Author name cluster

Javier Abad

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4 papers

2 author rows

ICLR Conference 2025 Conference Paper

Copyright-Protected Language Generation via Adaptive Model Fusion

Javier Abad
Konstantin Donhauser
Francesco Pinto
Fanny Yang

The risk of language models reproducing copyrighted material from their training data has led to the development of various protective measures. Among these, inference-time strategies that impose constraints via post-processing have shown promise in addressing the complexities of copyright regulation. However, they often incur prohibitive computational costs or suffer from performance trade-offs. To overcome these limitations, we introduce Copyright-Protecting Model Fusion (CP-Fuse), a novel approach that combines models trained on disjoint sets of copyrighted material during inference. In particular, CP-Fuse adaptively aggregates the model outputs to minimize the reproduction of copyrighted content, adhering to a crucial balancing property to prevent the regurgitation of memorized data. Through extensive experiments, we show that CP-Fuse significantly reduces the reproduction of protected material without compromising the quality of text and code generation. Moreover, its post-hoc nature allows seamless integration with other protective measures, further enhancing copyright safeguards. Lastly, we show that CP-Fuse is robust against common techniques for extracting training data.

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ICLR Conference 2025 Conference Paper

Doubly robust identification of treatment effects from multiple environments

Piersilvio De Bartolomeis
Julia Kostin
Javier Abad
Yixin Wang
Fanny Yang

Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are prone to confounding, potentially compromising the validity of causal conclusions. While it is possible to correct for biases if the underlying causal graph is known, this is rarely a feasible ask in practical scenarios. A common strategy is to adjust for all available covariates, yet this approach can yield biased treatment effect estimates, especially when post-treatment or unobserved variables are present. We propose RAMEN, an algorithm that produces unbiased treatment effect estimates by leveraging the heterogeneity of multiple data sources without the need to know or learn the underlying causal graph. Notably, RAMEN achieves *doubly robust identification*: it can identify the treatment effect whenever the causal parents of the treatment or those of the outcome are observed, and the node whose parents are observed satisfies an invariance assumption. Empirical evaluations across synthetic, semi-synthetic, and real-world datasets show that our approach significantly outperforms existing methods.

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NeurIPS Conference 2025 Conference Paper

Efficient Randomized Experiments Using Foundation Models

Piersilvio De Bartolomeis
Javier Abad
Guanbo Wang
Konstantin Donhauser
Raymond Duch
Fanny Yang
Issa Dahabreh

Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the other hand, in silico experiments leveraging foundation models offer a cost-effective alternative that can potentially attain higher statistical precision. However, the benefits of in silico experiments come with a significant risk: statistical inferences are not valid if the models fail to accurately predict experimental responses to interventions. In this paper, we propose a novel approach that integrates the predictions from multiple foundation models with experimental data while preserving valid statistical inference. Our estimator is consistent and asymptotically normal, with asymptotic variance no larger than the standard estimator based on experimental data alone. Importantly, these statistical properties hold even when model predictions are arbitrarily biased. Empirical results across several randomized experiments show that our estimator offers substantial precision gains, equivalent to a reduction of up to 20\% in the sample size needed to match the same precision as the standard estimator based on experimental data alone.

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UAI Conference 2024 Conference Paper

Detecting critical treatment effect bias in small subgroups

Piersilvio De Bartolomeis
Javier Abad
Konstantin Donhauser
Fanny Yang

Randomized trials are considered the gold standard for making informed decisions in medicine. However, they are often not representative of the patient population in clinical practice. Observational studies, on the other hand, cover a broader patient population but are prone to various biases. Thus, before using observational data for any downstream task, it is crucial to benchmark its treatment effect estimates against a randomized trial. We propose a novel strategy to benchmark observational studies on a subgroup level. First, we design a statistical test for the null hypothesis that the treatment effects – conditioned on a subset of relevant features – differ up to some tolerance value. Our test allows us to estimate an asymptotically valid lower bound on the maximum bias strength for any subgroup. We validate our lower bound in a real-world setting and show that it leads to conclusions that align with established medical knowledge.

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