Study on pricing options tied to stock tick complexity.
problem Pricing options based on stock tick complexity.
method Numerical and theoretical analysis of European and American options.
result Numerical and theoretical pricing results for different complexity types.
Formula for complex SVD backpropagation developed.
problem No specific problem stated; focuses on complex SVD.
method Back propagation formula for complex SVD developed.
result Back propagation formula for complex SVD created.
Automatic computation speeds up crosscap number calculation for alternating knots.
problem Computing crosscap numbers for alternating knots efficiently.
method Introduced an automatic computation with complexity O(E3). result Crosscap numbers of alternating knots can be computed in O(E3) time. agtboost speeds up gradient tree boosting with automatic complexity adjustment.
problem Speeding up and simplifying gradient tree boosting computations.
method Adaptive gradient tree boosting with automatic complexity adjustment and feature importance.
result Significant decrease in computation time and simplification of model complexity.
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
problem Manual decision-making for recovery commands is time-consuming and error-prone.
method Seq2Seq neural network model trained on past logs and commands.
result The model can estimate accurate recovery commands from new failures.
We discuss a problem posed by Gersten: Is every automatic group which does not contain Z+Z subgroup, hyperbolic? To study this question, we define the notion of "n-tracks of length n", which is a structure like Z+Z, and prove its existence in the non-hyperbolic automatic groups with mild conditions. As an application, …
Bayesian TNKMs automatically infer model complexity and feature relevance.
problem Manual tuning of TN rank and feature dimensions is error-prone and computationally expensive.
method Bayesian approach with hierarchical priors on TN factors for automatic rank and feature selection.
result Superior performance in prediction accuracy, uncertainty quantification, interpretability, and scalability.
mNARX+ creates accurate surrogate models for complex systems without requiring domain expertise.
problem Creating accurate surrogate models for complex dynamical systems without extensive domain knowledge.
method Data-driven, recursive algorithm that automatically selects temporal features and their causal ordering.
result Automatically identifies critical auxiliary quantities and their order for accurate modeling.
This paper introduces a spline-based method for nonparametric ADVI that handles complex posterior distributions.
problem Learning complex posterior distributions with skewness, multimodality, and bounded support.
method Develops a spline-based nonparametric approximation approach for ADVI.
result Establishes the asymptotic consistency of the derived lower bound for importance weighted autoencoder.
AutoKE automates embedding physical knowledge into neural networks for complex engineering problems.
problem Complex physical equations in engineering problems.
method AutoKE framework using deep neural networks, equation parsing, automatic differentiation, adaptive weights, and NAS.
result Automatically embeds physical knowledge into neural networks for complex equations efficiently.
Framework for automatically designing and training deep models.
problem Manual design of deep architectures is slow and error-prone.
method Extensible language for representing complex search spaces over architectures and hyperparameters. Uses tree-structured search spaces and various algorithms like MCTS and SMBO.
result MCTS and SMBO outperform random search in model training.
We use AD to compute gradients for complex functionals in stochastic model calibration.
problem Computing gradients for functions involving expectations in stochastic models.
method Automatic Adjoint Differentiation and parallelization.
result Faster and easier to implement approaches for gradient computation.
The study proves conditions for Hermitian metrics on compact almost complex manifolds.
problem Conditions for Hermitian metrics on compact almost complex manifolds.
method Analyzes compact almost complex manifolds with Hermitian metrics and integral conditions involving ∂-harmonic (0,1)-forms. result The integral condition is automatically satisfied for strongly Gauduchon metrics, and equivalent to being strongly Gauduchon for integrable almost complex structures.
ASVI automates variational inference for complex models.
problem Efficient variational inference for complex probabilistic models.
method Automatic structured variational inference (ASVI) using convex updates.
result ASVI outperforms other methods on a wide range of problems.
A new method, InfoGuide, improves automatic clustering analysis.
problem Lack of automatic clustering analysis frameworks.
method Capturing traces of information gain between clustering retrievals.
result InfoGuide can enable more automatic clustering analysis.
Proposes an automatic cyclical scheduling for gradient-based discrete sampling.
problem Gradient-based sampling in high-dimensional models can get stuck in local modes.
method Cyclical step size and balancing schedules with automatic hyperparameter tuning.
result Proves non-asymptotic convergence and inference guarantees for general discrete distributions.
