Neural network HDP improves virtual inertia control for non-inductive grids.
problem Traditional virtual inertia controllers are not suitable for non-inductive grids.
method Adaptive neural network heuristic dynamic programming (HDP) for optimal control.
result The proposed HDP controller outperforms traditional controllers in virtual inertia control.
DPDP combines neural heuristics with DP for vehicle routing problems.
problem Vehicle routing problems with large scale.
method Deep Policy Dynamic Programming (DPDP) that uses a neural network policy to prioritize and restrict the DP state space.
result DPDP improves upon classical DP algorithms and outperforms neural approaches for TSP, VRP, and TSPTW.
Optimal decision trees learned via dynamic programming and search.
problem Finding the best classification trees that are both accurate and compact.
method Dynamic programming and search techniques.
result Our algorithm learns optimal classification trees significantly faster and scales better than existing methods.
New algorithm tackles dynamic assortment optimization with knapsack constraints.
problem Optimizing retailer's assortment decisions under resource constraints with multi-nomial choice modeling.
method Epoch-based re-solving algorithm that transforms MNL's fractional structure into a linear program with slack variables.
result Regret scales logarithmically with time horizon and resource capacities.
Adapting neural networks to guide program optimization for better classifiers.
problem Learning differentiable programs with complex architectures.
method Formulating program optimization as a graph search problem, using neural networks as heuristic relaxations.
result Trained neural networks can guide combinatorial search for programmatic classifiers, improving accuracy and interpretability.
The well-known Influence Maximization (IM) problem has been actively studied by researchers over the past decade, with emphasis on marketing and social networks. Existing research have obtained solutions to the IM problem by obtaining the influence spread and utilizing the property of submodularity. This paper is based…
This paper tackles minimizing clipped convex functions with heuristics and mixed-integer convex programming.
problem Minimizing a sum of clipped convex functions.
method Heuristics and mixed-integer convex programming.
result Heuristics can find good solutions, and the perspective transformation yields tractable lower bounds.
New heuristics improve genetic programming's parent selection for classification problems.
problem Improving genetic programming's parent selection for classification tasks.
method Proposed three heuristics inspired by specific classifiers' characteristics, using similarity measures.
result Combination of agreement-based selection and random selection outperforms classical and state-of-the-art schemes.
Within many real-world networks the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal …
Optimizes fund portfolio updates using linear programming and heuristic search.
problem Efficiently updating mutual fund portfolios with various instruments.
method Linear programming and heuristic search approaches.
result Cost improvements over traditional methods.
Framework uses neural networks to learn fitness functions for machine programming.
problem Automatic software generation and crafting effective fitness functions.
method Genetic algorithms augmented with neural networks and a search heuristic.
result Framework discovers more correct programs with fewer candidate generations.
Nemo improves WS learning pipeline by 20%.
problem Creating effective labeling heuristics for weak supervision.
method Interactive procedure for designing heuristics, strategic data selection, contextualization of heuristics.
result Improves WS learning pipeline by 20%.
Optimizes sliding window approach for tracking Gaussian densities.
problem Improving tracking performance of Gaussian density estimation.
method Theoretical analysis of sliding window Gaussian Kernel Density Estimators.
result Empirical evidence shows improved tracking performance with optimal weight sequence.
The paper develops algorithms for Boolean matrix factorization using IP and heuristics.
problem Approximating binary input matrices as products of smaller binary factors.
method Alternating optimization with integer programming and greedy/local-search heuristics.
result Proposed methods improve scalability and performance compared to existing techniques.
Paper shows re-solving heuristics have constant regret for price-based revenue management.
problem Optimal pricing policies for revenue management with time constraints.
method Proves re-solving heuristics have O(1) regret compared to optimal policies. result Improved regret bound to O(1) from O(lnT), complemented by Ω(lnT) gap with fluid model. We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the mo…
Two heuristics solve dynamic multiple travelling salesmen problems.
problem Dynamic routing with unknown customers.
method Balanced dynamic closest vehicle heuristic and balanced dynamic assignment vehicle heuristic.
result Continuous approximation models for strategic dynamic routing.
