Danial Dervovic
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View article: ELATE: Evolutionary Language model for Automated Time-series Engineering
ELATE: Evolutionary Language model for Automated Time-series Engineering Open
Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critical for enhancing model performance, but is often manual and …
View article: Surrogate-Assisted Monte-Carlo Tree Search in Facility Location and Beyond (Extended Abstract)
Surrogate-Assisted Monte-Carlo Tree Search in Facility Location and Beyond (Extended Abstract) Open
Combinatorial problems abound in industry. A persistent issue encountered using search-based solutions is that evaluating particular nodes may be expensive. As an example, organisations frequently adjust their facilities network by opening…
View article: MAFE: Multi-Agent Fair Environments for Decision-Making Systems
MAFE: Multi-Agent Fair Environments for Decision-Making Systems Open
Fairness constraints applied to machine learning (ML) models in static contexts have been shown to potentially produce adverse outcomes among demographic groups over time. To address this issue, emerging research focuses on creating fair s…
View article: Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation
Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation Open
Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This paper introduces a novel approach to identify specific image …
View article: Are Logistic Models Really Interpretable?
Are Logistic Models Really Interpretable? Open
The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the information provided. That said, one of the simplest AI classifi…
View article: Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models Open
Models for natural language and images benefit from data scaling behavior: the more data fed into the model, the better they perform. This 'better with more' phenomenon enables the effectiveness of large-scale pre-training on vast amounts …
View article: Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports Open
As large language models (LLMs) expand the power of natural language processing to handle long inputs, rigorous and systematic analyses are necessary to understand their abilities and behavior. A salient application is summarization, due t…
View article: Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization Open
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits. In this paper, we examine a particular class of facility location problems. Our o…
View article: Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Balancing Fairness and Accuracy in Data-Restricted Binary Classification Open
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a classifier may not have direct access to sensitive attributes,…
View article: Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data Open
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. To prevent leakage of sensitive data, we consider using different…
View article: A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness Open
Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require differ…
View article: On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations Open
Explainable Artificial Intelligence (XAI) has received widespread interest in\nrecent years, and two of the most popular types of explanations are feature\nattributions, and counterfactual explanations. These classes of approaches have\nbe…
View article: Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning Open
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…
View article: Optimal Stopping with Gaussian Processes
Optimal Stopping with Gaussian Processes Open
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that structural properties commonly exhibited by financial time ser…
View article: Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction Open
We consider a novel queuing problem where the decision-maker must choose to accept or reject randomly arriving tasks into a no buffer queue which are processed by N identical servers. Each task has a price, which is a positive real number,…
View article: Counterfactual Shapley Additive Explanations
Counterfactual Shapley Additive Explanations Open
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. In the actionable recourse setting, wherein the goal of the explanations is to…
View article: Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction Open
We consider a novel queuing problem where the decision-maker must choose to accept or reject randomly arriving tasks into a no buffer queue which are processed by $N$ identical servers. Each task has a price, which is a positive real numbe…
View article: Tradeoffs in Streaming Binary Classification under Limited Inspection Resources
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources Open
Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures. These alerts often need to be manually investigated by specialists. Given the oper…
View article: Non-Parametric Stochastic Sequential Assignment With Random Arrival Times
Non-Parametric Stochastic Sequential Assignment With Random Arrival Times Open
We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constr…
View article: Counterfactual Explanations for Arbitrary Regression Models
Counterfactual Explanations for Arbitrary Regression Models Open
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regres…
View article: Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set Open
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability. To ensure accurate prediction, the model needs to be calibrated and validated against real data. Conventional methods approach t…
View article: Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations Open
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing. Furthermore, simulation is important for validation of hand-coded trading strategies and for te…
View article: Perfect weak modular product graphs
Perfect weak modular product graphs Open
In this paper we enumerate the necessary and sufficient conditions for the weak modular product of two simple graphs to be perfect. The weak modular product differs from the direct product by also encoding non-adjacencies of the factor gra…
View article: Constructing graphs with limited resources
Constructing graphs with limited resources Open
We discuss the amount of physical resources required to construct a given graph, where vertices are added sequentially. We naturally identify information -- distinct into instructions and memory -- and randomness as resources. Not surprisi…
View article: Quantum linear systems algorithms: a primer
Quantum linear systems algorithms: a primer Open
The Harrow-Hassidim-Lloyd (HHL) quantum algorithm for sampling from the solution of a linear system provides an exponential speed-up over its classical counterpart. The problem of solving a system of linear equations has a wide scope of ap…
View article: For every quantum walk there is a (classical) lifted Markov chain with faster mixing time
For every quantum walk there is a (classical) lifted Markov chain with faster mixing time Open
Quantum walks on graphs have been shown in certain cases to mix quadratically faster than their classical counterparts. Lifted Markov chains, consisting of a Markov chain on an extended state space which is projected back down to the origi…
View article: Weak Modular Product of Bipartite Graphs, Bicliques and Isomorphism
Weak Modular Product of Bipartite Graphs, Bicliques and Isomorphism Open
A 1978 theorem of Kozen states that two graphs on $n$ vertices are isomorphic if and only if there is a clique of size $n$ in the weak modular product between the two graphs. Restricting to bipartite graphs and considering complete biparti…