Improves sampling from complex hierarchical models using HMC and automatic marginalization.
problem Sampling from complex hierarchical models is difficult for HMC.
method Proposes automatic marginalization as part of the sampling process using HMC in a graphical model extracted from a PPL.
result Significantly improves sampling from real-world hierarchical models.
Super-efficient automatic differentiation outperforms analytic methods in min-min optimization.
problem Optimizing functions defined as a minimum using iterative algorithms.
method Comparing automatic differentiation to analytic gradient estimation methods.
result Automatic differentiation yields an asymptotic error close to the square of the optimization error, demonstrating super-efficiency.
BASS efficiently learns time-varying graphs with low complexity and automatic tuning.
problem Estimating time-varying graphical models with efficient and automatic parameter tuning.
method BASS uses temporally-dependent spike-and-slab priors and variational inference to learn graph structures efficiently.
result BASS outperforms existing methods in recovering true graphs, especially for high-dimensional cases.
ADVI simplifies model fitting for probabilistic models.
problem Fitting complex models to large datasets is computationally challenging.
method Automatic differentiation variational inference (ADVI).
result ADVI automatically derives efficient variational inference algorithms.
Storchastic improves stochastic AD for complex models in RL and VI.
problem Handling intractable expectations in RL and VI.
method Introduces Storchastic, a framework for AD of stochastic computation graphs with various gradient estimation methods.
result Provable unbiasedness and variance reduction for higher-order gradients.
ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation
problem Generative models for counterfactual outcomes
method ADIGen combines Riesz regression, causal invariance, and orthogonal statistical learning
result ADIGen controls counterfactual risk under general interventions
Tangent automates derivatives in Python, improving expressiveness and performance.
problem Efficiently calculating derivatives for complex models in Python.
method Source-code transformation for dynamically typed array programming.
result Demonstrates improved expressiveness and performance in automatic differentiation.
New method improves hyperparameter tuning efficiency across similar tasks.
problem Mismatch between evaluations in current and previous tasks.
method Nested drop-out and auto-relevance determination for learning basis functions of increasing complexity.
result Improves sample efficiency in hyperparameter tuning across different data regimes.
Automatically generates curricula for reinforcement learning agents.
problem Learning in dynamic, sparse reward environments.
method Setter-solver paradigm focusing on goal validity, feasibility, and coverage.
result Demonstrated success in 2D and 3D environments with varying goals.
Novel approach trains ASR models with less supervision using bilevel optimization.
problem Training acoustic models for ASR with minimal supervision.
method Bilevel optimization with unsupervised and supervised losses.
result Achieves superior performance compared to existing methods.
Automatically updates both network weights and architecture.
problem Manual selection of network architecture limits flexibility and efficiency.
method Continuous parameterization of network depth and automatic adjustment of architecture and weights.
result Correctly adjusts network complexity to task complexity.
The paper makes inference methods available for Gaussian models with banded precision.
problem Efficient inference for Gaussian models with banded precision.
method Develops linear algebra operators for banded matrices within automatic differentiation frameworks.
result The operators enable efficient variational inference and gradient-based sampling for Gaussian models with banded precision.
AutoGMM automates Gaussian mixture modeling in Python.
problem Automatic clustering of complex data with uncertainty-aware grouping.
method Strategic initialization using an agglomerative Mahalanobis heuristic, parallelized model selection by information criteria.
result Strong out-of-the-box performance on classic benchmarks and real datasets.
A new method for automatic gradient tree boosting using information theory.
problem Automatic selection of tree complexity and number in gradient boosting.
method Optimism of greedy leaf splitting procedure modeled as a Cox-Ingersoll-Ross process, leading to an information criterion for model selection.
result The method achieves significant speedups (10-1400) compared to xgboost without sacrificing predictive power.
Automatically learns flexible symmetry constraints in neural networks using gradients.
problem Fixed hard constraints on neural network functions that cannot be adapted.
method Improves parameterisations of soft equivariance and optimizes marginal likelihood using differentiable Laplace approximations.
result Achieves equivalent or improved performance on image classification tasks compared to baselines with hard-coded symmetry.
