New method improves neural network robustness without adversarial training.
problem Adversarial robustness of neural networks under flat loss surface.
method Visualizing decision surfaces in input space to assess robustness.
result Decision surface geometry in input space correlates with adversarial robustness.
Measures neural network decision boundary volume to predict model performance.
problem Understanding the geometry of deep learning models for better performance.
method Local surface volumes to measure decision boundary, applying Weyl's tube formula.
result Smaller surface volume correlates with higher classification accuracy.
Survey on 3-manifold problems and their complexity.
problem Decision problems in 3-manifold theory.
method Tools like normal surfaces, hierarchies, homomorphisms, and hyperbolic structures.
result Discussion of computational complexity of 3-manifold problems.
SVR-Tree improves classification trees for imbalanced and sparse data.
problem Classification difficulties in imbalanced and sparse data.
method Proposes SVR-Tree, penalizing the Surface-to-Volume Ratio of decision sets.
result SVR-Tree improves generalization error compared to other imbalance algorithms.
Decision-alignment evaluates uncertainty quantification for decision-relevant UQ
problem Evaluation of uncertainty quantification metrics
method Introduce decision-alignment
result Proper scoring rules align with decision utility
This review explores causal decision-making to improve decision quality.
problem Effective decision-making requires understanding causal relationships.
method Causal structure learning, causal effect learning, and causal policy learning.
result Challenges in causal decision-making are identified and recent advances are discussed.
Researchers compute exact decision boundaries of neural networks.
problem Finding exact decision boundaries of trained neural networks is intractable.
method Developed mathematical tools to investigate decision boundaries of trained deep models.
result Some simplifying assumptions about decision boundaries are unreliable.
Capacity control, the bias/variance dilemma, and learning unknown functions from data, are all concerned with identifying effective and consistent fits of unknown geometric loci to random data points. A geometric locus is a curve or surface formed by points, all of which possess some uniform property. A geometric locus…
In low-dimensional topology, many important decision algorithms are based on normal surface enumeration, which is a form of vertex enumeration over a high-dimensional and highly degenerate polytope. Because this enumeration is subject to extra combinatorial constraints, the only practical algorithms to date have been v…
Enhanced hedging for S&P 500 options using volatility surface data.
problem Optimizing hedging strategies for S&P 500 options with transaction costs.
method Deep policy gradient reinforcement learning with volatility surface feedback.
result Outperforms conventional hedging methods in simulations and backtesting.
Proposes deep hedging for index options using implied volatility surface.
problem Managing risk in index option portfolios with complex dynamics.
method Integrates surface-informed decisions with multiple hedging instruments, accounting for transaction costs and variance risk premium.
result Consistently outperforms traditional hedging strategies across various market conditions.
New method reduces curvature for improved adversarial robustness.
problem Improving the robustness of classifiers against adversarial attacks.
method Proposes a new curvature regularizer to minimize the curvature of the loss surface.
result Adversarial robustness is on par with adversarial training when curvature is minimized.
Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used base classifiers in an ensemble framework. In this paper, we first present a new variant of oblique decision tree based on a linear classifier,…
In this paper, we use normal surface theory to study Dehn filling on a knot-manifold. First, it is shown that there is a finite computable set of slopes on the boundary of a knot-manifold that bound normal and almost normal surfaces in a one-vertex triangulation of that knot-manifold. This is combined with existence th…
The paper proposes a method to evaluate superhuman models by checking for logical inconsistencies.
problem Evaluating superhuman models when ground truth is hard to obtain.
method A framework using consistency checks to identify logical inconsistencies in model decisions.
result Logical inconsistencies can be discovered in superhuman model decisions across various tasks.
Quantum mechanics models human perception and decision-making, offering a new approach to understanding social dynamics.
problem Understanding the complex interactions between individuals and groups in social networks.
method Developed a simple computational code based on quantum mechanics principles to model human perception and decision-making.
result Quantum-inspired models can help explain differences in individual and group behavior.
