Paper models financial contagion via leverage requirements and fire sales.
problem Financial contagion through leverage requirements and fire sales.
method Network model with multiple illiquid assets, proving equilibrium existence.
result Calibrated models show systemic risk varies with leverage requirements.
Study examines liquidation, leverage, and optimal margin requirements in Bitcoin futures markets.
problem Understanding and optimizing margin requirements in Bitcoin futures markets.
method Empirical analysis using generalized extreme value theory and BitMEX data.
result Margin requirements need to be significantly higher to reduce daily margin calls.
Paper develops efficient methods for leverage score sampling and kernel ridge regression.
problem Efficiently sampling leverage scores for large matrices.
method Novel algorithm for leverage score sampling and kernel ridge regression solver.
result Proposed algorithms are the most efficient and accurate for leverage score sampling and kernel ridge regression.
We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns wi…
This paper improves random feature sampling using empirical leverage scores.
problem Optimizing the number of features for kernel approximation and supervised learning.
method Uses empirical leverage scores to optimize feature sampling.
result Empirical sampling of random features using leverage scores outperforms vanilla Monte Carlo sampling.
We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high…
Deep learning tackles X-ray noise without clean data.
problem Lack of clean X-ray images for deep learning denoising.
method Uses Stein's Unbiased Risk Estimator (SURE) to train a deep neural network.
result SURE-based approach effectively denoises X-ray images.
The principal portfolios of the standard Capital Asset Pricing Model (CAPM) are analyzed and found to have remarkable hedging and leveraging properties. Principal portfolios implement a recasting of any correlated asset set of N risky securities into an equivalent but uncorrelated set when short sales are allowed. Whil…
Binary testing for softmax models requires many samples, similar to leverage score models.
problem Binary hypothesis testing for softmax models and leverage score models.
method Analyzing sample complexity and drawing analogies between models.
result Sample complexity is asymptotically \(O(ε^{-2})\), where \(ε\) is the distance between model parameters.
Dynamic acquisition of features improves predictions with limited data.
problem Limited or uncertain data requires additional relevant information for accurate assessments.
method Proposes models that dynamically acquire new features using conditional mutual information and arbitrary conditional flow.
result Demonstrates superior performance over baselines in multiple settings.
Estimates volatility of volatility and leverage effect using high-frequency options data.
problem Estimating volatility of volatility and leverage effect from high-frequency options data.
method Model-free estimators using characteristic function of price increments and spot volatility.
result Developed feasible inference methods for estimating volatility of volatility and leverage effect.
Method improves volatility targeting for index construction.
problem High turnover, leverage spikes, and sensitivity to estimation error in existing volatility-targeting strategies.
method Proportional-control approach for setting index weights that corrects tracking error through feedback.
result The proportional-control approach achieves the target volatility more effectively than open-loop alternatives.
Unified analysis improves random Fourier features for kernel methods.
problem Pessimistic theoretical bounds on random Fourier features.
method Unified risk analysis for squared error and Lipschitz loss.
result Improved bounds on number of features for convergence.
EMDQN uses episodic memory to improve RL efficiency.
problem Sample inefficiency of deep RL algorithms.
method Leverages episodic memory to supervise training.
result Significantly reduces interaction rounds for state-of-the-art performance.
A new algorithm for efficient kernel Nyström approximation.
problem Efficiently approximating large kernel matrices for machine learning.
method Recursive sampling of landmark points using ridge leverage scores.
result Scalable and accurate kernel approximation with linear runtime.
Researchers relax the CVF's smoothness requirement to create more flexible flow models.
problem Challenges in constructing flexible density models due to the CVF's smoothness requirement.
method Introduce L-diffeomorphisms as generalized transformations that may violate smoothness on zero Lebesgue-measure sets. result The relaxation allows for the use of non-smooth activation functions like ReLU in residual flows.
Domain adaptation reduces prosthetic training time for amputees.
problem Reducing training time for non-invasive myoelectric prostheses.
method Evaluation of domain adaptation algorithms on amputee and intact subjects data.
result Previous experience from other subjects reduces training time by about an order of magnitude.
