This paper presents generalized momentum mappings for covariant Hamiltonian field theories. The new momentum mappings arise from a generalization of symplectic geometry to , the bundle of vertically adapted linear frames over the bundle of field configurations . Specifically, the generalized field momentum obs…
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48 results for “Lévy noise”
Classification of the Bounds on the Probability of Ruin for L{é}vy Processes with Light-tailed Jumpsmath.PR
In this note, we study the ultimate ruin probabilities of a real-valued L{é}vy process X with light-tailed negative jumps. It is well-known that, for such L{é}vy processes, the probability of ruin decreases as an exponential function with a rate given by the root of the Laplace exponent, when the initial value goes to …
This article is devoted to the maximisation of HARA utilities of L{é}vy switching process on finite time interval via dual method. We give the description of all f-divergence minimal martingale measures in initially enlarged filtration, the expression of their Radon-Nikodym densities involving Hellinger and Kulback-Lei…
In this paper, we study the ruin problem with investment in a general framework where the business part X is a L{é}vy process and the return on investment R is a semimartingale. We obtain upper bounds on the finite and infinite time ruin probabilities that decrease as a power function when the initial capital increases…
Let be a manifold, be a vector field on , and be a Banach space. For any fixed function and any fixed complex number , we study Hyers-Ulam stability of the global differential equation .
A note on representation of BSDE-based dynamic risk measures and dynamic capital allocationsq-fin.PM
In this paper, we provide a representation theorem for dynamic capital allocation under It{ô}-L{é}vy model. We consider the representation of dynamic risk measures defined under Backward Stochastic Differential Equations (BSDE) with generators that grow quadratic-exponentially in the control variables. Dynamic capital …
The Wiener-Hopf factorization is obtained in closed form for a phase type approximation to the CGMY Lévy process. This allows, for the approximation, exact computation of first passage times to barrier levels via Laplace transform inversion. Calibration of the CGMY model to market option prices defines the risk neutral…
We introduce a class of interest rate models, called the -CIR model, which gives a natural extension of the standard CIR model by adopting the -stable L{é}vy process and preserving the branching property. This model allows to describe in a unified and parsimonious way several recent observations on the sovereign …
We prove that a compact stratied space satises the Riemannian curvature-dimension condition RCD(K, N) if and only if its Ricci tensor is bounded below by K R on the regular set, the cone angle along the stratum of codimension two is smaller than or equal to 2 and its dimension is at most equal to N. This gives…
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarkscs.CL
Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for lead…
Constructs supermartingale couplings with full marginals constraints.
problem Optimal transport for supermartingale couplings with multiple marginals.
method Markovian iteration of one-period optimal supermartingale couplings.
result Explicit construction of supermartingale processes solving optimal transport problem.
The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference…
We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extra…
Modeling financial markets with a novel order flow model.
problem Inconsistent parameter values from long-range memory estimators.
method Tsallis q-exponential distribution for limit order cancellation times.
result Improved accuracy in predicting financial market dynamics.
Measures three types of noise in LLM evaluations.
problem Separating signal from noise in LLM experiments.
method Defined and measured three types of noise: prediction, data, and total noise. Proposed the all-pairs paired method for statistical power.
result Total noise level is characteristic and predictable across all model pairs.
The study investigates noise effects on parameter estimation for Ornstein-Uhlenbeck processes.
problem Impact of noise on parameter fitting for Ornstein-Uhlenbeck processes.
method Proposed algorithms to distinguish between thermal and multiplicative noise.
result Effective methods to estimate parameters even when multiplicative noise dominates.
Noise in SGD affects overparameterized models, favoring sparse solutions.
problem Understanding and mitigating implicit bias in SGD with parameter-dependent noise.
method Theoretical analysis of a quadratically-parameterized model with label noise and Gaussian noise.
result SGD with label noise recovers sparse ground-truth solutions, while SGD with Gaussian noise overfits dense solutions.
