Paper introduces a cost function for balancing accuracy and reliability in probabilistic forecasts.
problem Assigning uncertainties to single-point predictions in probabilistic forecasts.
method Introduces an Accuracy-Reliability cost function and derives analytic formula for Gaussian distributions. Employs a two-objective optimization problem to estimate variance in heteroskedastic regression.
result The method can accurately estimate variance in heteroskedastic regression problems, as shown in synthetic data examples.
New Variational InfoMax objective improves neural network performance.
problem Optimizing neural networks using Bayesian Inference and Information Bottleneck.
method Derive Variational InfoMax (VIM) objective that maximizes InfoMax directly.
result VIM improves model performance in accuracy, robustness, and representation quality.
This survey reviews portfolio selection problem for long-term horizon. We consider two objectives: (i) maximize the probability for outperforming a target growth rate of wealth process (ii) minimize the probability of falling below a target growth rate. We study the asymptotic behavior of these criteria formulated as l…
MOSS optimizes decision rules for accuracy and stability.
problem Constructing stable sets of decision rules.
method Multi-objective optimization framework incorporating sparsity, accuracy, and stability.
result MOSS outperforms state-of-the-art rule ensembles in predictive performance and stability.
New scalable method balances hospital profit status and heart attack outcomes.
problem Balancing covariate distributions and minimizing weight dispersion in large datasets.
method Combines kernel basis expansion and convex optimization for efficient and flexible weighting.
result For-profit hospitals use interventional cardiology similarly to other hospitals but have higher mortality and readmission rates.
Optimizes privacy vs. utility trade-off through optimal transport.
problem Balancing privacy and utility in strategic information.
method Formalizes as an optimization problem with regularization, using Sinkhorn loss.
result The Sinkhorn loss naturally emerges, making the problem efficiently solvable.
In this paper, we accomplish two objectives: First, we provide a new mathematical characterization of the value function for impulse control problems with implementation delay and present a direct solution method that differs from its counterparts that use quasi-variational inequalities. Our method is direct, in the se…
Proposes TFDF to learn transferable and discriminative features for unsupervised domain adaptation.
problem Difficult to induce supervised classifier without labeled data in unsupervised domain adaptation.
method TFDF optimizes transferability and discriminability by aligning distributions and minimizing class confusion.
result TFDF achieves better performance on real-world datasets compared to existing methods.
End-to-end CCA optimizes both discriminative and latent space projections for multi-view learning.
problem Lack of class label information in CCA for multi-view learning tasks.
method Simultaneously optimizes a CCA-based and a task objective in an end-to-end manner to learn a non-linear CCA projection.
result Significant improvement in cross-view classification, regularization with a second view, and semi-supervised learning.
New MODRL framework finds Pareto-optimal solutions.
problem Multi-objective reinforcement learning problems.
method Deep Q-networks, multi-policy, linear and non-linear action selection.
result Effective Pareto-optimal solutions found on benchmark problems.
A framework for robust exploration in reinforcement learning under ambiguity.
problem Optimal stopping under ambiguity in reinforcement learning.
method Continuous-time robust reinforcement learning framework using g-expectation and backward stochastic differential equations. result Constructs a robust exploratory stopping time approximating the optimal stopping time under ambiguity.
Unified framework for active and passive portfolio management combining outperformance and tracking.
problem Combining active and passive portfolio management objectives.
method Dynamic asset allocation using stochastic control techniques.
result Explicit closed-form expressions for optimal asset allocation.
GOPC algorithm finds global optimal clusters efficiently.
problem NP-hard combinatorial optimization for clustering.
method Path-based clustering algorithm using medoids and minimax distances.
result GOPC algorithm finds all types of clusters efficiently.
Framework combines adversarial training and provable robustness for neural networks.
problem Training certifiably robust neural networks with provable robustness guarantees.
method Formulates joint optimization problem with adversarial and provable robustness objectives; develops gradient-descent technique.
result Consistently matches or outperforms prior approaches for provable l infinity robustness on MNIST and CIFAR-10.
Bayesian method reduces misclassification errors in ranking Pareto-optimal solutions.
problem Identifying true Pareto-optimal solutions in noisy multiobjective optimization.
method Sequential allocation of extra samples using stochastic kriging to build predictive distributions.
result The proposed method outperforms existing algorithms in reducing misclassification errors.
