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Found 34 papers, 10 papers with code
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
Second, there are multiple measures of fairness of rankings, and optimization-based methods utilizing a single measure of fairness of rankings may produce rankings that are unfair with respect to other measures.
Learning quantum Hamiltonians at any temperature in polynomial time with Chebyshev and bit complexity
We consider the problem of learning local quantum Hamiltonians given copies of their Gibbs state at a known inverse temperature, following Haah et al. [2108. 04842] and Bakshi et al. [arXiv:2310. 02243].
Generating Likely Counterfactuals Using Sum-Product Networks
We show that the search for the most likely explanations satisfying many common desiderata for counterfactual explanations can be modeled using mixed-integer optimization (MIO).
Piecewise Polynomial Regression of Tame Functions via Integer Programming
We consider approximating so-called tame functions, a class of nonsmooth, nonconvex functions, with piecewise polynomial functions.
Joint Problems in Learning Multiple Dynamical Systems
Clustering of time series is a well-studied problem, with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory.
Group-blind optimal transport to group parity and its constrained variants
Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by sensitive attributes, e. g., gender, race.
Taming Binarized Neural Networks and Mixed-Integer Programs
There has been a great deal of recent interest in binarized neural networks, especially because of their explainability.
Improving the Validity of Decision Trees as Explanations
Here, we train a shallow tree with the objective of minimizing the maximum misclassification error across each leaf node.
Fairness in Forecasting of Observations of Linear Dynamical Systems
We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably.
Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactions
In a closed-loop view of the AI system and its users, the equal treatment concerns one pass through the loop.
Herd Routes: A Preventative IoT-Based System for Improving Female Pedestrian Safety on City Streets
With positive results from the initial testing of the proof-of-concept, further development could significantly contribute towards creating safer pedestrian routes through cities, and tackle the societal change that is required to improve female pedestrian safety in the long term.
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
In this paper, we show some foundational results regarding the flow and asymptotic properties of Langevin-type Stochastic Differential Inclusions under assumptions appropriate to the machine-learning settings.
Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification
Current quantum devices suffer imperfections as a result of fabrication, as well as noise and dissipation as a result of coupling to their immediate environments.
Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice
Optimization problems with set submodular objective functions have many real-world applications.
Predictability and Fairness in Load Aggregation and Operations of Virtual Power Plants
We show that even considering the non-linearity of the alternating-current model, this notion of predictability and fairness can be guaranteed for incrementally input-to-state stable (iISS) controllers, under mild assumptions.
Subgroup Fairness in Two-Sided Markets
In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem.
Trilevel and Multilevel Optimization using Monotone Operator Theory
We consider rather a general class of multi-level optimization problems, where a convex objective function is to be minimized subject to constraints of optimality of nested convex optimization problems.
A space-indexed formulation of packing boxes into a larger box
Current integer programming solvers fail to decide whether 12 unit cubes can be packed into a 1x1x11 box within an hour using the natural relaxation of Chen/Padberg.
Screening for an Infectious Disease as a Problem in Stochastic Control
There has been much recent interest in screening populations for an infectious disease.
Predictability and Fairness in Social Sensing
We consider the design of distributed algorithms that govern the manner in which agents contribute to a social sensing platform.
Fairness in Forecasting and Learning Linear Dynamical Systems
In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population.
Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS.
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection
Many real-world monitoring and surveillance applications require non-trivial anomaly detection to be run in the streaming model.
On-line Non-Convex Constrained Optimization
Time-varying non-convex continuous-valued non-linear constrained optimization is a fundamental problem.
A Fine-Grained Variant of the Hierarchy of Lasserre
There has been much recent interest in hierarchies of progressively stronger convexifications of polynomial optimisation problems (POP).
Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting
Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs).
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations.
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed ``sparse'' noise.
Robust Spectral Filtering and Anomaly Detection
We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise.
Resource Allocation with Population Dynamics
Many analyses of resource-allocation problems employ simplistic models of the population.
Pricing Vehicle Sharing with Proximity Information
For vehicle sharing schemes, where drop-off positions are not fixed, we propose a pricing scheme, where the price depends in part on the distance between where a vehicle is being dropped off and where the closest shared vehicle is parked.
Integer-Programming Ensemble of Temporal-Relations Classifiers
The extraction and understanding of temporal events and their relations are major challenges in natural language processing.
Matrix Completion under Interval Uncertainty
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints.
r-Extreme Signalling for Congestion Control
A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time.
