Dhanya Sridhar

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 :

philippebrouillard@gmail.com

Collaborating researcher - Helmholtz AI

Independent visiting researcher - University of Maryland College Park

Skylar Gu

Research Intern - McGill University

Co-supervisor :

Guillaume Lajoie

Sophia Gunluk

PhD - Université de Montréal

Shruti Joshi

PhD - Université de Montréal

Tejas Kasetty

Master's Research - Université de Montréal

Principal supervisor :

Guillaume Lajoie

tejaskasetty@gmail.com

Cristian Dragos Manta

PhD - Université de Montréal

Principal supervisor :

Yoshua Bengio

Tom Marty

PhD - Université de Montréal

tom.marty@mila.quebec

Mizu Nishikawa-Toomey

PhD - Université de Montréal

Principal supervisor :

Theo Saulus

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

Publications

In-context learning and Occam's razor

Mahan Fathi

A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees fo… (see more)r generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor. Despite the need for simple models, most current approaches in machine learning only minimize the training error, and at best indirectly promote simplicity through regularization or architecture design. Here, we draw a connection between Occam's razor and in-context learning: an emergent ability of certain sequence models like Transformers to learn at inference time from past observations in a sequence. In particular, we show that the next-token prediction loss used to train in-context learners is directly equivalent to a data compression technique called prequential coding, and that minimizing this loss amounts to jointly minimizing both the training error and the complexity of the model that was implicitly learned from context. Our theory and the empirical experiments we use to support it not only provide a normative account of in-context learning, but also elucidate the shortcomings of current in-context learning methods, suggesting ways in which they can be improved. We make our code available at https://github.com/3rdCore/PrequentialCode.

2025-05-01

ICML.cc/2025/Conference (poster)

doi.org

openreview.net

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

Philippe Brouillard

Chandler Squires

Jonas Wahl

Konrad Paul Kording

Karen Sachs

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.

2025-01-28

cclear.cc/CLeaR/2025/Conference (poster)

doi.org

openreview.net

Sparsity regularization via tree-structured environments for disentangled representations

Elliot Layne

Gintare Karolina Dziugaite

Jason Hartford

Mathieu Blanchette

2024-05-30

ArXiv (preprint)

arxiv.org

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan

Gintare Karolina Dziugaite

Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. We evaluate six LLMs on the benchmarks, finding that GPT models show promising accuracy at predicting the intervention effects.

2024-04-08

ArXiv (preprint)

doi.org

arxiv.org

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan