Accelerates machine learning algorithms for sparse data.
problem Efficiently solving composite convex minimization problems.
method Accelerated dual-averaging primal-dual method for composite convex minimization.
result Demonstrates advantages in handling sparse data both theoretically and empirically.
This work develops machine learning for micromagnetic energy minimization.
problem Minimizing Gibbs free energy in full 3D micromagnetic simulations.
method Advanced machine learning techniques, including Physics-Informed Neural Networks (PINNs) and Extreme Learning Machines (ELMs), with reformulated bounds and optimization schemes.
result Competitive performance of machine learning methods compared to traditional numerical approaches.
This study investigates porosity and topological properties of TPMS using machine learning.
problem Understanding the relationships between porosity and topological properties of TPMS.
method Application of machine learning techniques to analyze porosity and shape factor of TPMS.
result Conjectures suggesting polynomial relationships between porosity and shape factor of TPMS.
Machine learning factors outperform traditional portfolio optimization methods.
problem Comparing machine learning and traditional portfolio optimization methods.
method Examined machine learning and factor-based portfolio optimization using autoencoder neural networks and dimensionality reduction techniques.
result Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in risk minimization.
Machine learning helps predict smoke types for safer forest burns.
problem Determining which fuels to burn safely to reduce wildfire risk and minimize smoke.
method Machine learning techniques like spectral clustering and manifold learning.
result Interpretable representations and tools for differentiating smoke types.
A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learning problems such as regression and classification. The stochastic optimization problems arising in these machine learning problems is solved…
New algorithm for safer machine learning with different testing and training distributions.
problem Challenges in modern machine learning where training and testing distributions differ.
method First-order optimization algorithm for superquantile-based learning.
result Promising numerical results show the approach's effectiveness.
The Minimal Learning Machine improves regression performance with reference point selection.
problem Improving the performance of the Minimal Learning Machine (MLM) in regression tasks.
method Developed theoretical guarantees for MLM's interpolation and approximation capabilities. Proposed clustering-based methods for selecting reference points to enhance MLM's generalization.
result Clustering-based methods for reference point selection outperform random selection, especially with a small number of points.
New unsupervised learning technique learns independent kernels for better machine learning tasks.
problem Improving unsupervised representation learning for machine learning tasks.
method Stacking convolutional transforms using alternating proximal minimization scheme.
result DCTL outperforms shallow version CTL on benchmark datasets.
DeltaGrad rapidly retrain models with minimal data changes.
problem Rapid retraining of machine learning models with minimal data changes.
method DeltaGrad algorithm based on cached training information.
result DeltaGrad compares favorably to state-of-the-art methods.
Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first …
A new method detects changes in machine learning models over time.
problem Automatic monitoring of machine learning models trained on evolving data.
method Score-based statistical hypothesis test for change detection.
result The method can detect changes in any number of model components.
Federated learning collaborates clients to train models without sharing data.
problem Privacy and data sharing in machine learning.
method Central server orchestrates collaborative training of models on decentralized data.
result Recent advances and open problems in FL.
MetaPhysiCa tackles robust physics-informed machine learning for OOD tasks.
problem Designing robust PIML methods for OOD forecasting tasks in physics.
method Meta-learning procedure for causal structure discovery including invariant risk minimization.
result Significantly outperforms existing PIML and deep learning methods in OOD tasks.
New framework improves fairness in machine learning models.
problem Eliminating discrimination in machine learning models for sensitive fields.
method Reformulate data input by removing sensitive features and design adversarial network to minimize dependence.
result Our model achieves better fairness metrics and prediction performance than state-of-the-art methods.
Currently, machine learning plays an important role in the lives and individual activities of numerous people. Accordingly, it has become necessary to design machine learning algorithms to ensure that discrimination, biased views, or unfair treatment do not result from decision making or predictions made via machine le…
Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.
problem Quantum machine learning's loss minimization through cross entropy is affected by measurement outcomes.
method Defined quantum cross entropy, proved its lower bounds, and investigated its relation to quantum fidelity and likelihood.
result Quantum cross entropy is lower-bounded by negative log-likelihood when derived from quantum data, but measurement outcomes can cause loss.
