4 citations · 4 across the 9 of their papers we have counts for
8 papers · 1 filter
CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
Gerrit Großmann, Sumantrak Mukherjee, Sebastian J. Vollmer
Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated inte…
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
Yahya Aalaila, Sumantrak Mukherjee, Gerrit Großmann +1
Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult…
Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
Kanta Yamaoka, Sumantrak Mukherjee, Thomas Gärtner +5
Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reasoning---estimating effect sizes…
Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
Sumantrak Mukherjee, Serafima Lebedeva, Valentin Margraf +6
We propose a novel Bayesian framework for efficient exploration in contextual multi-task multi-armed bandit settings, where the context is only observed partially and dependencies…
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
Yahya Aalaila, Gerrit Großmann, Sumantrak Mukherjee +2
Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the 'holy grail' of causal learning, with applications ranging from interpreting ma…
Neural Spatiotemporal Point Processes: Trends and Challenges
Sumantrak Mukherjee, Mouad Elhamdi, George Mohler +4
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and he…