We study the multinomial logit (MNL) contextual bandit problem for sequential assortment selection. Although most existing research assumes utility functions to be linear in item features, this linearity assumption restricts the modeling of intricate interactions between items and user preferences. A recent work [41] has investigated general utility function classes, yet its method faces fundamental tradeoffs between computational tractability and statistical efficiency. To address this limitation, we propose a computationally efficient algorithm for MNL contextual bandits leveraging the upper confidence bound principle, specifically designed for non-linear parametric utility functions, including those modeled by neural networks. Under a realizability assumption and a mild geometric condition on the utility function class, our algorithm achieves a regret bound of eO( T), where T denotes the total number of rounds. Our result establishes that sharp eO( T)-regret is attainable even with neural network-based utilities, without relying on strong assumptions such as neural tangent kernel approximations. To the best of our knowledge, our proposed method is the first computationally tractable algorithm for MNL contextual bandits with non-linear utilities that provably attains eO( T) regret.

Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexityofbrain circuits canbereduced, andeffectivelystudied bymeans of interactions betweencelltypes.

For example, if an individual appears in all of the sources, the trained model would be biased by data of this individual (there is imbalance caused by the use of more data from this particular individual than the others). Hence, this condition would ensure that such bias does not exist. Toaddress suchaproblem, wepropose a pre-training step to exclude such duplicated individuals. The pre-training step are summarized as follows: (1) Suppose thatanindividual canbeuniquely identified viaasetoffeatures. The causal effects are unidentifiable if the confounders are unobserved.

The proposed method does not distort the internal structure of the underlyingdistribution.

This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process model focuses on achieving metabolomics analytical objectives and on considering the nature of its involved data. MeKDDaM-SAGA was successfully used for guiding the process model execution in a number of metabolomics applications. It satisfies the requirements of the proposed process model design and execution. The software realises the process model layout, structure and flow and it enables its execution externally using various data mining and machine learning tools or internally using a number of embedded facilities that were built for performing a number of automated activities such as data preprocessing, data exploration, data acclimatization, modelling, evaluation and visualization. MeKDDaM-SAGA was developed using object-oriented software engineering methodology and was constructed in Java. It consists of 241 design classes that were designed to implement 27 use-cases. The software uses an XML database to guarantee portability and uses a GUI interface to ensure its user-friendliness. It implements an internal embedded version control system that is used to realise and manage the process flow, feedback and iterations and to enable undoing and redoing the execution of the process phases, activities, and the internal tasks within its phases.