Martin Takác

Venues

Frequent coauthors

Papers

1. Bant: Byzantine Antidote via Trial Function and Trust Scores
   AAAI 2026 ·

   Gleb Molodtsov

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2. Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models
   ACL 2026 ·

   Yanda Li

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3. Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
   ICML 2025 ·

   Savelii Chezhegov

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4. FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
   ICML 2025 ·

   Philip Zmushko

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5. From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
   ICLR 2025 ·

   Nikita Kotelevskii

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6. Learning Confident Classifiers in the Presence of Label Noise
   SDM 2025 ·

   Asma Ahmed Hashmi

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7. Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles
   ACL 2025 ·

   Munachiso Nwadike

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8. MedNNS: Supernet-Based Medical Task-Adaptive Neural Network Search
   MICCAI 2025 ·

   Lotfi Abdelkrim Mecharbat

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9. Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity
   ICLR 2025 ·

   Eduard Gorbunov

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10. Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
    ICLR 2025 ·

    Yury Demidovich

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11. OPTAMI: Global Superlinear Convergence of High-order Methods
    ICLR 2025 ·

    Dmitry Kamzolov

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12. Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
    AISTATS 2025 ·

    Ruichen Luo

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13. SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
    NeurIPS 2025 ·

    Ahmed Heakl

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14. Uncovering the Spectral Bias in Diagonal State Space Models
    NeurIPS 2025 ·

    Ruben Solozabal

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15. Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness
    ICLR 2024 ·

    Artem Agafonov

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16. Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
    IJCAI 2024 ·

    Nikita Kotelevskii

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17. Efficient Conformal Prediction under Data Heterogeneity
    AISTATS 2024 ·

    Vincent Plassier

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18. Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
    NeurIPS 2024 ·

    Artem Agafonov

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19. Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
    NeurIPS 2024 ·

    Sayantan Choudhury

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20. Robustly Train Normalizing Flows via KL Divergence Regularization
    AAAI 2024 ·

    Kun Song

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21. Self-Guiding Exploration for Combinatorial Problems
    NeurIPS 2024 ·

    Zangir Iklassov

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