We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives…
T-SVM improves learning in spiking neurons by maximizing dynamical margin.
problem Finding robust solutions in spiking neuronal networks with temporal correlations.
method Introduces Temporal Support Vector Machine (T-SVM) to maximize dynamical margin.
result T-SVM enables learning of tasks requiring nonlinear spatial integration.
New method optimizes 3D training data generation for deep networks.
problem Challenges in generating realistic 3D training data for deep networks.
method Hybrid gradient optimization of design decisions in graphics-based generation pipelines.
result Our approach outperforms prior methods in computational efficiency and performance.
The paper tackles AI advice giving by considering adherence levels and defer options.
problem Inadequate consideration of human adherence to AI recommendations.
method Sequential decision-making model that considers adherence levels and incorporates a defer option.
result Specialized learning algorithms provide better convergence and empirical performance.
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of these 3D-CNNs is still an infeasible task. In this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D…
Proposes a new way to represent and analyze Pareto front surfaces.
problem Identifying and analyzing Pareto front surfaces in multi-objective optimization.
method Parameterizes Pareto front surfaces using polar coordinates and scalar-valued length functions.
result Derives statistics of Pareto front surfaces and develops visualisation techniques.
Deep learning framework predicts surface texture parameters and their uncertainties.
problem Predicting surface texture parameters and their uncertainties from multi-instrument datasets.
method Reproducible deep learning framework using multi-instrument dataset, quantile and heteroscedastic heads for uncertainty modeling, and post-hoc conformal calibration.
result High fidelity predictions (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918) and well-modelled uncertainty targets (Ra_uncert 0.9899, Rz_uncert 0.9955).
Recognition of Seifert fibered spaces with boundary is computationally tractable.
problem Recognizing Seifert fibered spaces with boundary.
method Proving the existence of fundamental horizontal surfaces and normal vertical annuli with bounded total weight.
result The decision problem is in NP and the construction problem is in FNP.
In this paper, we explore and detail our experiments in a high-dimensionality, multi-class image classification problem often found in the automatic recognition of Sign Languages. Here, our efforts are directed towards comparing the characteristics, advantages and drawbacks of creating and training Support Vector Machi…
A method to control neural level sets for improved generalization and robustness.
problem Improving the properties of neural networks, particularly their decision boundaries and robustness.
method Sampling neural level sets and relating them to network parameters through a sample network.
result High fidelity surface reconstruction from raw 3D point clouds and comparable robust accuracy to state-of-the-art methods.
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to captu…
Two fundamental objects in knot theory are the minimal genus surface and the least area surface bounded by a knot in a 3-dimensional manifold. When the knot is embedded in a general 3-manifold, the problems of finding these surfaces were shown to be NP-complete and NP-hard respectively. However, there is evidence that …
Study characterizes bladder motion using dynamic MRI and statistical analysis.
problem Limited volume coverage in dynamic MRI sequences hinders 3D shape reconstruction.
method 3D dense velocity measurements, LDDMM framework, statistical characterization, mean curvature changes, surface deformation analysis.
result Stable shape descriptor for characterizing bladder surface dynamics.
We create consistent option surfaces without arbitrage.
problem Constructing consistent option surfaces free of arbitrage across different maturities.
method Combining PCA-Smolyak approximation with chain-consistent diffusion and c-EMOT bridge.
result Computable certificates for strong convexity, solver correctness, and Dupire/Greeks stability.
Paper tackles catastrophic overfitting in single-step adversarial training.
problem Catastrophic overfitting leads to sudden drop in robust accuracy.
method Proposes a method to prevent overfitting by using all adversarial examples.
result Demonstrates prevention of catastrophic overfitting and improves robustness.
The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid K-means and regression classification methodology, with the intent of detecting anomalies. Conditions are given …
An algorithm is proposed that solves two decision problems for pseudo-Anosov elements in the mapping class group of a surface with at least one marked fixed point. The first problem is the root problem: decide if the element is a power and in this case compute the roots. The second problem is the symmetry problem: deci…
New framework for predicting decisions that influence their own outcomes.
problem Predictions that affect the outcomes they predict, leading to undesirable distribution shift.
method Risk minimization framework combining statistics, game theory, and causality.
result Necessary and sufficient conditions for retraining to converge to a performatively stable point of minimal loss.
