no code implementations • 21 Feb 2024 • Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, Yaron Lipman
Taming the generation outcome of state of the art Diffusion and Flow-Matching (FM) models without having to re-train a task-specific model unlocks a powerful tool for solving inverse problems, conditional generation, and controlled generation in general.
no code implementations • 23 Oct 2023 • Davide Viviano, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers.
1 code implementation • 3 Oct 2023 • Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A. Theodorou, Ricky T. Q. Chen
Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions.
no code implementations • 25 Sep 2023 • Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer
Solving inverse problems without any training involves using a pretrained generative model and making appropriate modifications to the generation process to avoid finetuning of the generative model.
1 code implementation • 5 Sep 2023 • Lili Yu, Bowen Shi, Ramakanth Pasunuru, Benjamin Muller, Olga Golovneva, Tianlu Wang, Arun Babu, Binh Tang, Brian Karrer, Shelly Sheynin, Candace Ross, Adam Polyak, Russell Howes, Vasu Sharma, Puxin Xu, Hovhannes Tamoyan, Oron Ashual, Uriel Singer, Shang-Wen Li, Susan Zhang, Richard James, Gargi Ghosh, Yaniv Taigman, Maryam Fazel-Zarandi, Asli Celikyilmaz, Luke Zettlemoyer, Armen Aghajanyan
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on Text-to-Image Generation on MS COCO
1 code implementation • 7 Jun 2023 • Alexandre Sablayrolles, Yue Wang, Brian Karrer
Privately generating synthetic data from a table is an important brick of a privacy-first world.
1 code implementation • 28 Jan 2022 • Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten
Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy attacks.
1 code implementation • NeurIPS 2020 • Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree.
2 code implementations • NeurIPS 2020 • Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
no code implementations • 26 Jun 2018 • Carlos Alberto Gomez-Uribe, Brian Karrer
Motivated by the needs of online large-scale recommender systems, we specialize the decoupled extended Kalman filter (DEKF) to factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through numerical experiments on synthetic and on real-world data.
no code implementations • 21 Jun 2017 • Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems.
no code implementations • 20 May 2017 • Tim Danford, Onur Filiz, Jing Huang, Brian Karrer, Manohar Paluri, Guan Pang, Vish Ponnampalam, Nicolas Stier-Moses, Birce Tezel
This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks.
1 code implementation • 1 Apr 2013 • Travis Martin, Brian Ball, Brian Karrer, M. E. J. Newman
A large number of published studies have examined the properties of either networks of citation among scientific papers or networks of coauthorship among scientists.
Digital Libraries Social and Information Networks Physics and Society
no code implementations • 18 Nov 2011 • Johan Ugander, Brian Karrer, Lars Backstrom, Cameron Marlow
Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure.
Social and Information Networks Physics and Society
1 code implementation • 18 Apr 2011 • Brian Ball, Brian Karrer, M. E. J. Newman
We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times.
Social and Information Networks Statistical Mechanics Physics and Society