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Konstantin Donhauser

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10 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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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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ICML Conference 2025 Conference Paper

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models

Konstantin Donhauser
Kristina Ulicna
Gemma E. Moran
Aditya Ravuri
Kian Kenyon-Dean
Cian Eastwood
Jason S. Hartford

Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in the text domain. In this work, we explore whether DL can extract meaningful concepts from less human-interpretable scientific data, such as vision foundation models trained on cell microscopy images, where limited prior knowledge exists about which high-level concepts should arise. We propose a novel combination of a sparse DL algorithm, Iterative Codebook Feature Learning (ICFL), with a PCA whitening pre-processing step derived from control data. Using this combined approach, we successfully retrieve biologically meaningful concepts, such as cell types and genetic perturbations. Moreover, we demonstrate how our method reveals subtle morphological changes arising from human-interpretable interventions, offering a promising new direction for scientific discovery via mechanistic interpretability in bioimaging.

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ICML Conference 2025 Conference Paper

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

Kian Kenyon-Dean
Zitong Jerry Wang
John Urbanik
Konstantin Donhauser
Jason S. Hartford
Saber Saberian
Nil Sahin
Ihab Bendidi

Deriving insights from experimentally generated datasets requires methods that can account for random and systematic measurement errors and remove them in order to accurately represent the underlying effects of the conditions being tested. Here we present a framework for pretraining on large-scale microscopy datasets that includes three steps: (1) curating a set of diverse and self-consistent training samples, (2) scaling training of an appropriate foundation model architecture on this dataset, (3) evaluating intermediate layers of the trained model to identify the best representation for downstream tasks. Using this strategy, we present the largest foundation model for cell microscopy data to our knowledge, a new 1. 9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy image crops. Compared to a previous published ViT-L/8 MAE, our new model achieves a 60% improvement in linear separability of genetic perturbations and obtains the best overall performance on whole-genome relationship recall, batch correction replicate consistency, and compound-gene activity prediction benchmarks.

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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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ICML Conference 2024 Conference Paper

Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent

Konstantin Donhauser
Javier Abad Martinez
Neha Hulkund
Fanny Yang

We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while being highly scalable and offering the flexibility to incorporate additional domain-specific constraints.

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ICLR Conference 2023 Conference Paper

Strong inductive biases provably prevent harmless interpolation

Michael Aerni
Marco Milanta
Konstantin Donhauser
Fanny Yang

Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization. In contrast, modern overparameterized models can yield small test error despite interpolating noise — a phenomenon often called "benign overfitting" or "harmless interpolation". This paper argues that the degree to which interpolation is harmless hinges upon the strength of an estimator's inductive bias, i.e., how heavily the estimator favors solutions with a certain structure: while strong inductive biases prevent harmless interpolation, weak inductive biases can even require fitting noise to generalize well. Our main theoretical result establishes tight non-asymptotic bounds for high-dimensional kernel regression that reflect this phenomenon for convolutional kernels, where the filter size regulates the strength of the inductive bias. We further provide empirical evidence of the same behavior for deep neural networks with varying filter sizes and rotational invariance.

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ICML Conference 2022 Conference Paper

Fast rates for noisy interpolation require rethinking the effect of inductive bias

Konstantin Donhauser
Nicolò Ruggeri
Stefan Stojanovic
Fanny Yang

Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for regularized models, in this paper we caution against a strong inductive bias for interpolation in the presence of noise: While a stronger inductive bias encourages a simpler structure that is more aligned with the ground truth, it also increases the detrimental effect of noise. Specifically, for both linear regression and classification with a sparse ground truth, we prove that minimum $\ell_p$-norm and maximum $\ell_p$-margin interpolators achieve fast polynomial rates close to order $1/n$ for $p > 1$ compared to a logarithmic rate for $p = 1$. Finally, we provide preliminary experimental evidence that this trade-off may also play a crucial role in understanding non-linear interpolating models used in practice.

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ICML Conference 2021 Conference Paper

How rotational invariance of common kernels prevents generalization in high dimensions

Konstantin Donhauser
Mingqi Wu
Fanny Yang

Kernel ridge regression is well-known to achieve minimax optimal rates in low-dimensional settings. However, its behavior in high dimensions is much less understood. Recent work establishes consistency for high-dimensional kernel regression for a number of specific assumptions on the data distribution. In this paper, we show that in high dimensions, the rotational invariance property of commonly studied kernels (such as RBF, inner product kernels and fully-connected NTK of any depth) leads to inconsistent estimation unless the ground truth is a low-degree polynomial. Our lower bound on the generalization error holds for a wide range of distributions and kernels with different eigenvalue decays. This lower bound suggests that consistency results for kernel ridge regression in high dimensions generally require a more refined analysis that depends on the structure of the kernel beyond its eigenvalue decay.

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NeurIPS Conference 2021 Conference Paper

Interpolation can hurt robust generalization even when there is no noise

Konstantin Donhauser
Alexandru Tifrea
Michael Aerni
Reinhard Heckel
Fanny Yang

Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers. These findings suggest that ridge regularization has vanishing benefits in high dimensions. We challenge this narrative by showing that, even in the absence of noise, avoiding interpolation through ridge regularization can significantly improve generalization. We prove this phenomenon for the robust risk of both linear regression and classification, and hence provide the first theoretical result on \emph{robust overfitting}.

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