Develops a measure for subjective explainability of ML predictions.
problem Ensuring transparency and trust in automated decision-making.
method Information-theoretic concepts applied to conditional entropy of predictions given user feedback.
result EERM principle balances subjective explainability and risk.
The paper argues that machine learning is a falsificationist process.
problem The role of falsification in machine learning is underexplored.
method The paper presents a falsificationist account of artificial neural networks, emphasizing empirical risk minimization and implicit regularization.
result Artificial neural networks can be seen as a falsificationist process, rejecting inadequate prediction rules.
METRO predicts reactions using minimal templates, reducing computational overhead and achieving state-of-the-art results.
problem Predicting possible reaction substrates for complex molecules from simpler precursors.
method METRO (Molecule-Edit Templates for RetrOsynthesis) uses minimal templates to predict reactions efficiently and accurately.
result METRO achieves state-of-the-art results on standard benchmarks, reducing computational overhead.
New model tackles real-world distribution mismatches in machine learning.
problem Real-world applications often have training and test distributions that differ.
method Developed a learning model based on information theory using importance sampling.
result The model performs better under large distribution deviations.
A heuristic minimizes tardy jobs' total weight on single-machine scheduling.
problem Minimizing tardy jobs' total weight on single-machine scheduling.
method Data-driven heuristic combining machine learning and problem-specific characteristics.
result Significantly outperforms state-of-the-art in optimality gap and adaptability.
Machine learning predicts minimal surfaces for knots, supporting a conjecture.
problem Predicting minimal surfaces for knots in hyperbolic space.
method Physics-Informed Neural Networks (PINNs) to solve minimal surface equation.
result Computational minimal surfaces align with Fine's Conjecture.
SONIA optimizes machine learning problems with a novel algorithm.
problem Empirical risk minimization in machine learning.
method Symmetric Blockwise Truncated Optimization (SONIA) algorithm combining second-order and steepest descent steps.
result SONIA converges to stationary points in both convex and nonconvex cases.
Revisits orbital minimization for neural operator decomposition.
problem Training neural networks to approximate eigenfunctions of operators.
method Adapts orbital minimization method (OMM) for neural networks.
result Justifies broader applicability of OMM in modern learning pipelines.
In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions. This framework has been learned by us, validated and improved over a period of more than two years by participating in online machine learning competitions. It aims at minimizing human interf…
New machine learning paradigm ignores loss function until action time.
problem Learning with unknown loss functions.
method Introduces omnipredictors for any loss function.
result Extracts predictive power from any class, ignoring loss function.
Machine learning models predict the behavior of negatively buoyant jets from wastewater.
problem Minimizing harmful effects of negatively buoyant jets during wastewater discharge.
method Training machine learning models (ANN, XGBoost, CatBoost, LightGBM) on OpenFOAM simulations and experimental data.
result Artificial Neural Network provided the best prediction with R2 0.98 and RMSE 0.28.
Majorization-minimization algorithms consist of successively minimizing a sequence of upper bounds of the objective function. These upper bounds are tight at the current estimate, and each iteration monotonically drives the objective function downhill. Such a simple principle is widely applicable and has been very popu…
New approach to learning kernels from data using AIT principles.
problem Learning kernels from data in machine learning.
method Sparse Kernel Flows method based on AIT principles.
result Sparse Kernel Flows aligns with MDL principle and offers a robust theoretical foundation.
New algorithms improve submodular minimization via DC programming.
problem Minimizing the difference of two submodular functions.
method Introducing variants of the DC algorithm (DCA) and its complete form (CDCA) for DC programs corresponding to DS minimization.
result Our algorithms outperform existing baselines on speech corpus selection and feature selection.