L2R learns to denoise images without needing noise distribution knowledge.
problem Traditional denoising methods require noise distribution knowledge, limiting their applicability.
method L2R uses a learnable monotonic neural network to learn recorruption without distribution knowledge.
result L2R achieves state-of-the-art performance across various noise distributions.
Interpolating label noise makes models vulnerable to adversarial attacks.
problem Adversarial vulnerability of models trained on noisy labels.
method Theoretical analysis of label noise and adversarial risk relationship.
result Uniform label noise induces adversarial risk similar to worst-case poisoning.
NR-GANs learn clean images from noisy data.
problem Learning clean images from noisy training data.
method Introduced a noise generator and distribution/transformation constraints.
result NR-GANs can generate clean images from noisy data.
We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that injected noise speeds up the average convergence of the EM algorithm to a local…
BeGIN benchmarks GNNs for instance-dependent label noise in graphs.
problem Instance-dependent label noise in graph data.
method BeGIN introduces a benchmark with various noise types and evaluates noise-handling strategies across GNN architectures.
result Challenges of instance-dependent noise, especially LLM-based corruption, and the importance of node-specific parameterization.
Enhanced consistency bounds derived for classification under a new noise condition.
problem Enhanced consistency bounds for classification under a new noise condition.
method Model Margin Noise (MM noise) assumption, derived enhanced H-consistency bounds.
result Enhanced H-consistency bounds under MM noise condition, interpolates between linear and square-root regimes.
Proposes a progressive label correction method for feature-dependent label noise.
problem Real-world large-scale datasets often suffer from heterogeneous, feature-dependent label noise.
method A progressive label correction algorithm that iteratively refines the model.
result A classifier trained with this strategy converges to be consistent with the Bayes classifier for various noise patterns.
New methods for handling time-varying label noise in time series classification.
problem Temporal label noise in time series classification tasks.
method Proposed methods to estimate temporal label noise function directly from data.
result Our methods lead to state-of-the-art performance under diverse types of temporal label noise.
NoiseRank reduces label noise without supervision, improving classification accuracy.
problem Label noise in datasets from noisy channels.
method NoiseRank uses Markov Random Fields to estimate and rank instances based on their noise probability.
result NoiseRank improves classification accuracy on noisy datasets.
Paper tackles online control of linear systems with unbounded noise.
problem Online control of linear systems under unbounded noise with unknown convex cost functions.
method Developed an algorithm achieving high-probability regret under unbounded noise, and established regret bound for strongly convex costs and sub-Gaussian noise.
result Achieved high-probability regret under unbounded noise, and regret bound for specific noise and cost conditions.
Efficient algorithm for halfspaces with specific noise conditions.
problem Learning halfspaces with Massart and Tsybakov noise.
method PAC active learning algorithm for d-dimensional halfspaces.
result Near-optimal label complexity under Massart noise and lower than passive learning under Tsybakov noise.
Study on noise models for noisy labels in NLP.
problem Quality of noise models from noisy labels.
method Theoretical analysis and synthetic dataset creation.
result Expected error of noise models derived.
Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate the sparse signal of interest and the sparse noise of no interest.…
TrustNet robustly learns noise patterns from trusted data to improve weakly-supervised classification.
problem Robustness to label noise in weakly-supervised learning.
method TrustNet learns noise patterns from trusted data, then trains a robust classifier using these patterns.
result TrustNet outperforms state-of-the-art methods in robustness to various noise patterns.
Noise enhancement improves generalization in training.
problem Improving generalization in training with controlled noise.
method Noise enhancement method to control SGD noise without changing learning rate or minibatch size.
result Noise enhancement improves generalization for real datasets.
HeMPPCAT improves PCA for data with varying noise.
problem PCA's suboptimal performance on data with heterogeneous noise.
method HeMPPCAT uses a GEM algorithm to estimate factors, means, and noise variances.
result Improved factor estimates and clustering accuracy compared to MPPCA.