Develops CPL for optimal prediction set length and validity.
problem Balancing conditional validity and length efficiency in conformal prediction.
method Conformal Prediction with Length-Optimization (CPL).
result Achieves optimal prediction set length while maintaining conditional validity.
A new loss function HUG decouples and generalizes neural collapse.
problem Neural collapse limits in deep learning models.
method Hyperspherical uniformity gap (HUG) as a unified framework.
result HUG decouples and generalizes neural collapse, improving model flexibility and robustness.
New bounds for weighted ERM in networked data.
problem Learning from networked data with unknown target values.
method General weighted ERM, new universal risk bounds, FPTAS.
result Appropriate weights for networked examples.
This paper is based on the author's talk at 1997 Taniguchi Symposium ``Integrable Systems and Algebraic Geometry''. We consider an approach to the theory of Frobenius manifolds based on the geometry of flat pencils of contravariant metrics. It is shown that, under certain homogeneity assumptions, these two objects are …
New algorithm achieves nearly optimal regret with one-pass updates for GLB problems.
problem Generalized linear bandits with non-linear reward distributions.
method Jointly efficient algorithm using OMD estimator with one-pass updates.
result Nearly optimal regret bound with O(1) time and space complexities per round. Advocates Tversky's model for image similarity learning.
problem Learning Tversky similarity measures from image data.
method Computational approach using Tversky's ratio model.
result Performs well compared to existing methods on image datasets.
MERL uses evolutionary and gradient-based methods to optimize sparse team-based and dense agent-specific rewards in multiagent coordination.
problem Training multiagent reinforcement learning policies on sparse team-based rewards is difficult and relying solely on agent-specific rewards is sub-optimal.
method MERL employs a split-level training platform with an evolutionary algorithm and a gradient-based optimizer, transferring skills between the two processes.
result MERL significantly outperforms state-of-the-art methods on coordination benchmarks.
Algorithm optimizes two objectives in bandits: minimizing regret and identifying best arm.
problem Balancing exploration and exploitation for optimal performance in multi-armed bandits.
method Design and analysis of BoBW-lil'UCB(γ) algorithm, establishing lower bounds. result BoBW-lil'UCB(γ) achieves optimal performance for RM or BAI under different γ values. Improved detection of burnt areas in satellite images using evolved hyper-features.
problem Radiometric variations across satellite images and different datasets.
method Understanding feature spaces, training on multi-image datasets, evolving hyper-features, and optimizing for different classifiers.
result Training on multi-image datasets improves model generalization, and evolved hyper-features enhance classifier performance.
A new loss function for set reconstruction without order consideration.
problem Reconstructing sets of elements without considering their order.
method Set Cross Entropy, a permutation-invariant loss function.
result Natural information-theoretic interpretation and successful evaluations in tasks.
ADS explains object differences by quantifying and removing underlying properties.
problem Explaining differences between two object images.
method Align-Deform-Subtract (ADS) framework that uses semantic alignments and iterative quantification/removal of differences.
result ADS provides disentangled error measures explaining object differences in terms of underlying properties.
Let X be a smooth elliptic fibration over a smooth base B. Under mild assumptions, we establish a Fourier-Mukai equivalence between the derived categories of two objects, each of which is an O^* gerbe over a genus one fibration which is a twisted form of X. The roles of the gerbe and the twist are interchanged by our d…
Teaches diverse students in a classroom setting with minimal examples.
problem Teaching a diverse group of students with varying initial states and learning rates.
method Proves an optimal teaching strategy with O(min{d,N} log(1/eps)) examples, robust to limited knowledge, and studies workload-cost trade-offs.
result Teaching a target concept to the entire classroom using optimal number of examples, validated by experiments.
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel that measures how different or similar two objects are. This approach has been …
Models learn spatial templates from implicit language, predicting spatial arrangements with high accuracy.
problem Predicting spatial arrangements from implicit spatial language.
method Simple neural-based models leveraging annotated images and structured text.
result Models can predict spatial arrangements from implicit spatial language with high accuracy, even for unseen objects.