Develops a direct debiased machine learning framework using Bregman divergence.
problem Reduces bias in machine learning estimates of causal effects or structural models.
method Neyman targeted estimation and generalized Riesz regression using Bregman divergence.
result Improves estimation of parameters of interest in causal models.
We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budg…
We consider a variant of the classic Ski Rental online algorithm with applications to machine learning. In our variant, we allow the skier access to a black-box machine-learning algorithm that provides an estimate of the probability that there will be at most a threshold number of ski-days. We derive a class of optimal…
Study shows convergence rate for empirical minimizer of unbounded functions with fast growth.
problem Convergence rate of empirical minimizer for unbounded functions with fast growth.
method Analyzes L1-distance convergence rate of the empiric minimizer for coercive functions sampled with noise. result Convergence rate is bounded above by ann−1/q, where q is the dimension and an=o(nε) for every ε>0. A new machine learning method solves high-dimensional Kolmogorov PDEs efficiently.
problem Solving high-dimensional Kolmogorov PDEs and SDEs.
method Stochastic weighted minimization and stochastic gradient descent with Malliavin weights.
result Accurate approximation of high-dimensional Kolmogorov PDEs and SDEs without curse of dimensionality.
MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relativ…
Regularized empirical risk minimization including support vector machines plays an important role in machine learning theory. In this paper regularized pairwise learning (RPL) methods based on kernels will be investigated. One example is regularized minimization of the error entropy loss which has recently attracted qu…
This paper develops methods for obtaining distribution-free prediction regions for invariant representations.
problem Distributional shifts in machine learning models.
method Invariant risk minimization and weighted conformity scores.
result Proves the effectiveness of adaptive conformal intervals for uncertainty estimation.
Unified framework for decentralized optimization combining gradient tracking and variance reduction.
problem Solving finite-sum minimization problems in distributed systems with privacy and resource constraints.
method Unified algorithmic framework combining variance-reduction and gradient tracking.
result Unified methods achieve robust performance and fast convergence for smooth and strongly-convex objectives, and are applicable to non-convex problems.
MRCpy implements minimax risk classifiers with performance guarantees and distribution shift adaptability.
problem Classical risk minimization approaches are not robust to distribution shifts.
method Robust risk minimization approach for minimax risk classifiers.
result MRCs provide performance guarantees and adapt to distribution shifts.
We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit.…
We propose an inference method to estimate sparse interactions and biases according to Boltzmann machine learning. The basis of this method is L1 regularization, which is often used in compressed sensing, a technique for reconstructing sparse input signals from undersampled outputs. L1 regularization impedes the …
New method uses minimal assumptions for machine learning, improving performance and speed.
problem Current machine learning methods require specific model assumptions that are not derived from prior knowledge.
method Assumes scale invariance principles and differentiability of the true function to derive a novel stochastic process.
result The method achieves equal performance to Gaussian process regression but is less arbitrary, faster, and has better extrapolation.
MM (majorization--minimization) algorithms are an increasingly popular tool for solving optimization problems in machine learning and statistical estimation. This article introduces the MM algorithm framework in general and via three popular example applications: Gaussian mixture regressions, multinomial logistic regre…
Paper shows how gradient concentration helps in learning from inexact data.
problem Learning from inexact and stochastic training data.
method Combines probabilistic gradient concentration with inexact optimization techniques.
result Derives sharp test error guarantees for learning.
New learning algorithm for real analytic functions without gradient descent.
problem Learning real analytic functions without gradient descent.
method Taylor approximation and sampling data distribution.
result Nonuniform learning result for real analytic functions.
The paper develops fair machine learning models using causal path-specific effects.
problem Fairness in machine learning models under causal constraints.
method Lagrange multiplier approach for infinite-dimensional functional estimation, closed-form solutions for constrained optimization.
result Theoretical and flexible semiparametric estimation strategies for fair predictions.