↗PDF ↗Scholar ↗
2019

Boundless: Generative Adversarial Networks for Image Extension

Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu +1
ICCV 2019poster

Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to gene…

Cited by 124PDF ↗Scholar ↗
2018

Learning Latent Permutations with Gumbel-Sinkhorn Networks

Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek
ICLR 2018poster

Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper in…

Cited by 307Source ↗PDF ↗code ↗Scholar ↗
2017

Synthesizing Normalized Faces From Facial Identity Features

Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman
CVPR 2017spotlight

We present a method for synthesizing a frontal, neutral-expression image of a person's face, given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our en…

Cited by 190PDF ↗Scholar ↗
2016

Bethe Learning of Graphical Models via MAP Decoding

Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara
AISTATS 2016poster

Many machine learning tasks require fitting probabilistic models over structured objects, such as pixel grids, matchings, and graph edges. Maximum likelihood estimation (MLE) for such domains is challenging due to the intractability of computing partition functions. One can resort to approximate mar…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