Given a collection of data points, non-negative matrix factorization (NMF) suggests to express them as convex combinations of a small set of `archetypes' with non-negative entries. This decomposition is unique only if the true archetypes are non-negative and sufficiently sparse (or the weights are sufficiently sparse),…
New algorithm improves non-submodular objective maximization in adversarial settings.
problem Maximizing non-submodular objectives under adversarial deletions.
method Oblivious-Greedy algorithm for non-submodular objectives.
result First constant-factor guarantees for non-submodular objectives.
The main contribution of this article is a new prior distribution over directed acyclic graphs, which gives larger weight to sparse graphs. This distribution is intended for structured Bayesian networks, where the structure is given by an ordered block model. That is, the nodes of the graph are objects which fall into …
Algorithm maps trade-off between clustering fidelity and representation size.
problem Optimizing trade-off between clustering fidelity and representation size.
method Introduces primal Deterministic Information Bottleneck (DIB) problem for discrete search spaces.
result Shows richer Pareto frontier over Lagrangian relaxation.
NSGA-II optimizes Bayesian Network structure learning, achieving better likelihood and fewer arcs.
problem Learning the structure of Bayesian Networks is NP-complete and leads to multimodal fitness landscapes.
method Multi-objective optimization using NSGA-II with likelihood and arc count as objectives.
result NSGA-II can converge to solutions with better likelihood and fewer arcs than classic methods.
A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in st…
Method solves learning problem with hierarchical control objectives.
problem Learning high-dimensional nonlinear functions with model validation accuracy.
method Successive approximation method in functional spaces for hierarchical optimal control.
result Nested algorithm for solving optimal control problem.
Introduces a new equivalence for singular foliations and their groupoids.
problem Preserving transverse geometry in singular foliations.
method Introduces a new equivalence relation for singular foliations and connects it to holonomy groupoids.
result Establishes a connection between singular foliations and their associated holonomy groupoids.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
problem Efficiently optimizing multiple expensive and noisy black-box functions with redundant objectives.
method Proposes a metric to identify redundant objectives and a Bayesian optimization algorithm to stop evaluating them.
result Reduces computational cost by stopping evaluation of redundant objectives, improving efficiency.
Deep network predicts action sequences for complex tasks from a scene image.
problem Scalable task and motion planning from initial scene images.
method Deep convolutional recurrent neural network that predicts action sequences.
result Predicts promising action sequences, reducing motion planning problems.
We formulate higher order variations of a Lagrangian in the geometric framework of jet prolongations of fibered manifolds. Our formalism applies to Lagrangians which depend on an arbitrary number of independent and dependent variables, together with higher order derivatives. In particular, we show that the second varia…
CFRecs uses counterfactual reasoning to improve graph-based recommendations in real estate.
problem Improving model interpretability and actionable insights in graph-based recommender systems.
method A two-stage architecture combining GNN and Graph-VAE to propose minimal yet impactful changes in graph structure and node attributes.
result Demonstrates effectiveness in delivering actionable recommendations for home buyers and sellers.
FlexCMH learns effective hashing codes from weakly-paired data.
problem Cross-modal hashing assumes perfect correspondence between samples, which is unrealistic.
method FlexCMH uses clustering-based matching to find potential correspondence and jointly optimizes it with hashing functions.
result FlexCMH achieves significantly better results than state-of-the-art methods.
In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that contain information of the cluster structure in high-dimensional space. Based on this …
Similarity measure for Gaussian process predictive distributions.
problem Comparing predictive distributions of Gaussian processes for correlated functions.
method Developed a similarity metric to compare predictive distributions of Gaussian processes.
result Gaussian process predictive distributions can be compared and one is enough to model two correlated functions.
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new appro…
ADAC uses analogous policies to improve RL exploration without sacrificing stability.
problem Improving RL exploration without compromising stability and expressiveness.
method Disentangled actor-critic approach with analogous pairs of actors and critics.
result Empirical evaluation shows ADAC outperforms alternatives in challenging exploration tasks.
Paper proposes a hybrid AI method to optimize pandemic actions.
problem Optimizing government actions to balance public health and economy.
method Combines Deep Q-Learning and Genetic Algorithms for optimal sequences of actions.
result Deep Q-Learning outperforms Genetic Algorithms in optimizing action sequences.