Portrait of Negar Rostamzadeh

Negar Rostamzadeh

Associate Industry Member
Senior Research Scientist, Google Brain Ethical AI Team
Research Topics
Computer Vision
Generative Models
Multimodal Learning

Biography

Negar Rostamzadeh is a Senior Research Scientist at Google Responsible AI team and an Associate Industrial member at Mila - Quebec Artificial Intelligence Institute. Her research primarily focuses on understanding the social implications of machine learning and evaluation systems, as well as developing equitable and fair ML systems.

Negar holds a deep interest in the creative applications of computer vision and their impact on society and artists. She is the founder and program chair of the workshop series, "Computer Vision for Fashion, Art, and Design," as well as "Ethical Considerations in Creative Applications," featured at Computer Vision venues from ECCV 2018 to CVPR 2023.

Before joining Google, Negar worked as a research scientist at Element AI (Service Now), where she specialized in efficient learning from limited data in computer vision and multi-modal problems.

She completed her PhD in 2017 at the University of Trento under the supervision of Prof. Nicu Sebe, focusing on Video Understanding problems. She also spent two years at MILA (2015-2017), working on attention mechanisms in videos, generative models, and video captioning under the guidance of Prof. Aaron Courville. In 2016, she had the opportunity to intern with Google's Machine Intelligence team.

Negar actively contributes to various community engagements within the AI community. She has served as the program chair for the workshop series, "Science meets Engineering of Deep Learning," at ICLR, FAccT, and NeurIPS. Since 2020, she has been a board member of the Montreal AI Symposium, and in 2019, she held the position of Senior Program Chair. Negar is also an Area Chair for Vision Conferences such as CVPR and ICCV, and gave multiple keynotes in various workshops and conferences.

Current Students

Alireza Farashah
Master's Research - McGill University
Principal supervisor :
Golnoosh Farnadi
Website
Github

Publications

Reinforced Imitation in Heterogeneous Action Space
Konrad Żołna
Negar Rostamzadeh
Yoshua Bengio
Sungjin Ahn
Pedro O. Pinheiro
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a c… (see more)hallenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. In addition, this method adapts the agent's policy based on either mimicking expert behavior or maximizing sparse reward. We show, through navigation scenarios, that (i) an agent is able to efficiently leverage sparse rewards to outperform standard state-only imitation learning, (ii) it can learn a policy even when its actions are different from the expert, and (iii) the performance of the agent is not bounded by that of the expert, due to the optimized usage of sparse rewards.
2019-04-06
ArXiv (preprint)
arxiv.org
arxiv.org
Towards Standardization of Data Licenses: The Montreal Data License
Misha Benjamin
P. Gagnon
Negar Rostamzadeh
Chris Pal
Yoshua Bengio
Alex Shee
This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The paper's goal is … (see more)to build towards a common framework for data licensing akin to the licensing of open source software. Increased transparency and resolving conceptual ambiguities in existing licensing language are two noted benefits of the approach proposed in the paper. In parallel, such benefits may help foster fairer and more efficient markets for data through bringing about clearer tools and concepts that better define how data can be used in the fields of AI and ML. The paper's approach is summarized in a new family of data license language - \textit{the Montreal Data License (MDL)}. Alongside this new license, the authors and their collaborators have developed a web-based tool to generate license language espousing the taxonomies articulated in this paper.
2019-03-21
ArXiv (preprint)
arxiv.org
arxiv.org
Hierarchical Adversarially Learned Inference
Ishmael Belghazi
Sai Rajeswar
Olivier Mastropietro
Negar Rostamzadeh
Jovana Mitrovic
Aaron Courville
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative … (see more)and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity. Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset. There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA. Finally, we leverage the model's inference network to achieve state-of-the-art performance on a semi-supervised variant of the MNIST digit classification task.
2018-02-04
ArXiv (preprint)
openreview.net
openreview.net
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Ying Zhang
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Negar Rostamzadeh
Yoshua Bengio
Chris Pal
At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and re… (see more)presentations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech spectrum prediction using TIMIT. We achieve state-of-the-art performance on these audio-related tasks.
2018-01-01
ICLR.cc/2018/Conference (poster)
openreview.net
openreview.net