ProGraML uses graph-based machine learning to improve program optimization and analysis.
problem Improving program optimization and analysis with machine learning.
method Low-level, language agnostic graph representation and message passing neural networks.
result ProGraML achieves an average 94.0 F1 score on a benchmark dataset, significantly outperforming state-of-the-art approaches.
This study improves lookahead Bayesian optimization using rollout approximation.
problem Error propagation in lookahead Bayesian optimization due to model mis-specification.
method Proves rollout's improving nature in lookahead BO and provides guidelines for choosing the rolling horizon.
result Empirical results show rollout improves over myopic and non-myopic BO algorithms.
RL improves IP solver performance by learning to select cutting planes.
problem Improving the performance of IP solvers through heuristic optimization.
method Employing reinforcement learning to intelligently select cutting planes in the Cutting Plane Method.
result Trained RL agent significantly outperforms human-designed heuristics across various IP tasks.
New formulation of MIL using shapelets for better classifier of bags.
problem Finding a good classifier of bags based on shapelets.
method Formulation using all possible shapelets, reduced to DC programs, and heuristic options.
result Richer class of classifiers with theoretical justification and empirical validation.
New algorithm solves complex stopping problems with robust optimization.
problem Solving complex stochastic optimal stopping problems.
method Simulation-based robust optimization with exact reformulation as a zero-one bilinear program.
result Developed polynomial-time heuristics and algorithms for practical solution.
Paper proposes a COP model for Algo trading using LQR.
problem Complexities in child order placement in Algo trading.
method Stochastic LQR model for passive limit orders and aggressive takeout orders.
result Closed-form solutions for optimal child order placement.
Proposes a method to allocate time budgets in mixed criticality systems.
problem Managing execution time variability in mixed criticality systems.
method Quantifies execution time variability using statistical dispersion parameters and proposes a heuristic to allocate time budgets.
result The proposed heuristic reduces the probability of exceeding allocated budgets.
Study uses DHS to classify anemia types using CBC indices.
problem Anemia classification for medical purposes.
method Dynamic Harmony Search (DHS) applied to CBC indices.
result DHS outperforms other models in anemia classification.
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal…
Graph clustering improved using Boltzmann machine heuristics.
problem Graph clustering to form densely connected clusters.
method Two mathematical programming formulations, two variations of Boltzmann machine heuristic.
result Boltzmann machine provides superior solutions and faster computation times.
In this paper, we analyze dynamic programming as a novel approach to solve the problem of maximizing the profits of a bank. The mathematical model of the problem and the description of a bank's work is described in this paper. The problem is then approached using the method of dynamic programming. Dynamic programming m…
This paper explores the use of Column Generation (CG) techniques in constructing univariate binary decision trees for classification tasks. We propose a novel Integer Linear Programming (ILP) formulation, based on root-to-leaf paths in decision trees. The model is solved via a Column Generation based heuristic. To spee…
We consider the optimization of active extension portfolios. For this purpose, the optimization problem is rewritten as a stochastic programming model and solved using a clever multi-start local search heuristic, which turns out to provide stable solutions. The heuristic solutions are compared to optimization results o…
Optimizing the execution time of tensor program, e.g., a convolution, involves finding its optimal configuration. Searching the configuration space exhaustively is typically infeasible in practice. In line with recent research using TVM, we propose to learn a surrogate model to overcome this issue. The model is trained…
The paper optimizes dynamic scheduling for ring architectures in deep learning training.
problem Optimizing deep learning training times with ring architectures.
method Formulated a non-convex, non-linear, NP-hard integer programming problem and developed a doubling heuristic.
result Dynamic scheduling can significantly reduce job completion times in ring architectures.