New methods improve accuracy in predicting complex systems.
problem Improving accuracy in predicting complex physical systems from simulators.
method Proposes two new methods of design approaches that sequentially select input settings.
result Demonstrates effectiveness of the proposed methods through numerical examples.
Bayesian framework optimizes 3D view selection for specific tasks.
problem Optimizing 3D view selection for specific tasks in reconstruction.
method Bayesian decision theory, prior and posterior distributions, stochastic surface reconstruction.
result Framework achieves superior performance with fewer views.
Paper uses Gaussian processes and neural nets to model sub-km wind accurately.
problem Accurately modeling sub-kilometer surface wind for optimal decision-making.
method Integrates Gaussian processes and neural networks to model wind gusts at sub-kilometer resolution.
result Modeling covariance structure improves prediction quality and calibration.
In machine learning (ML) security, attacks like evasion, model stealing or membership inference are generally studied in individually. Previous work has also shown a relationship between some attacks and decision function curvature of the targeted model. Consequently, we study an ML model allowing direct control over t…
Decision Machines embeds decision trees into vector spaces for improved optimization.
problem Overfitting and difficulty in finding optimal decision tree structure.
method Embedding Boolean tests into a binary vector space and representing tree structure as matrices.
result Optimized decision trees with enhanced predictive power.
We show that three natural decision problems about links and 3-manifolds are computationally hard, assuming some conjectures in complexity theory. The first problem is determining whether a link in the 3-sphere bounds a Seifert surface with Thurston norm at most a given integer; this is shown to be NP-complete. The sec…
Study optimal investment under imitation of decision-changing rates.
problem Optimal investment under imitation of decision-changing rates.
method Proposed integral disparity to quantify imitation, derived general solution using variational method, analyzed asymptotic properties, validated with real data.
result Investor's optimal decisions under imitation of decision-changing rates.
The Chain-of-Decision approach improves forecasting of financial professionals' trading decisions.
problem Challenges in forecasting professionals' behaviors, especially in trading decisions.
method Integrates an opinion-generator-in-the-loop to provide subjective analysis based on news items.
result Promising improvements in the proposed tasks' performance.
dtControl uses decision trees to represent controllers efficiently and explainably.
problem Representing controllers concisely and explainably.
method dtControl uses decision tree learning algorithms to represent controllers. Novel techniques for determinizing controllers are introduced.
result Novel techniques for determinizing controllers during decision tree construction are extremely efficient, yielding small decision trees.
Paper develops streaming algorithms to estimate classifier accuracy on unlabeled data.
problem Estimating classifier accuracy on unlabeled data with noisy decisions.
method Two algebraic evaluators: majority voting and a novel method to handle correlated classifiers.
result The novel method can be as accurate as 1% when handling small amounts of correlation.
Single neurons can perform as well as dense networks in binary and multi-class recognition tasks.
problem Designing efficient neural networks for recognition tasks.
method Investigated the use of single or multiple neurons in neural networks for binary and multi-class recognition tasks.
result Sparse networks can be as efficient as dense networks in both binary and multi-class tasks.
New research shows machine-assisted decisions can still be unfair even when the algorithm is fair.
problem Ensuring fairness in decisions made with machine-assisted human input.
method Formal model and lab experiment to analyze how machine predictions affect human decisions.
result Excluding information about protected groups from machine predictions can increase disparities.
Non-spanning identification of scheduled event risk in option pricing.
problem Separating continuous surface from scheduled jump in option pricing.
method Modeling FOMC decisions, CPI releases, and NFP reports as deterministic-time jumps in risk-neutral option pricing.
result Improves held-out event-spanning pricing with Gaussian and two-component mixture jumps.
The paper presents a machine learning framework to combine weather forecasts from multiple models.
problem Combining forecasts from different NWP models with varying biases and limitations.
method Three-stage framework using Quantile Regression Forests and quantile averaging.
result The framework generates well-calibrated probabilistic weather forecasts suitable for decision support.