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Anthony Lin

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2 papers

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AAMAS Conference 2026 Conference Paper

Solving Qualitative Multi-Objective Stochastic Games

Moritz Graf
Anthony Lin
Rupak Majumdar

Manyproblemsincompositionalsynthesisandverificationofmultiagent systems—such as rational verification and assume-guarantee verificationinprobabilisticsystems—reducetoreasoningabouttwoplayer multi-objective stochastic games. This motivates us to study the problem of characterizing the complexity and memory requirements for two-player stochastic games with Boolean combinations of qualitative reachability and safety objectives. Reachability objectives require that a given set of states is reached; safety requires that a given set is invariant. A qualitative winning condition asks that an objective is satisfied almost surely (AS) or (in negated form) with non-zero (NZ) probability. We study the determinacy and complexity landscape of the problem. We show that games with conjunctions of AS and NZ reachability and safety objectives are determined, and determining the winner is PSPACE-complete. The same holds for positive boolean combinations of AS reachability and safety, as well as for negations thereof. On the other hand, games with full Boolean combinations of qualitative objectives are not determined, and are NEXPTIMEhard. Our hardness results show a connection between stochastic games and logics with partially-ordered quantification. Our results shed light on the relationship between determinacy and complexity, and extend the complexity landscape for stochastic games in the multi-objective setting.

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NeurIPS Conference 2025 Conference Paper

Mitigating Spurious Features in Contrastive Learning with Spectral Regularization

Naghmeh Ghanooni
Waleed Mustafa
Dennis Wagner
Sophie Fellenz
Anthony Lin
Marius Kloft

Neural networks generally prefer simple and easy-to-learn features. When these features are spuriously correlated with the labels, the network's performance can suffer, particularly for underrepresented classes or concepts. Self-supervised representation learning methods, such as contrastive learning, are especially prone to this issue, often resulting in worse performance on downstream tasks. We identify a key spectral signature of this failure: early reliance on dominant singular modes of the learned feature matrix. To mitigate this, we propose a novel framework that promotes a uniform eigenspectrum of the feature covariance matrix, encouraging diverse and semantically rich representations. Our method operates in a fully self-supervised setting, without relying on ground-truth labels or any additional information. Empirical results on SimCLR and SimSiam demonstrate consistent gains in robustness and transfer performance, suggesting broad applicability across self-supervised learning paradigms. Code: https: //github. com/NaghmehGh/SpuriousCorrelation_SSRL

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