Approximate dynamic programming algorithms, such as approximate value iteration, have been successfully applied to many complex reinforcement learning tasks, and a better approximate dynamic programming algorithm is expected to further extend the applicability of reinforcement learning to various tasks. In this paper w…
This paper improves dynamic hedging accuracy using genetic programming to forecast implied volatilities.
problem Improving the accuracy of dynamic hedging using implied volatilities.
method The paper uses genetic programming to forecast implied volatilities and tests the performance of these forecasts in dynamic hedging strategies.
result Genetic programming-generated implied volatilities improve hedging accuracy compared to static training methods.
Proposes individual fairness for clustering, making data points prefer their own cluster.
problem No fair clustering for clustering data points.
method Introduces a new fairness notion for clustering and studies its feasibility and heuristics.
result Individual fairness for clustering is NP-hard in general but feasible for one-dimensional data.
dynestyx: A library for probabilistic programming of dynamical systems
problem integrating state-space models into probabilistic programming languages
method a unified interface for specifying priors and performing inference
result principled uncertainty quantification for state and parameters
New method uses dynamic programming for meta continual learning.
problem Challenges of generalization and catastrophic forgetting in sequential learning.
method Developed a theoretical framework using dynamic programming for meta continual learning.
result Theoretical and practical method achieves better accuracy than existing methods.
PLANS synthesizes programs from noisy inputs using neural specs and filtering.
problem Synthesizing robust programs from noisy, raw inputs.
method Hybrid model combining neural extraction and rule-based synthesis with noise filtering.
result State-of-the-art performance in diverse environments with no ground-truth training.
NEST optimizes deep learning training by placing devices efficiently across networks and memory.
problem Inefficient device placement in distributed deep learning leads to high communication and memory overhead.
method NEST uses network-, compute-, and memory-aware dynamic programming to optimize device placement.
result NEST achieves up to 2.43 times higher throughput and better memory efficiency.
In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticipating the instance-sp…
DynamicPPL speeds up probabilistic modeling in Julia.
problem Developing and executing complex dynamic probabilistic models efficiently.
method Modular Julia library with a DSL, tracing data structure, and contextual dispatch.
result Achieves computational performance close to or better than Stan.
We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying ML to problems in new domains. There is a growing divide in approaches to building systems: on the one hand, programming leverages human expe…
Proposes proactive bed requests to reduce ED boarding and patient wait times.
problem Reduces ED boarding and patient wait times by proactively requesting inpatient beds.
method Formulates as a Markov decision process, uses predictions of admission probability and time to disposition to guide bed requests.
result Proactive aggregate bed requests can reduce boarding times by 30-70% and length of stay by 6-15%.
Approximate dynamic programming is a popular method for solving large Markov decision processes. This paper describes a new class of approximate dynamic programming (ADP) methods- distributionally robust ADP-that address the curse of dimensionality by minimizing a pessimistic bound on the policy loss. This approach tur…
Framework optimizes battery storage for markets by separating long-term degradation from short-term market dynamics.
problem Intractable computation due to timescale mismatch between battery degradation and market dynamics.
method Approximate dynamic programming with value function approximation and pseudo-time encoding.
result Policy outperforms benchmarks in real-time market scenarios.
New tools for uncertainty in dynamical systems without distribution assumptions.
problem Uncertainty representation in dynamical systems without distributional assumptions.
method Kernel mean embedding and kernel probabilistic programming.
result Distribution-free representation, comparison, and propagation of uncertainties.
Much combinatorial optimisation problems constitute a non-polynomial (NP) hard optimisation problem, i.e., they can not be solved in polynomial time. One such problem is finding the shortest route between two nodes on a graph. Meta-heuristic algorithms such as A∗ along with mixed-integer programming (MIP) methods …
We implement a market microstructure model including informed, uninformed and heuristic-driven investors, which latter behave in line with loss-aversion and mental accounting. We show that the probability of informed trading (PIN) varies significantly during 2008. In contrast, the probability of heuristic-driven tradin…