Cascading flows improve variational inference in structured programs.
problem Challenges in variational inference for complex probabilistic programs.
method Integrates normalizing flows and ASVI to create cascading flows, which embed the forward-pass of probabilistic programs.
result Cascading flows outperform normalizing flows and ASVI in structured inference problems.
CoLA automates efficient numerical linear algebra for complex matrix structures.
problem Efficiently solving large-scale linear algebra problems with complex matrix structures.
method Combining linear operator abstraction with compositional dispatch rules.
result Automatic and efficient numerical algorithms for various linear algebra operations.
We construct a simply connected compact manifold which has complex and symplectic structures but does not admit Kähler metrics, in the lowest possible dimension where this can happen, that is, dimension 6. Such a manifold is automatically formal and has even odd-degree Betti numbers but it does not satisfy the Lefschet…
This paper automates PID controller tuning using model-based policy search.
problem Tuning multivariate PID controllers is tedious in industrial applications.
method Extends PILCO to automatically tune PID controllers using model-based policy search.
result Demonstrates fast and data-efficient policy learning on complex real-world problems.
Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to mode…
Robot solves Rubik's cube using simulation-trained models.
problem Solving Rubik's cube with a robot hand.
method Automatic domain randomization (ADR) and a custom robot platform.
result Control policies and vision state estimators trained with ADR exhibit improved sim2real transfer.
GP-CNAS uses genetic programming to automatically design CNN architectures.
problem Designing optimal CNN architectures is laborious and error-prone.
method GP-CNAS uses a tree-based representation of CNNs and dynamic crossover operators to search for optimal architectures.
result GP-CNAS finds optimal CNN architectures with balanced depth and width in limited trials.
Paper develops an AI system to improve power system control.
problem Insufficient effectiveness of existing power system control.
method Combines deep learning and game theory for power system control.
result Improves power system control to normal steady-state or post-emergency conditions.
Framework for inverting complex software simulators using probabilistic programming.
problem Inverting complex software simulators is difficult and requires custom algorithms.
method Formulation of inversion as approximate inference in a simple probabilistic model; four inference strategies implemented.
result Framework implemented in under 20 lines of probabilistic code.
Automatically constructs models from time series data in seconds.
problem Creating models of complex systems from small time series data.
method Automated construction of dynamic prime models from experimental data.
result Models can be constructed in less than a minute.
New method extracts radio signal features for automatic modulation classification.
problem Challenges in automatic modulation classification without expert-defined features.
method Biologically-inspired regularized stacked sparse denoising autoencoders (SSDAs).
result Correct classification rates > 99% at 7.5 dB SNR and > 92% at 0 dB SNR.
Reduced computational complexity for indoor positioning.
problem Efficiently positioning users in large areas with limited data.
method Segmentation, Jaccard index, LASSO feature selection, Bayesian MAP estimation.
result Positioning accuracy with a subset of features and sub-regions is equivalent to full data, but computation time is reduced.
This paper presents an automatic approach for selecting optimal meta-models for sensitivity analysis in complex systems.
problem Efficient surrogate models for high-dimensional problems in virtual prototyping.
method Automatic selection of meta-models, variable space reduction, and advanced sensitivity measures.
result Optimal meta-models and subspace identification for accurate probabilistic analysis.
Automates neural network model selection for efficiency.
problem Efficiently choosing neural network architectures and hyper-parameters.
method Modified micro-genetic algorithm for automated model selection.
result AMS finds efficient neural network models for classification and regression.
New method speeds up Bayesian optimization in high dimensions.
problem High-dimensional expensive function optimization struggles.
method Structured automatic differentiation for kernel matrices.
result First-order Bayesian optimization scalable to high dimensions.
INO learns physical models with momentum conservation laws.
problem Learning physical models without preserving fundamental laws.
method Designing an invariant neural operator that automatically satisfies momentum conservation laws.
result The model learns complex material behaviors and achieves state-of-the-art accuracy and efficiency.
New methods solve complex optimization problems without smoothness.
problem Solving fully nonsmooth constrained convex optimization problems.
method Two alternating direction methods with automatic parameter updates.
result Best iteration-complexity guarantees for objective residual and feasibility gap.