Solves partial assignment problems using random clique complexes.
problem Partial assignment problems, especially with severe occlusions and distortions.
method Formulate as matching random clique complexes, analyze k-skeletons, match adjacency matrices, consider geometric neighbourhoods.
result Outperforms diverse matching algorithms significantly.
EgalMAB solves fair resource allocation in stochastic bandits.
problem Fair resource allocation in a stochastic multi-armed bandit setting.
method Design and analysis of UCB-based policy EgalUCB.
result Established upper bounds on cumulative regret.
This letter tackles channel assignment in uplink wireless communication systems.
problem Maximizing the sum rate of all users in uplink wireless communication systems with integer channel assignment constraints.
method A convex optimization based algorithm is used to find the optimal channel assignment. Machine learning approaches, including CNNs, FNNs, random forest, and GRUs, are employed to reduce computation time.
result Machine learning methods largely reduce computation time with slightly compromised prediction accuracy.
New algorithms assign credit to past decisions based on hindsight.
problem Efficient credit assignment in reinforcement learning.
method Explicit credit assignment using new data in hindsight.
result Value functions can be rewritten to include hindsight credit assignment.
Linear-time algorithm for optimal assignment in graph matching.
problem Finding optimal assignments between graph vertices efficiently.
method Developed an algorithm for linear-time optimal assignment using tree distances.
result Approximated edit distance between graphs in linear time.
In this paper we study speaker linking (a.k.a.\ partitioning) given constraints of the distribution of speaker identities over speech recordings. Specifically, we show that the intractable partitioning problem becomes tractable when the constraints pre-partition the data in smaller cliques with non-overlapping speakers…
Study minimax regret in sequential probability assignment with and without side information.
problem Minimax regret analysis in sequential probability assignment.
method Upper and lower bounds on minimax regret using square-root entropy.
result Lower bound matches upper bound for Donsker classes, up to log factors.
Optimizes balanced treatment assignment for experiments.
problem Balancing treatment groups in experiments for optimal results.
method Optimization of a two-sample test, using minimum spanning tree test.
result Optimal assignment algorithm with polynomial time complexity.
The paper develops methods to estimate optimal treatment sequences under policy constraints.
problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.
Study on optimal rates for sequential probability assignment using smoothed analysis.
problem Optimal rates for sequential probability assignment under smoothed adversaries.
method General-purpose reduction from minimax rates to transductive learning, development of an efficient algorithm using MLE oracle.
result Optimal (logarithmic) fast rates for parametric and finite VC dimension classes, sublinear regret for general classes.
COCOA improves credit assignment in reinforcement learning by measuring contributions to rewards.
problem Improving sample efficiency in reinforcement learning through better credit assignment methods.
method Counterfactual Contribution Analysis (COCOA) for precise credit assignment.
result COCOA achieves lower bias and variance compared to Hindsight Credit Assignment (HCA), improving reinforcement learning performance.
New framework predicts future shipments with less error and saves labor.
problem Optimizing storage assignment in warehouses under uncertainty.
method Introduces a new framework that combines neural networks to predict future shipments and integrates it into a storage assignment system.
result Achieves up to 29% decrease in MAPE compared to CNN-LSTM on unseen future shipments.
Detects ghostwriting in high school assignments.
problem Increasing problem of students hiring ghostwriters.
method Trained deep neural networks on large dataset.
result Achieved 0.875 accuracy and 0.947 AUC score.
Paper tackles inconsistent CATE estimation across group assignments.
problem Inconsistent learning behavior for the same instance across different group assignments.
method CLAGA method to eliminate inconsistency.
result Significant performance improvements with CLAGA method.
We consider the problem of learning soft assignments of N items to K categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity mat…
Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments. These marginals correspond to how frequently each variable is set to true among satisfying assignments, and …
CONE evaluates treatment assignment functions using networked observational data to mitigate hidden confounding bias.
problem Evaluate treatment assignment functions using networked observational data with hidden confounders.
method CONE framework that learns partial representations of latent confounders and combines them for counterfactual evaluation.
result Network information mitigates hidden confounding bias in counterfactual evaluation.
Graph cuts find global optima for Potts models in slight perturbations.
problem Finding optimal solutions in Potts models with graph cuts.
method α-expansion algorithm for MAP inference, with certification for perturbations.
result All local minima are global minima in slight perturbations, and solutions are close to original.
New kernel learns optimal bijection for graph classification.
problem Graph classification with optimal bijection.
method Multiple kernel learning for Weisfeiler-Lehman assignment kernels.
result Feasibility and effectiveness of the approach demonstrated.
This paper studies the problem of inferring a global preference based on the partial rankings provided by many users over different subsets of items according to the Plackett-Luce model. A question of particular interest is how to optimally assign items to users for ranking and how many item assignments are needed to a…
Paper introduces a structured prediction approach for multi-agent reinforcement learning.
problem Scaling and generalization in cooperative multi-agent reinforcement learning.
method Structured prediction approach using learned scoring models for task assignment.
result The approach can generalize to larger problems with more agents and tasks.
In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, …
Generative model for joint discrete distributions using randomized assignment flows.
problem Efficiently representing and sampling from complex joint distributions of discrete variables.
method Randomized assignment flows on the statistical submanifold of factorizing distributions.
result Our model can efficiently represent and sample from any target distribution and assess likelihood of unseen data points.
Study compares methods for treatment assignment, finding A-learner best for playlist generation.
problem Treatment assignment in various applications.
method Three classes of algorithms: O-learner, E-learner, A-learner.
result Optimizing for outcomes or causal effects does not lead to optimal treatment assignments.
