Dhanya Sridhar

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 :

Collaborating researcher - Helmholtz AI

PhD - Université de Montréal

Shruti Joshi

PhD - Université de Montréal

Google Scholar

Tejas Kasetty

Master's Research - Université de Montréal

Principal supervisor :

Cristian Dragos Manta

PhD - Université de Montréal

Principal supervisor :

Yoshua Bengio

Github

Tom Marty

PhD - Université de Montréal

tom.marty@mila.quebec

Github

Google Scholar

Mizu Nishikawa-Toomey

PhD - Université de Montréal

Principal supervisor :

Theo Saulus

Collaborating researcher

Principal supervisor :

Simon Lacoste-Julien

Publications

General Causal Imputation via Synthetic Interventions

Marco Jiralerspong

Thomas Jiralerspong

Vedant Shah

Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (see more)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.

2024-10-30

NeurIPS.cc/2024/Workshop/CRL (poster)

General Causal Imputation via Synthetic Interventions

Marco Jiralerspong

Thomas Jiralerspong

Vedant Shah

Gintare Karolina Dziugaite

2024-10-28

ArXiv (preprint)

arxiv.org

Evaluating Interventional Reasoning Capabilities of Large Language Models

Tejas Kasetty

Divyat Mahajan

Alexandre Drouin

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. Our analysis on four LLMs highlights that while GPT- 4 models show promising accuracy at predicting the intervention effects, they remain sensitive to distracting factors in the prompts.

2024-10-10

NeurIPS.cc/2024/Workshop/CALM (poster)

In-Context Learning, Can It Break Safety?

Sophie Xhonneux

David Dobre

Michael Noukhovitch

Jian Tang

2024-06-28

ICML.cc/2024/Workshop/NextGenAISafety (poster)

Demystifying amortized causal discovery with transformers

Francesco Montagna

Max Cairney-Leeming

Francesco Locatello

Supervised learning approaches for causal discovery from observational data often achieve competitive performance despite seemingly avoiding… (see more) explicit assumptions that traditional methods make for identifiability. In this work, we investigate CSIvA \citep{ke2023learning}, a transformer-based model promising to train on synthetic data and transfer to real data. First, we bridge the gap with existing identifiability theory and show that constraints on the training data distribution implicitly define a prior on the test observations. Consistent with classical approaches, good performance is achieved when we have a good prior on the test data, and the underlying model is identifiable. At the same time, we find new trade-offs. Training on datasets generated from different classes of causal models, unambiguously identifiable in isolation, improves the test generalization. Performance is still guaranteed, as the ambiguous cases resulting from the mixture of identifiable causal models are unlikely to occur (which we formally prove). Overall, our study finds that amortized causal discovery still needs to obey identifiability theory, but it also differs from classical methods in how the assumptions are formulated, trading more reliance on assumptions on the noise type for fewer hypotheses on the mechanisms.

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

Does learning the right latent variables necessarily improve in-context learning?

Sarthak Mittal

Eric Elmoznino

L'eo Gagnon

Sangnie Bhardwaj

Guillaume Lajoie

Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting ave… (see more)nues for efficiently solving new tasks. For many tasks, e.g., linear regression, the data factorizes: examples are independent given a task latent that generates the data, e.g., linear coefficients. While an optimal predictor leverages this factorization by inferring task latents, it is unclear if Transformers implicitly do so or if they instead exploit heuristics and statistical shortcuts enabled by attention layers. Both scenarios have inspired active ongoing work. In this paper, we systematically investigate the effect of explicitly inferring task latents. We minimally modify the Transformer architecture with a bottleneck designed to prevent shortcuts in favor of more structured solutions, and then compare performance against standard Transformers across various ICL tasks. Contrary to intuition and some recent works, we find little discernible difference between the two; biasing towards task-relevant latent variables does not lead to better out-of-distribution performance, in general. Curiously, we find that while the bottleneck effectively learns to extract latent task variables from context, downstream processing struggles to utilize them for robust prediction. Our study highlights the intrinsic limitations of Transformers in achieving structured ICL solutions that generalize, and shows that while inferring the right latents aids interpretability, it is not sufficient to alleviate this problem.