We consider the problem of exact recovery of any m×n matrix of rank ϱ from a small number of observed entries via the standard nuclear norm minimization framework. Such low-rank matrices have degrees of freedom (m+n)ϱ−ϱ2. We show that any arbitrary low-rank matrices can be recovered exa…
A new sampling strategy for random Fourier features reduces computation time and improves prediction performance.
problem Efficient generation of random Fourier features for kernel approximation.
method Surrogate leverage weighted sampling guided by kernel alignment, avoiding matrix inversion.
result Time complexity reduced from O(ns^2+s^3) to O(ns^2), comparable or slightly better prediction performance.
New methods identify causal effects without needing complete proxy variables.
problem Identifying causal effects in the presence of unmeasured confounders.
method Partial identification methods that do not require completeness of proxy variables.
result Obtain bounds on causal effects using available proxy variables.
FedSyn generates synthetic data from multiple organizations' datasets.
problem Generating diverse synthetic data from limited datasets.
method Federated learning and GAN for privacy-preserving synthetic data generation.
result Synthetic data can be generated from diverse datasets without accessing individual data.
Axient creates a blockchain protocol for managing leveraged event markets, separating roles and formalizing capital management.
problem Managing credit and losses in leveraged event markets on a blockchain.
method Develops a venue-agnostic on-chain credit architecture, formalizing roles and capital management.
result Establishes a balanced accounting system, settlement-confirmed debt priority, and loss-allocation mechanisms.
This paper improves matrix completion by leveraging element importance and non-uniform sampling.
problem The challenge of completing low-rank matrices from noisy, subsampled measurements.
method Employing leverage scores to characterize element importance and devising a biased sampling procedure.
result Theoretical and empirical evidence shows that a smaller number of entries (about O(nrlog2(n))) can recover a low-rank matrix with noise. Sketched SVD improves SVD runtime for large datasets.
problem Efficiently applying SVD to large datasets.
method Randomized sketching to approximate SVD.
result Sketched SVD provides accurate leverage score ordering.
New method uses unlabeled data to improve model robustness across different environments.
problem Learning robust models for new, unseen environments when labeled data are scarce.
method Regularizes model sensitivity to perturbations in covariate means and covariances without requiring labels.
result Empirically validated on physical and physiological datasets, demonstrating improved robustness.
Algorithm leverages low-rank relations between surrogate tasks for structured prediction.
problem Structured prediction with large or infinite-dimensional surrogate spaces.
method Trace norm regularization to leverage relationships between surrogate outputs without explicit coding/decoding functions.
result Our algorithm can improve generalization performance over previous methods.
This paper proves subsampled Newton methods work for high-dimensional data.
problem The high cost of forming Hessian matrices in Newton methods for high-dimensional data.
method Subsampled Newton methods approximate Hessians using subsampling techniques, requiring only dmeffγ samples. result Only dmeffγ samples are needed, where dmeffγ is much smaller than d for high-dimensional data. Optimizes cryptocurrency exchanges' risk management by reducing positions based on leverage.
problem Managing risk in cryptocurrency futures exchanges during large price moves.
method Formulates ADL as an optimization problem to minimize risk of loss, using a water-filling rule to equalize leverage.
result The optimal ADL policy minimizes maximum leverage among participants, providing a transparent and implementable benchmark.
A new method uses local sensitivity to improve importance sampling for approximating complex loss functions.
problem Approximating complex loss functions using subsampling with strong theoretical guarantees.
method Introducing local sensitivity to measure data point importance and using leverage scores for efficient estimation.
result Local sensitivity sampling can be efficiently estimated and used to approximate complex loss functions with strong guarantees.
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a given matrix. Taking an approach different from existing literature, our method first involves a specific biased sampling, with an element being chosen based on the leverage scores of its row and column, and then involves we…
DART2 enhances multiple testing by leveraging ancillary information robustly.
problem Enhancing multiple testing power with uncertain ancillary information.
method Distance-assisted multiple testing procedure (DART2) that handles both helpful and misleading ancillary information.
result DART2 asymptotically controls FDR and improves power when ancillary information is helpful, maintaining FDR and power otherwise.