Algorithm uses RBM to solve matching problems on weighted graphs.
problem Perfect matching problem on bipartite weighted graphs.
method Iterative RBM algorithm to maximize energy function and assignment.
result Algorithm successfully solves real-world matching problems.
Various applications involve assigning discrete label values to a collection of objects based on some pairwise noisy data. Due to the discrete---and hence nonconvex---structure of the problem, computing the optimal assignment (e.g.~maximum likelihood assignment) becomes intractable at first sight. This paper makes prog…
GOAT improves graph matching speed and accuracy using optimal transport.
problem Efficiently matching large graphs in various applications.
method Replaces linear assignment with optimal transport methods.
result GOAT provides improvements in speed and accuracy.
A new method uses optimal transport for semi-supervised classification.
problem Semi-supervised learning with limited labeled data.
method Optimal transport formulation with Sinkhorn iteration for label assignment.
result Improved performance on CIFAR-10, CIFAR-100, and SVHN datasets compared to FixMatch.
Study tackles causal effects of close contact on MRSA infections from entangled treatment data.
problem Estimating causal effects of close contact on MRSA infections from observational data with entangled treatments.
method NEAT method that models treatment assignment mechanism and mitigates confounding biases.
result NEAT method effectively estimates causal effects from entangled treatment data.
Fuses posterior distributions from different datasets using KL divergence.
problem Combining information from multiple datasets with uncertainty.
method Mean field assumption, KL divergence, assign-and-average approach.
result Efficient non-parametric algorithm for fused model computation.
Paper solves Gromov-Wasserstein for point clouds efficiently.
problem Quantifying similarity between two formations or shapes.
method Reformulates QAP as low-rank concave quadratic optimization problem.
result Global solution for large-scale problems with thousands of points.
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
problem Maximizing returns while minimizing risk in portfolio optimization.
method Incorporates pre-assignment constraints and margin trading strategies using Genetic Algorithms and Particle Swarm Optimization.
result Metaheuristic-based portfolio optimization yields superior risk-adjusted returns compared to traditional methods.
Unified view on selective credit assignment for reinforcement learning.
problem Efficient credit assignment in reinforcement learning.
method Unified temporal-difference algorithms with selective weightings.
result New algorithms for backward credit assignment and off-policy learning.
Role mining tackles the problem of finding a role-based access control (RBAC) configuration, given an access-control matrix assigning users to access permissions as input. Most role mining approaches work by constructing a large set of candidate roles and use a greedy selection strategy to iteratively pick a small subs…
New system assigns vehicles to routes for cost and energy efficiency.
problem Optimizing vehicle route assignment for cost and energy efficiency.
method Machine learning-based neural network algorithm to estimate energy consumption and provide real-time recommendations.
result Demonstrated efficient vehicle assignment for medium and heavy duty trucks.
The paper explores how planning with models improves credit assignment in reinforcement learning.
problem Improving credit assignment in reinforcement learning.
method Investigates the use of forward and backward models for planning in reinforcement learning.
result Establishes the relative merits and limitations of forward and backward planning mechanisms.
We consider the problem of automated assignment of papers to reviewers in conference peer review, with a focus on fairness and statistical accuracy. Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the commonly used objective of maximizing the total quality over a…
New framework for modular reinforcement learning reduces sample complexity.
problem Achieving independent credit assignment in reinforcement learning.
method Defining modular credit assignment as minimizing algorithmic mutual information, introducing modularity criterion for causal analysis.
result Single-step temporal difference action-value methods meet the modularity criterion, improving sample efficiency.
Review and compare sorting model selection methods for preference disaggregation.
problem Selecting a representative sorting model from multiple compatible models.
method Reviewed and proposed new procedures for selecting sorting models, including robust assignment rule.
result Identified most efficient procedures in terms of classification accuracy and robustness.
We consider the problem of learning a measure of distance among vectors in a feature space and propose a hybrid method that simultaneously learns from similarity ratings assigned to pairs of vectors and class labels assigned to individual vectors. Our method is based on a generative model in which class labels can prov…
A new algorithm improves solving QAP with better performance.
problem Solving the Quadratic Assignment Problem (QAP) efficiently.
method Estimation of Distribution Algorithms (EDAs) with a non-parametric distance-based Mallows model.
result The proposed algorithm outperforms existing methods for QAP.
Paper defines predictive multiplicity and measures its severity in classification problems.
problem Challenges in machine learning due to competing models with conflicting predictions.
method Formal measures and integer programming tools for linear classification problems.
result Real-world datasets may admit competing models with wildly conflicting predictions.
LICA learns credit assignment for cooperative agents without explicit formulation.
problem Credit assignment among cooperative agents.
method Centralized critic as hypernetwork, adaptive entropy regularization.
result Significantly outperforms previous methods on benchmarks.
Complex systems, such as airplanes, cars, or financial markets, produce multivariate time series data consisting of a large number of system measurements over a period of time. Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior. An important problem in this …
Quantum annealing speeds up extreme clustering.
problem Efficiently grouping large datasets into many representative clusters.
method Distributed quantum annealing method.
result Optimal clustering assignments achieved under separability assumption.
We present new results concerning the solvability, of lack thereof, in the Cauchy problem for the debar operator, with initial values assigned on a weakly pseudoconvex hypersurface, and provide illustrative examples.
Solves GAN mode collapse by assigning minibatches to multiple discriminators.
problem Mode collapse in GANs where models generate similar samples.
method Multiple discriminators, microbatching, and changing tasks.
result Promotes sample diversity in generated sets.
New bounds on minimax regret for sequential probability assignment using logarithmic loss.
problem Minimizing regret in sequential probability assignment against arbitrary experts.
method Using self-concordance property of logarithmic loss to derive tight bounds.
result Tight bounds on minimax regret for various expert classes.