4 citations · 4 across the 10 of their papers we have counts for
11 papers
Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal +8
Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two challenges: learning user preferen…
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…
Graph Agnostic Causal Bayesian Optimisation
Sumantrak Mukherjee, Mengyan Zhang, Seth Flaxman +1
We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimisi…
Had enough of experts? Quantitative knowledge retrieval from large language models
David Selby, Kai Spriestersbach, Yuichiro Iwashita +6
Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information…
X Hacking: The Threat of Misguided AutoML
Rahul Sharma, Sergey Redyuk, Sumantrak Mukherjee +4
Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to…
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…