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Found 15 papers, 7 papers with code
D-Flow: Differentiating through Flows for Controlled Generation
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.
Causal clustering: design of cluster experiments under network interference
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers.
Generalized Schrödinger Bridge Matching
Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions.
Training-free Linear Image Inverses via Flows
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.
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
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
Privately generating tabular data using language models
Privately generating synthetic data from a table is an important brick of a privacy-first world.
Bounding Training Data Reconstruction in Private (Deep) Learning
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.
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
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.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
The decoupled extended Kalman filter for dynamic exponential-family factorization models
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.
Constrained Bayesian Optimization with Noisy Experiments
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems.
End-to-end Planning of Fixed Millimeter-Wave Networks
This article discusses a framework to support the design and end-to-end planning of fixed millimeter-wave networks.
Coauthorship and citation in scientific publishing
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
The Anatomy of the Facebook Social Graph
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
An efficient and principled method for detecting communities in networks
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
