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Found 15 papers, 1 papers with code
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
We propose a computational framework for characterizing multimodal long-form summarization and investigate the behavior of Claude 2. 0/2. 1, GPT-4/3. 5, and Command.
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits.
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier.
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset.
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness.
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterfactual explanations.
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales.
Optimal Stopping with Gaussian Processes
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets.
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
To improve the scalability of our approach to a greater number of task classes, we present an approximation based on state abstraction.
Counterfactual Shapley Additive Explanations
Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model.
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources
Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures.
Counterfactual Explanations for Arbitrary Regression Models
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models.
Non-Parametric Stochastic Sequential Assignment With Random Arrival Times
We consider a problem wherein jobs arrive at random times and assume random values.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.
