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Lenon Minorics

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4 papers

2 author rows

TMLR Journal 2025 Journal Article

νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints

Thomas Pethick
Parameswaran Raman
Lenon Minorics
Mingyi Hong
Shoham Sabach
Volkan Cevher

Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, the method comes at the expense of storing a perturbation of the model parameters, which can be restrictive when memory bound. We design a variant of SAM, called $\nu$SAM, which obtains a low-rank perturbation by modifying the perturbation constraint. The update almost entirely removes the memory footprint of the perturbation without increasing the computational complexity, thus achieving close to a $1/3$ memory saving regarding the parameters when using SGD as the base optimizer. We demonstrate comparable performance of $\nu$SAM with SAM on vision transformers both when training models from scratch and for fine-tuning. Interestingly, $\nu$SAM seems to significantly improve performance for MLP-Mixer architectures across both settings. The results are corroborated theoretically, where we show that SAM with an \emph{arbitrary} norm choice (which includes $\nu$SAM) can converge even with fixed perturbation radius.

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ICLR Conference 2023 Conference Paper

Unsupervised Model Selection for Time Series Anomaly Detection

Mononito Goswami
Cristian I. Challu
Laurent Callot
Lenon Minorics
Andrey Kan

Anomaly detection in time-series has a wide range of practical applications. While numerous anomaly detection methods have been proposed in the literature, a recent survey concluded that no single method is the most accurate across various datasets. To make matters worse, anomaly labels are scarce and rarely available in practice. The practical problem of selecting the most accurate model for a given dataset without labels has received little attention in the literature. This paper answers this question \textit{i.e.} Given an unlabeled dataset and a set of candidate anomaly detectors, how can we select the most accurate model? To this end, we identify three classes of surrogate (unsupervised) metrics, namely, \textit{prediction error}, \textit{model centrality}, and \textit{performance on injected synthetic anomalies}, and show that some metrics are highly correlated with standard supervised anomaly detection performance metrics such as the $F_1$ score, but to varying degrees. We formulate metric combination with multiple imperfect surrogate metrics as a robust rank aggregation problem. We then provide theoretical justification behind the proposed approach. Large-scale experiments on multiple real-world datasets demonstrate that our proposed unsupervised approach is as effective as selecting the most accurate model based on partially labeled data.

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UAI Conference 2022 Conference Paper

Causal forecasting: generalization bounds for autoregressive models

Leena Chennuru Vankadara
Philipp Michael Faller
Michaela Hardt
Lenon Minorics
Debarghya Ghoshdastidar
Dominik Janzing

Despite the increasing relevance of forecasting methods, causal implications of these algorithms remain largely unexplored. This is concerning considering that, even under simplifying assumptions such as causal sufficiency, the statistical risk of a model can differ significantly from its causal risk. Here, we study the problem of causal generalization—generalizing from the observational to interventional distributions—in forecasting. Our goal is to find answers to the question: How does the efficacy of an autoregressive (VAR) model in predicting statistical associations compare with its ability to predict under interventions? To this end, we introduce the framework of causal learning theory for forecasting. Using this framework, we obtain a characterization of the difference between statistical and causal risks, which helps identify sources of divergence between them. Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts albeit with additional structure (restriction to interventional distributions under the VAR model). This structure allows us to obtain uniform convergence bounds on causal generalizability for the class of VAR models. To the best of our knowledge, this is the first work that provides theoretical guarantees for causal generalization in the time-series setting.

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ICML Conference 2022 Conference Paper

Causal structure-based root cause analysis of outliers

Kailash Budhathoki
Lenon Minorics
Patrick Blöbaum
Dominik Janzing

Current techniques for explaining outliers cannot tell what caused the outliers. We present a formal method to identify "root causes" of outliers, amongst variables. The method requires a causal graph of the variables along with the functional causal model. It quantifies the contribution of each variable to the target outlier score, which explains to what extent each variable is a "root cause" of the target outlier. We study the empirical performance of the method through simulations and present a real-world case study identifying "root causes" of extreme river flows.

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