Dhanya Sridhar

Francesco Locatello

Supervised learning approaches for causal discovery from observational data often achieve competitive performance despite seemingly avoiding… (voir plus) 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.

2024-06-16

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

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

2024-03-04

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

In-Context Learning Can Re-learn Forbidden Tasks

David Dobre

Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vuln… (voir plus)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.

2024-02-07

ArXiv (prépublication)

arxiv.org

Learning Macro Variables with Auto-encoders

Eric Elmoznino

Maitreyi Swaroop

2023-10-26

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… (voir plus)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.

2022-12-31

Trans. Mach. Learn. Res. (publié)

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 … (voir plus)(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.

2022-10-19

TMLR (accepté)

Causal inference from text: A commentary

David Blei

2022-10-18

Science Advances (publié)

Estimating Social Influence from Observational Data

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… (voir plus)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.

2022-06-27

Proceedings of the First Conference on Causal Learning and Reasoning (publié)

proceedings.mlr.press

Heterogeneous Supervised Topic Models

Hal Daumé III

David Blei

Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to bo… (voir plus)th uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the outcome across latent topics. To fit HSTMs, we develop a variational inference algorithm based on the auto-encoding variational Bayes framework. We study the performance of HSTMs on eight datasets and find that they consistently outperform related methods, including fine-tuned black-box models. Finally, we apply HSTMs to analyze news articles labeled with pro- or anti-tone. We find evidence of differing language used to signal a pro- and anti-tone.

2021-12-31

Transactions of the Association for Computational Linguistics (publié)