The paper examines various RL algorithms to address overestimation and noise issues.
problem Overestimation and noise in deep reinforcement learning algorithms.
method Analysis of DQN, double DQN, DDPG, TD3, and hill climbing algorithms.
result Optimal noise settings for TD3 in specific environments.
Geometry-aware noise improves model generalization on complex manifolds.
problem Improving model generalization on highly curved data manifolds.
method Add geometry-aware noise to input space, projecting Gaussian noise onto tangent space of manifold and mapping it via geodesic curve.
result Geometry-aware noise leads to improved generalization and robustness on highly curved manifolds.
A method uses confidence scores to handle noisy labels for each instance.
problem Learning with noisy labels where each instance's label can randomly change.
method Introduces confidence-scored instance-dependent noise (CSIDN) to estimate transition distributions for each instance.
result Demonstrates the utility and effectiveness of CSIDN through experiments with synthetic and real-world noise.
New insights into noise distribution for self-supervised learning.
problem Challenges the assumption that optimal noise should match data distribution.
method Turns to Noise-Contrastive Estimation (NCE) to define optimality of noise distribution.
result Optimal noise distribution is different from data distribution, challenging GANs assumption.
New method designs joint initial noises for diffusion models to improve diversity and alignment.
problem Independent initial noises limit diversity in generated images.
method Coupling of initial noises, maintaining Gaussian distribution while allowing dependence.
result Repulsive Gaussian coupling improves diversity without increasing sampling cost.
Paper improves SLCB regret bound for bounded noise.
problem Stochastic linear contextual bandits with bounded noise.
method Set-membership estimation (SME) and optimism in the face of uncertainty (OFU).
result Improved regret bound of .
Proposes DISCO, the first CVI for density-based clustering with noise.
problem Evaluation of noise assignments in density-based clustering.
method Density-connectivity and Silhouette Coefficient adaptation for noise evaluation.
result DISCO is the first CVI to explicitly assess noise assignments.
We address noisy Euclidean distances in high dimensions, estimating noise levels and correcting distances.
problem Distorted pairwise Euclidean distances due to heteroskedastic noise.
method Developed a hyperparameter-free approach to jointly estimate noise magnitudes and correct distances.
result Our method provides accurate noise magnitude estimates and corrected distances in high-dimensional settings.
DA-GNN improves robustness of GNNs by modeling noise dependencies.
problem Real-world graph node features often contain noise, leading to performance degradation in GNNs.
method DA-GNN captures noise dependencies using variational inference and new benchmark datasets.
result DA-GNN consistently outperforms existing baselines across various noise scenarios.
The study analyzes how label noise affects deep learning feature learning.
problem The impact of label noise on deep learning feature learning.
method Theoretical analysis of a two-layer convolutional neural network under noisy label conditions.
result Two key stages identified: signal learning in Stage I and noise memorization in Stage II.
PiNGDA learns beneficial noise for graph augmentation stability.
problem Challenges in generating effective and stable graph augmentations.
method PiNGDA uses positive-incentive noise to scientifically analyze and generate beneficial graph augmentations.
result PiNGDA improves GCL performance by learning beneficial noise on graph topology and attributes.
We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise, and demonstrate its effectiveness by designing algorithms with improved noise tolerance guarantees for learning linear separators. We consider both the malicious noise model and the adversarial label nois…
Paper presents robust boosting methods for label noise.
problem Boosting methods degrade in noisy environments.
method Robust Minimax Boosting (RMBoost) with theoretical guarantees.
result RMBoost provides strong classification accuracy and robustness.
As all physical adaptive quantum-enhanced metrology schemes operate under noisy conditions with only partially understood noise characteristics, so a practical control policy must be robust even for unknown noise. We aim to devise a test to evaluate the robustness of AQEM policies and assess the resource used by the po…
Deep-neural-network (DNN) based noise suppression systems yield significant improvements over conventional approaches such as spectral subtraction and non-negative matrix factorization, but do not generalize well to noise conditions they were not trained for. In comparison to DNNs, humans show remarkable noise suppress…