11.12.2023

Teaser image to MCML at NeurIPS 2023

MCML at NeurIPS 2023

37th Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, LA, USA, December 10-16, 2023

We are happy to announce that MCML researchers are represented with the following papers at NeurIPS 2023:


S. Chen, J. Gu, Z. Han, Y. Ma and V. Tresp.

Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models.
URL

D. Frauen, V. Melnychuk and S. Feuerriegel.

Sharp Bounds for Generalized Causal Sensitivity Analysis.
URL

F. Fumagalli, M. Muschalik, P. Kolpaczki, E. Hüllermeier and B. Hammer.

SHAP-IQ: Unified Approximation of any-order Shapley Interactions.
URL

M. Ghahremani Boozandani and C. Wachinger.

RegBN: Batch Normalization of Multimodal Data with Regularization.
URL

T. Klug, D. Atik and R. Heckel.

Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods.
URL

A. Krainovic, M. Soltanolkotabi and R. Heckel.

Learning Provably Robust Estimators for Inverse Problems via Jittering.
URL

R. Liao, X. Jia, Y. Ma and V. Tresp.

GENTKG: Generative Forecasting on Temporal Knowledge Graph.
URL

S. Maskey, R. Paolino, A. Bacho and G. Kutyniok.

A Fractional Graph Laplacian Approach to Oversmoothing.
PDF

V. Melnychuk, D. Frauen and S. Feuerriegel.

Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model.
URL

S. Scepanovic, I. Obadic, S. Joglekar, L. GIUSTARINI, C. Nattero, D. Quercia and X. Zhu.

MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery.
URL

J. Schweisthal, D. Frauen, V. Melnychuk and S. Feuerriegel.

Reliable Off-Policy Learning for Dosage Combinations.
URL

M. Singh, A. Fono and G. Kutyniok.

Expressivity of Spiking Neural Networks through the Spike Response Model.
URL

G. Zhai, E. P. Örnek, S.-C. Wu, Y. Di, F. Tombari, N. Navab and B. Busam.

CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs.
URL

S. Zhang, P. Wicke, L. K. Senel, L. Figueredo, A. Naceri, S. Haddadin, B. Plank and H. Schütze.

LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation.
URL

11.12.2023


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