Portrait of Dhanya Sridhar

Dhanya Sridhar

Core Academic Member
dhanya.sridhar@mila.quebec
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Causality
Deep Learning
Probabilistic Models
Reasoning
Representation Learning
Website
Google Scholar

Biography

Dhanya Sridhar is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

She was a postdoctoral researcher at Columbia University and received her doctorate from the University of California, Santa Cruz.

In brief, Sridhar’s research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently and discover new knowledge alongside us.

Current Students

Philippe Brouillard
PhD - Université de Montréal
Co-supervisor :
Alexandre Drouin
Website
Github
Google Scholar
Andrea Dittadi
Collaborating researcher - Helmholtz AI
Website
Github
Google Scholar
Navita Goyal
Research Intern - Université de Montréal
Co-supervisor :
Alexandre Drouin
Website
Github
Google Scholar
Sophia Gunluk
PhD - Université de Montréal
Website
Shruti Joshi
PhD - Université de Montréal
Website
Google Scholar
Tejas Kasetty
Master's Research - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Website
Github
Google Scholar
Cristian Dragos Manta
PhD - Université de Montréal
Principal supervisor :
Yoshua Bengio
Github
Tom Marty
PhD - Université de Montréal
tom.marty@mila.quebec
Website
Github
Google Scholar
Mizu Nishikawa-Toomey
PhD - Université de Montréal
Principal supervisor :
Laurent Charlin
Website
Github
Google Scholar
Maitreyi Swaroop
Collaborating researcher
Github

Publications

Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations
Elliot Layne
Jason Hartford
Sébastien Lachapelle
Mathieu Blanchette
Dhanya Sridhar
Recently, learning invariant predictors across varying environments has been shown to improve the generalization of supervised learning meth… (see more)ods. This line of investigation holds great potential for application to biological problem settings, where data is often naturally heterogeneous. Biological samples often originate from different distributions, or environments. However, in biological contexts, the standard "invariant prediction" setting may not completely fit: the optimal predictor may in fact vary across biological environments. There also exists strong domain knowledge about the relationships between environments, such as the evolutionary history of a set of species, or the differentiation process of cell types. Most work on generic invariant predictors have not assumed the existence of structured relationships between environments. However, this prior knowledge about environments themselves has already been shown to improve prediction through a particular form of regularization applied when learning a set of predictors. In this work, we empirically evaluate whether a regularization strategy that exploits environment-based prior information can be used to learn representations that better disentangle causal factors that generate observed data. We find evidence that these methods do in fact improve the disentanglement of latent embeddings. We also show a setting where these methods can leverage phylogenetic information to estimate the number of latent causal features.
2022-07-09
auai.org/UAI/2022/Workshop/CRL (poster)
doi.org
openreview.net
Estimating Social Influence from Observational Data
Dhanya Sridhar
Caterina De Bacco
David Blei
We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The k… (see more)ey challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.
2022-06-28
Proceedings of the First Conference on Causal Learning and Reasoning (published)
doi.org
openreview.net
Heterogeneous Supervised Topic Models
Dhanya Sridhar
Hal Daumé III
David Blei
2022-06-17
Transactions of the Association for Computational Linguistics (published)
doi.org