2024-05-29

ArXiv (preprint)

arxiv.org

Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain?

Sarthak Mittal

Eric Elmoznino

Leo Gagnon

Sangnie Bhardwaj

Guillaume Lajoie

2024-03-05

ICLR.cc/2024/Workshop/R2-FM (poster)

In-Context Learning Can Re-learn Forbidden Tasks

Sophie Xhonneux

David Dobre

Jian Tang

Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vuln… (see more)erabilities. One perspective on LLM safety training is that it algorithmically forbids the model from answering toxic or harmful queries. To assess the effectiveness of safety training, in this work, we study forbidden tasks, i.e., tasks the model is designed to refuse to answer. Specifically, we investigate whether in-context learning (ICL) can be used to re-learn forbidden tasks despite the explicit fine-tuning of the model to refuse them. We first examine a toy example of refusing sentiment classification to demonstrate the problem. Then, we use ICL on a model fine-tuned to refuse to summarise made-up news articles. Finally, we investigate whether ICL can undo safety training, which could represent a major security risk. For the safety task, we look at Vicuna-7B, Starling-7B, and Llama2-7B. We show that the attack works out-of-the-box on Starling-7B and Vicuna-7B but fails on Llama2-7B. Finally, we propose an ICL attack that uses the chat template tokens like a prompt injection attack to achieve a better attack success rate on Vicuna-7B and Starling-7B. Trigger Warning: the appendix contains LLM-generated text with violence, suicide, and misinformation.

2024-02-08

ArXiv (preprint)

arxiv.org

Learning Macro Variables with Auto-encoders

Eric Elmoznino

Maitreyi Swaroop

2023-10-27

NeurIPS.cc/2023/Workshop/CRL (poster)

Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness

Yixin Wang

David Blei

Machine learning ( ml ) methods have the potential to automate high-stakes decisions, such as bail admissions or credit lending, by analyzin… (see more)g and learning from historical data. But these algorithmic decisions may be unfair: in learning from historical data, they may replicate discriminatory practices from the past. In this paper, we propose two algorithms that adjust ﬁtted ML predictors to produce decisions that are fair. Our methods provide post-hoc adjustments to the predictors, without requiring that they be retrained. We consider a causal model of the ML decisions, deﬁne fairness through counterfactual decisions within the model, and then form algorithmic decisions that capture the historical data as well as possible, but are provably fair. In particular, we consider two deﬁnitions of fairness. The ﬁrst is “equal counterfactual opportunity,” where the counterfactual distribution of the decision is the same regardless of the protected attribute; the second is counterfactual fairness. We evaluate the algorithms, and the trade-o � between accuracy and fairness, on datasets about admissions, income, credit, and recidivism.

2023-01-01

Trans. Mach. Learn. Res. (published)

Identifiable Deep Generative Models via Sparse Decoding

Gemma Elyse Moran

Yixin Wang

David Blei

We develop the sparse VAE for unsupervised representation learning on high-dimensional data. The sparse VAE learns a set of latent factors … (see more)(representations) which summarize the associations in the observed data features. The underlying model is sparse in that each observed feature (i.e. each dimension of the data) depends on a small subset of the latent factors. As examples, in ratings data each movie is only described by a few genres; in text data each word is only applicable to a few topics; in genomics, each gene is active in only a few biological processes. We prove such sparse deep generative models are identifiable: with infinite data, the true model parameters can be learned. (In contrast, most deep generative models are not identifiable.) We empirically study the sparse VAE with both simulated and real data. We find that it recovers meaningful latent factors and has smaller heldout reconstruction error than related methods.

2022-10-20

TMLR (accepted)

Causal inference from text: A commentary

David Blei

2022-10-19

Science Advances (published)