CONSISTENCY OF SPECTRAL HYPERGRAPH PARTITIONING UNDER PLANTED PARTITION MODEL von Ghoshdastidar, Debarghya, Dukkipati, Ambedkar

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Graph-based prediction of missing KPIs through optimization and random forests for KPI systems von May, Marvin Carl, Fang, Zeyu, Eitel, Michael B. M., Stricker, Nicole, Ghoshdastidar, Debarghya, Lanza, Gisela

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TWO-SAMPLE HYPOTHESIS TESTING FOR INHOMOGENEOUS RANDOM GRAPHS von Ghoshdastidar, Debarghya, Gutzeit, Maurilio, Carpentier, Alexandra, von Luxburg, Ulrike

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A consistent estimator for confounding strength von Rendsburg, Luca, Vankadara, Leena Chennuru, Ghoshdastidar, Debarghya, von Luxburg, Ulrike

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Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms von Ghoshdastidar, Debarghya, Dukkipati, Ambedkar, Bhatnagar, Shalabh

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Learning With Jensen-Tsallis Kernels von Ghoshdastidar, Debarghya, Adsul, Ajay P., Dukkipati, Ambedkar

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Smoothed Functional Algorithms for Stochastic Optimization Using q -Gaussian Distributions von Ghoshdastidar, Debarghya, Dukkipati, Ambedkar, Bhatnagar, Shalabh

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On the Convergence of Gradient Descent for Large Learning Rates von Crăciun, Alexandru, Ghoshdastidar, Debarghya

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Tight PAC-Bayesian Risk Certificates for Contrastive Learning von Van Elst, Anna, Ghoshdastidar, Debarghya

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Infinite Width Limits of Self Supervised Neural Networks von Fleissner, Maximilian, Anil, Gautham Govind, Ghoshdastidar, Debarghya

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Decision Trees for Interpretable Clusters in Mixture Models and Deep Representations von Fleissner, Maximilian, Zarvandi, Maedeh, Ghoshdastidar, Debarghya

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Data Augmentations Go Beyond Encoding Invariances: A Theoretical Study on Self-Supervised Learning von Feigin, Shlomo Libo, Fleissner, Maximilian, Ghoshdastidar, Debarghya

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When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? von Anil, Gautham Govind, Esser, Pascal, Ghoshdastidar, Debarghya

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Explaining Kernel Clustering via Decision Trees von Fleissner, Maximilian, Vankadara, Leena Chennuru, Ghoshdastidar, Debarghya

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On the Convergence of Gradient Descent for Large Learning Rates von Crăciun, Alexandru, Ghoshdastidar, Debarghya

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Non-Parametric Representation Learning with Kernels von Esser, Pascal, Fleissner, Maximilian, Ghoshdastidar, Debarghya

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Representation Learning Dynamics of Self-Supervised Models von Esser, Pascal, Mukherjee, Satyaki, Ghoshdastidar, Debarghya

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Exact Certification of (Graph) Neural Networks Against Label Poisoning von Sabanayagam, Mahalakshmi, Gosch, Lukas, Günnemann, Stephan, Ghoshdastidar, Debarghya

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Wasserstein Projection Pursuit of Non-Gaussian Signals von Mukherjee, Satyaki, Mukherjee, Soumendu Sundar, Ghoshdastidar, Debarghya

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A Revenue Function for Comparison-Based Hierarchical Clustering von Mandal, Aishik, Perrot, Michaël, Ghoshdastidar, Debarghya

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