1 code implementation • 2 Dec 2024 • Philippe Brouillard, Chandler Squires, jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines.
no code implementations • 28 Oct 2024 • Marco Jiralerspong, Thomas Jiralerspong, Vedant Shah, Dhanya Sridhar, Gauthier Gidel
The task of causal imputation involves using this subset to predict unobserved interactions.
1 code implementation • 17 Oct 2024 • Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie
While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor.
1 code implementation • 9 Oct 2024 • Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick
From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\~no phenomenon and other processes, as well as the causal model over them.
no code implementations • 30 May 2024 • Elliot Layne, Jason Hartford, Sébastien Lachapelle, Mathieu Blanchette, Dhanya Sridhar
The key insight is that the mapping from latent variables driven by gene expression to the phenotype of interest changes sparsely across closely related environments.
1 code implementation • 29 May 2024 • Sarthak Mittal, Eric Elmoznino, Leo Gagnon, Sangnie Bhardwaj, Dhanya Sridhar, Guillaume Lajoie
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.
no code implementations • 27 May 2024 • Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello
Consistent with classical approaches, good performance is achieved when we have a good prior on the test data, and the underlying model is identifiable.
no code implementations • 8 Apr 2024 • Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system.
no code implementations • 8 Feb 2024 • Sophie Xhonneux, David Dobre, Jian Tang, Gauthier Gidel, Dhanya Sridhar
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.
1 code implementation • 24 Mar 2022 • 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.
1 code implementation • 20 Oct 2021 • Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei
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.
1 code implementation • 2 Sep 2021 • Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
A fundamental goal of scientific research is to learn about causal relationships.
1 code implementation • NAACL 2021 • Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar
Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e. g., predictions from classifiers and lexicons.
no code implementations • 19 Jun 2020 • Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently.
1 code implementation • 10 Jun 2019 • Dhanya Sridhar, Lise Getoor
In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses.
4 code implementations • 29 May 2019 • Victor Veitch, Dhanya Sridhar, David M. Blei
To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.
no code implementations • 26 May 2019 • Yixin Wang, Dhanya Sridhar, David M. Blei
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data.
no code implementations • 3 Jul 2018 • Varun Embar, Dhanya Sridhar, Golnoosh Farnadi, Lise Getoor
We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways.
no code implementations • 16 Nov 2017 • Dhanya Sridhar, Jay Pujara, Lise Getoor
Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning.
no code implementations • 2 Jul 2016 • Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction.