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.
New method reduces neural image compression run-time by 50%.
problem Computational efficiency of neural image compression models.
method Automatic network optimization to reduce decoder complexity.
result Decreased decoder run-time by over 50%.
New algorithms improve agnostic learning for triangles and polygons, reducing time complexity.
problem Efficient agnostic learning for geometric concept classes.
method Data structures and algorithms from computational geometry, probabilistic combinatorics.
result Optimal time complexity improvements for agnostic learning of triangles and polygons.
Paper tackles running physics model training, improving reinforcement learning efficiency.
problem Training a physiologically-based human model to navigate complex obstacle courses efficiently.
method Physics-based reinforcement learning, using Deep Deterministic Policy Gradient, with improvements like layer normalization and parameter noise.
result Deep Deterministic Policy Gradient proved most efficient, and improvements helped stabilize training.
The paper predicts run times for Gaussian chemistry code.
problem Accurate run time prediction for complex scientific codes.
method Characterized data set, explored regression methods.
result Promising future directions for run time prediction.
Efficient algorithms improve learning of large-margin halfspaces.
problem Learning large-margin halfspaces efficiently and reproducibly.
method Design of efficient, dimension-independent, polynomial-time algorithms; SGD-based approach; DP-to-Replicability reduction.
result Improved sample complexity compared to previous algorithms, with optimal sample complexity for one algorithm.
SCRiBLe optimizes online bandit linear optimization with a polynomial run time.
problem Efficiently solving online bandit linear optimization problems.
method SCRiBLe setup and algorithm with O ( T ) O(\sqrt{T}) O ( T ) regret bound and polynomial run time complexity. result Achieves O ( T ) O(\sqrt{T}) O ( T ) regret bound and polynomial run time complexity. Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag behind. This motivates the need for algorithms that are efficient, both in terms of the number of measurements needed and running time. To comb…
Algorithm classifies surface homeomorphisms with polynomial time complexity.
problem Classifying surface homeomorphisms with polynomial time complexity.
method Algorithm to compute curve distances and decide Nielsen-Thurston types.
result Polynomial time classification of surface homeomorphisms.
New framework reduces private mean estimation error with optimal efficiency.
problem Locally private mean estimation of high-dimensional vectors.
method ProjUnit framework: random projections, normalization, and optimal algorithm execution in lower dimensions.
result Optimal error up to a 1+o(1)-factor with computational efficiency and low communication complexity.
Paper presents efficient algorithms for robust PCA with reduced computational complexity.
problem Robust PCA in fully and partially observed settings, especially when corruptions are present.
method Non-convex optimization approach using gradient descent.
result Significant reduction in computational complexity compared to existing algorithms.
Quantum version of C5.0 algorithm improves decision tree construction time.
problem Improving the efficiency of decision tree construction in machine learning.
method Improved classical algorithm and applied quantum subroutines for faster decision tree construction.
result Quantum algorithm reduces decision tree construction time significantly.
New insights into training ReLU networks, especially as data dimensionality increases.
problem Understanding computational complexity of training ReLU networks with varying data dimensions.
method Analyzed the parameterized complexity of two-layer ReLU networks with respect to various loss functions, focusing on the influence of data dimensionality.
result Running time lower bounds and optimal brute-force strategies for training ReLU networks, extending previous results to broader loss functions.
We give the first algorithm for Matrix Completion whose running time and sample complexity is polynomial in the rank of the unknown target matrix, linear in the dimension of the matrix, and logarithmic in the condition number of the matrix. To the best of our knowledge, all previous algorithms either incurred a quadrat…
Randomly chosen support makes sparse linear regression easy.
problem Sparse linear regression with random support.
method Random support selection for efficient prediction.
result Prediction error ε ε ε with N = e x t p o l y ( k , log d , 1 / ε ) N = ext{poly}(k, \log d, 1/ε) N = e x t p o l y ( k , log d , 1/ ε ) samples and e x t p o l y ( d , N ) ext{poly}(d,N) e x t p o l y ( d , N ) run-time. Algorithm learns mixtures of rankings from noisy data.
problem Learning an unknown mixture of rankings from noisy samples.
method Algorithm for different noise models, including heat kernel and Mallows model.
result High accuracy learning of mixture to n O ( log k ) n^{O(\log k)} n O ( l o g k ) time. This study examines the interaction between CDS and stock indices, revealing significant short and long-term impacts.
problem Understanding the interaction between Credit Default Swaps (CDS) and national stock indices.
method ARDL technique applied to analyze short and long-run interactions between BIST-100 index and CDS prices over a specific period.
result The study finds that changes in CDS and BIST-100 index prices have significant impacts on each other, with long-term effects being more pronounced.
Robustly estimates multivariate polynomials in noisy data.
problem Estimating multivariate polynomials in noisy data with outliers.
method Generalizes robust multivariate polynomial regression to n-variate setting.
result Achieves optimal sample complexity and approximation error.
Complex Monge-Ampère flows regularize initial conditions on Hermitian manifolds.
problem Regulating complex Monge-Ampère flows on Hermitian manifolds.
method Proving flows can start from arbitrary initial conditions with zero Lelong number, confirming a conjecture, and studying a generalized flow.
result Chern-Ricci flow performs a canonical surgical contraction on Hermitian manifolds.
This paper analyzes the process of long-run co-movements and stock market globalization on the basis of cointegration tests and vector error correction (VEC) models. The cointegration tests used here allow for structural breaks to be explicitly modeled and breakpoints to be computed on a relative-time basis. The data u…
Efficiently learns complex Boolean functions under Gaussian distributions.
problem Learning complex Boolean functions of halfspaces under Gaussian marginals.
method First efficient proper agnostic learning algorithm for arbitrary Boolean functions of K halfspaces.
result Matches the best known improper learning algorithm's run-time dependence on dimension.
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model …
Simpler algorithm learns shallow networks faster.
problem Learning a linear combination of ReLU activations.
method A simpler one-stage algorithm with improved runtime.
result Runs in ( d / ε ) O ( k 2 ) (d/\varepsilon)^{O(k^2)} ( d / ε ) O ( k 2 ) time. This study simplifies ML learning with ConvNet potentials using MCMC, achieving realistic samples without complex hyper-parameters.
problem Training ConvNet potentials for ML learning with realistic samples using MCMC.
method Minimal hyper-parameter framework, noise-initialized MCMC, and correct Langevin noise tuning.
result ConvNet potentials can learn realistic samples with minimal hyper-parameters and correct MCMC tuning.
New algorithm learns halfspaces with adversarial noise efficiently.
problem Learning halfspaces in the presence of adversarial noise.
method Polynomial-time Perceptron-like online active learning algorithm.
result Near-optimal label and sample complexity with isotropic log-concave marginal distribution.
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.
The paper explores a non-convergent MCMC method for EBM learning.
problem Learning energy-based models using traditional methods is challenging.
method The paper uses a non-convergent, non-mixing, and non-persistent short-run MCMC to learn EBM parameters.
result The learned short-run MCMC can generate realistic images and reconstruct/interpolate between images.
Cross-regularization adapts model complexity during training.
problem Manual tuning of model complexity for overfitting prevention.
method Directly adapts regularization parameters through validation gradients during training.
result Organic emergence of architecture-specific regularization during training.
This paper improves SGMs by using a predictor-corrector scheme to converge faster.
problem Theoretical and practical limitations of existing SGMs when T 1 o ∞ T_1 o \infty T 1 o ∞ . method Integrates a predictor-corrector scheme after the forward process to converge in finite time.
result Convergence guarantees for SGMs require only a fixed finite time T 1 T_1 T 1 . Clusters of withdrawals emerge in banks due to latent fragility.
problem Understanding clustered withdrawals in dynamic bank runs.
method Mean-field game framework to model dynamic bank runs with clustered withdrawals.
result Existence of equilibrium and characterization of earliest-run and latest-run equilibria.
This paper presents quantum and classical algorithms for approximate submodular function minimization.
problem Approximate minimization of submodular functions.
method Classical and quantum algorithms for submodular minimization, with a new quantum sampling method.
result Quantum algorithm for approximate submodular minimization with improved time complexity.
CIAG method accelerates optimization for high-dimensional problems.
problem Training classifiers for high-dimensional problems with large datasets.
method CIAG method uses curvature information to accelerate IAG methods.
result Improved linear convergence rate over state-of-the-art IAG methods.
Algorithm constructs JSJ decomposition for hyperbolic groups.
problem Constructing JSJ decompositions for hyperbolic groups.
method Combinatorial and geometric analysis of immersed cycles in CAT(0) square complexes.
result First algorithm with explicit time bound for JSJ decompositions.
New algorithms solve dense linear systems with low-rank structure efficiently.
problem Solving dense linear systems with specific singular value conditions.
method Randomized algorithms using matrix sketching and low-rank update formulas.
result Achieves nearly-linear time complexity for solving such systems.
Two new algorithms improve robust PCA and Schatten packing.
problem Robustly estimating the top eigenvector of corrupted sub-Gaussian data.
method Two iterative filtering and nearly-linear time algorithms.
result First polynomial-time algorithms for non-trivial covariance estimation.
SURF simplifies distribution estimation with simple, robust, and fast algorithms.
problem Efficient and accurate distribution estimation in statistics and machine learning.
method Piecewise polynomial approximation using empirical probability interpolation and divide-and-conquer merging.
result Surpassing state-of-the-art algorithms in efficiency and accuracy, SURF estimates distributions robustly and quickly.
Statistical and machine-learning algorithms are frequently applied to high-dimensional data. In many of these applications data is scarce, and often much more costly than computation time. We provide the first sample-efficient polynomial-time estimator for high-dimensional spherical Gaussian mixtures. For mixtures of a…
Machine learning improves measuring climate adaptation impacts.
problem Measuring adaptation to climate change using weather damage elasticities.
method Debiased machine learning approach in panel data settings.
result Long-run impacts of damaging heat exposure significantly offset short-run impacts.
Improved canary crafting for one-run privacy auditing reduces leakage estimates.
problem Detecting canaries in one-run privacy auditing to estimate leakage effectively.
method Optimizes canaries for detectability and diversity, using a greedy initialization and bilevel optimization.
result Achieves stronger leakage estimates at lower computational cost.
New method optimizes expensive simulations for complex systems.
problem Optimizing complex systems with limited expensive experiments.
method Black-box Optimization via Marginal Means (BOMM) approach.
result BOMM improves optimization performance in high dimensions.
Quantum SVM clustering speeds up big data analysis.
problem Performance degradation of classical SVM clustering on big data.
method Developed a quantum version of SVM clustering using quantum support vector machine and kernels.
result Significant speed-up gain on run-time complexity.
Algorithm calculates L 2 L^2 L 2 -Euler characteristic for complex spaces.
problem Computing the twisted L 2 L^2 L 2 -Euler characteristic for complex spaces. method Uses Oki's matrix expansion algorithm to indirectly evaluate the Dieudonné determinant.
result Truncated algorithm produces good results in various complex spaces.
Study on insurer solvency under economic growth and run-off conditions.
problem Impact of economic growth on insurer ruin probabilities.
method Comprehensive model with varying business volumes, focusing on run-off companies.
result Sharp asymptotic estimates for infinite time ruin probabilities.
This paper uses SDEs to analyze GANs training and long-run behavior.
problem Understanding the training process and long-run behavior of GANs.
method Established SDE approximations for GANs training and analyzed long-run behavior via invariant measures.
result The long-run behavior of GANs training can be studied via the invariant measures of its SDE approximations.
Deep reinforcement learning models improve musculoskeletal running speed.
problem Optimizing a musculoskeletal model to run as fast as possible through an obstacle course.
method Deep reinforcement learning approaches, including DDPG, PPO, and TRPO, with modifications.
result Various modifications to known algorithms improve running performance.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
New framework evaluates MIA without retraining, addressing biases.
problem Evaluate MIA without retraining and under distribution shift.
method Causal inference approach to MIA evaluation.
result Practical estimators for MIA metrics without retraining.
The paper uses diffusion approximations to analyze and optimize online principal component estimation.
problem Optimizing online principal component estimation from streaming data.
method Diffusion approximation tools applied to Oja's iteration for principal component analysis.
result The Oja's iteration for the top eigenvector generates a continuous-state discrete-time Markov chain over the unit sphere.