In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we…
Improved aspect detection from few seed keywords.
problem Fine-grained aspect detection from user reviews is labor-intensive.
method Weakly supervised co-training with student-teacher approach.
result Significant improvement in F1 scores over previous methods.
SQUEAK reduces space complexity for Nystrom approximations in KRR.
problem Large datasets in KRR require impractical storage space.
method SQUEAK uses unnormalized ridge leverage scores for incremental updates.
result Space complexity improved with constant factor worse than exact RLS.
Efficiently estimates private least squares with linear error growth.
problem Private estimation of ordinary least squares with bounded residuals and leverage.
method Scaled noise added to a stable nonprivate estimator of the regression vector.
result Near-optimal accuracy guarantee with linear error growth in dimension.
ELICA helps analysts understand unfamiliar domains by extracting relevant terms.
problem Communication barriers between analysts and stakeholders in unfamiliar domains.
method ELICA uses WFSTs to dynamically extract and label requirements-relevant knowledge from text and non-linguistic cues.
result ELICA supports analysts in understanding and eliciting requirements from unfamiliar domains.
SQUEAK approximates kernel matrices without storing the full matrix, scaling to large datasets.
problem Large datasets make kernel-based methods impractical due to high time and space requirements.
method Sequentially processes the dataset, using RLS sampling to create a small dictionary for accurate approximations.
result SQUEAK achieves accurate kernel matrix approximations with a number of points only dependent on the effective dimension of the dataset.
Low-rank matrix completion is an important problem with extensive real-world applications. When observations are uniformly sampled from the underlying matrix entries, existing methods all require the matrix to be incoherent. This paper provides the first working method for coherent matrix completion under the standard …
Securely trains neural networks remotely with deep learning's flaws.
problem Secure and efficient training of neural networks over unsecured channels.
method Leverages deep learning's weaknesses for secure training.
result Efficient and secure training of neural networks remotely.
Neural operators solve families of 2BSDEs efficiently.
problem Solving infinite families of 2BSDEs on bounded domains.
method Introduces a mild generative neural operator model to approximate solutions.
result Solution operators can be approximated by neural operators with polynomial parameters.
RINS-T solves time series inverse problems robustly without pretraining.
problem Recovering original signals from corrupted time series data.
method Implicit neural solvers with robust optimization techniques.
result RINS-T achieves high recovery performance without pretraining.
Temporal Normalizing Flows enhance density estimation of time-dependent data.
problem Accurate and robust density estimation of time-dependent stochastic data.
method Leveraging normalizing flows for temporal data, tNFs estimate multi-scale distributions without prior scale knowledge.
result Temporal Normalizing Flows improve density estimation of time-dependent data, including multi-scale distributions.
Unified framework denoises data and abstains from uncertain predictions.
problem Data quality and predictive uncertainty in deep neural networks.
method Unified filtering framework leveraging data density.
result Framework outperforms state-of-the-art techniques in denoising and abstaining.
Behavior Transfer improves reinforcement learning by leveraging pre-trained policies.
problem Efficient transfer of knowledge in reinforcement learning.
method Behavior Transfer (BT) that uses pre-trained policies for exploration.
result BT combined with pre-training leads to better solutions than without pre-training.
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
DiffusionBlocks trains neural networks by breaking them into independent blocks, reducing memory usage.
problem Memory bottlenecks in end-to-end neural network training.
method Transforming transformer-based networks into independent trainable blocks via a denoising process.
result Independent block-wise training matches end-to-end training performance while reducing memory requirements.
Bruno model learns from sets of complex observations using deep learning.
problem Learning from sets of high-dimensional, complex observations with generalization.
method Deep Recurrent Model leveraging deep learning for exact Bayesian inference, provably exchangeable.
result The model can generate new samples conditionally on previous ones with linear cost in the size of the conditioning set.
Paper presents unsupervised calibration for split conformal classification.
problem Inconvenient requirement of labeled calibration samples.
method Uses unsupervised calibration samples alongside supervised training samples.
result Achieves comparable performance to supervised calibration methods.