65 citations · 109 across the 15 of their papers we have counts for
17 papers
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard +2
In experimental causal inference, we distinguish between two sources of uncertainty: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when t…
Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference
Zahra Fatemi, Jean Pouget-Abadie, Elena Zheleva
The presence of interference, where the outcome of an individual may depend on the treatment assignment and behavior of neighboring nodes, can lead to biased causal effect estimati…
Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms
Jennifer Brennan, Yahu Cong, Yiwei Yu +6
It is increasingly common in digital environments to use A/B tests to compare the performance of recommendation algorithms. However, such experiments often violate the stable unit…
Improving the Variance of Differentially Private Randomized Experiments through Clustering
Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie
Estimating causal effects from randomized experiments is only possible if participants are willing to disclose their potentially sensitive responses. Differential privacy, a widely…
Causal Estimation of User Learning in Personalized Systems
Evan Munro, David Jones, Jennifer Brennan +3
In online platforms, the impact of a treatment on an observed outcome may change over time as 1) users learn about the intervention, and 2) the system personalization, such as indi…
Modeling Interference Using Experiment Roll-out
Ariel Boyarsky, Hongseok Namkoong, Jean Pouget-Abadie
Experiments on online marketplaces and social networks suffer from interference, where the outcome of a unit is impacted by the treatment status of other units. We propose a framew…
Cluster Randomized Designs for One-Sided Bipartite Experiments
Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie
The conclusions of randomized controlled trials may be biased when the outcome of one unit depends on the treatment status of other units, a problem known as interference. In this…
Optimizing Randomized and Deterministic Saturation Designs under Interference
Chencheng Cai, Jean Pouget-Abadie, Edoardo M. Airoldi
Randomized saturation designs are a family of designs which assign a possibly different treatment proportion to each cluster of a population at random. As a result, they generalize…
Generative Adversarial Networks
Gilad Cohen, Raja Giryes
Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Argua…
From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics
ChunJun Cao
In this note, I review a recent approach to quantum gravity that "gravitizes" quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insigh…
Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model
Christopher Harshaw, Fredrik Sävje, David Eisenstat +2
A bipartite experiment consists of one set of units being assigned treatments and another set of units for which we measure outcomes. The two sets of units are connected by a bipar…
Causal inference with bipartite designs: A generalized propensity score approach
Nick Doudchenko, Minzhengxiong Zhang, Evgeni Drynkin +5
Bipartite experiments, in which one set of units receives a treatment while outcomes are measured on another set, have recently garnered attention for their ability to capture inte…
Optimizing cluster-based randomized experiments under a monotonicity assumption
Jean Pouget-Abadie, David C. Parkes, Vahab Mirrokni +1
Cluster-based randomized experiments are popular designs for mitigating the bias of standard estimators when interference is present and classical causal inference and experimental…
Testing for arbitrary interference on experimentation platforms
Jean Pouget-Abadie, Martin Saveski, Guillaume Saint-Jacques +4
Experimentation platforms are essential to modern large technology companies, as they are used to carry out many randomized experiments daily. The classic assumption of no interfer…
Inferring Graphs from Cascades: A Sparse Recovery Framework
Jean Pouget-Abadie, Thibaut Horel
In the Network Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. In this paper, we approach this…
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation
Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merrienboer +2
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating…