Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The first method can be applied to any pre-trained prediction rule, while the second method deals specifically w…
New reshaping method improves hyperparameter search over random search.
problem Improving hyperparameter search efficiency.
method Introducing reshaping techniques to optimize search distributions.
result Substantial gains over random search in various experiments.
RESHAPE explains financial statement anomalies by aggregating explanations from AENNs.
problem Detecting and explaining accounting anomalies in financial audits is challenging.
method Proposes RESHAPE to explain model output on an aggregated attribute-level.
result RESHAPE provides more comprehensible explanations compared to existing methods.
SignSGD outperforms SGD in linear regression with optimal scaling laws under PLRF model.
problem Improving linear regression performance with signSGD under power-law random features.
method Analysis of signSGD risk under PLRF model, comparison with SGD, identification of unique effects.
result SignSGD can have a steeper compute-optimal slope than SGD in noisy regimes, especially with WSD schedule.
Gradient descent reshapes the function space of neural networks.
problem Understanding how feature learning affects the function space of neural networks.
method Characterized the evolution of the feature space during training using a two-layer neural network.
result Gradient descent induces a data-adaptive deformation that selectively enhances signal-aligned directions.
Forward hedging reshapes incentive provision in firms.
problem How does forward hedging affect incentive provision in firms?
method We consider a CARA framework to jointly characterize optimal production, compensation, and static hedging in equilibrium.
result Delegation and external hedging are partial substitutes, and delegation can increase firm value even when the agent is more risk averse.
This work explores how overparametrization and priors affect Bayesian neural network posteriors.
problem Symmetries, non-identifiabilities, and weight-space priors fragment and inflate BNN posteriors.
method We study the interplay between overparametrization and priors in BNN posteriors, deriving key phenomena and validating through experiments.
result Overparametrization induces structured, prior-aligned weight posterior distributions.
This study explains RL training dynamics in LLMs, focusing on token-level optimization and reasoning pattern reshaping.
problem Understanding the training dynamics of RL in LLMs to improve their reasoning capabilities.
method Empirical analysis and theoretical modeling of RL training process, focusing on reasoning patterns and token optimization.
result RL primarily optimizes a sparse subset of critical tokens, reshaping reasoning pattern distributions and affecting model performance.
We study the phase retrieval problem, which solves quadratic system of equations, i.e., recovers a vector x∈Rn from its magnitude measurements yi=∣⟨ai,x⟩∣,i=1,...,m. We develop a gradient-like algorithm (referred to as RWF representing reshaped W…
LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and extttCal−PIT algorithm. result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
When Daan Krammer and Stephen Bigelow independently proved that braid groups are linear, they used the Lawrence-Krammer-Bigelow representation for generic values of its variables q and t. The t variable is closely connected to the traditional Garside structure of the braid group and plays a major role in Krammer's alge…
SignSGD analysis quantifies its effects in high dimensions.
problem Understanding signSGD's effects in high-dimensional settings.
method High-dimensional analysis of signSGD, deriving SDE and ODE for risk.
result Quantification of signSGD's effects: effective learning rate, noise compression, diagonal preconditioning, gradient noise reshaping.
DNN is presenting human-level performance for many complex intelligent tasks in real-world applications. However, it also introduces ever-increasing security concerns. For example, the emerging adversarial attacks indicate that even very small and often imperceptible adversarial input perturbations can easily mislead t…
Shape adaptor learns flexible resizing factors for neural networks.
problem Fixed resizing layers limit network performance.
method Learnable reshaping factor for traditional resizing layers.
result Performance increases consistently across multiple datasets.
This paper reviews ML applications in finance, enhancing asset pricing models.
problem Limitations of traditional asset pricing models in complex market dynamics.
method Exploring ML models including supervised, unsupervised, semi-supervised, and reinforcement learning.
result Enhanced return prediction and portfolio optimization through ML integration.
Variance-Calibrated Modulation (VCM) addresses the likelihood trap in LLMs by reshaping the probability distribution before truncation.
problem LLMs fall into the likelihood trap, leading to repetitive degeneration and vocabulary dullness.
method VCM reshapes the probability distribution before truncation through Contextual Searchlight and Adaptive Self-Debiasing.
result VCM mitigates the likelihood trap across open-ended generation, factual QA, and mathematical reasoning.
New LSH methods for tensor data improve efficiency and space usage.
problem Efficiency and space usage issues in LSH for tensor data.
method Proposes new LSH methods using CP and TT decompositions for Euclidean and cosine similarity.
result Space-efficient and scalable LSH for tensor data.
InteractE improves link prediction in KGs by increasing feature interactions.
problem Improving link prediction in knowledge graphs by inferring missing links.
method Feature permutation, novel feature reshaping, circular convolution.
result InteractE outperforms ConvE on multiple KG datasets.
In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with c…
SMM preserves matrix data structure for SVM classification.
problem Preserving spatial correlations in matrix data for SVM.
method SMM uses spectral elastic net combining nuclear and Frobenius norms.
result SMM improves SVM performance on matrix data.
Adaptive rerouting reshapes impacts of maritime chokepoint disruptions
problem How disruptions to shipping traffic at chokepoints affect global economy
method Empirically calibrated full-scale agent-based model of global commercial shipping fleet
result Rerouting changes arrival losses under chokepoint closures
Framework for causal signals in non-stationary financial markets.
problem Constructing causal signals in non-stationary financial time series.
method Combines normalized indicators and causally computed derivatives, with hysteresis-based decision mapping.
result Demonstrates risk-reshaping effect with smoother trajectories and reduced drawdowns.
We describe a framework for deriving and analyzing online optimization algorithms that incorporate adaptive, data-dependent regularization, also termed preconditioning. Such algorithms have been proven useful in stochastic optimization by reshaping the gradients according to the geometry of the data. Our framework capt…
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including "sampled ancestors" in which we sequence a genotype along wit…
Optimizes sampling in continuous domains by adjusting search distribution.
problem Improving sampling efficiency in continuous domains.
method Analyzes and refines the search distribution based on population size and dimension.
result Explicit values for reshaping the search distribution are provided.
Project estimates risk-neutral dependence from option prices.
problem Extracting risk-neutral dependence from option prices.
method Projection estimator using portfolios of observed options.
result Estimates risk-neutral dependence in incomplete markets.
FineMorphs models smooth transformations for multivariate regression.
problem Efficiently modeling complex transformations for multivariate regression.
method Optimal control of affine and diffeomorphic transformations using smooth vector fields.
result FineMorphs can reduce dimensionality and adapt to large datasets.
AI enhances financial services but humans are irreplaceable for empathy, presence, and ethics.
problem AI's limitations in financial services, especially with small datasets and human judgment.
method EPOCH framework highlighting five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope.
result Humans are essential for trust, innovation, and consumer experience in financial services.
L2O uses ML to optimize traditional optimization techniques.
problem Real-world optimization problems with shared structures.
method Exploiting shared structures to enhance optimization techniques.
result Better or faster solutions through machine learning integration.
Geotechnics adopts data-driven methods from materials informatics.
problem Soil complexity and lack of comprehensive data.
method Leveraging deep learning and transfer learning for feature extraction.
result Revolutionary potential of advanced computational tools in geotechnics.
The study examines robust decision-making in volatile financial markets, finding action robustness is more impactful than uncertainty tolerance.
problem Sequential decision making in high-frequency markets under evolving uncertainty.
method Analyzes two dimensions of robustness: uncertainty tolerance and action robustness, using simulations and empirical evidence.
result Action robustness has a larger impact on profitability than uncertainty tolerance, and excessive robustness can reduce profitability in illiquid markets.
SIBRE boosts reinforcement learning convergence by rewarding improvement over past performance.
problem Improving the rate of convergence in reinforcement learning.
method SIBRE is a reward shaping approach that rewards improvement over the agent's own past performance.
result SIBRE converges faster and more stably to the optimal policy compared to baseline RL algorithms.
Different optimizer choices lead to different financial model predictions.
problem The impact of optimizer choice on neural network models in financial time series.
method Analysis of large-scale volatility forecasting for S&P 500 stocks using various model-training-pipeline pairs.
result Optimizer choice reshapes non-linear response profiles and temporal dependence in financial models, leading to different functional outcomes.
This paper studies how to find compact state embeddings from high-dimensional Markov state trajectories, where the transition kernel has a small intrinsic rank. In the spirit of diffusion map, we propose an efficient method for learning a low-dimensional state embedding and capturing the process's dynamics. This idea a…
New method models portfolios with leptokurtic risk factors using Gram-Charlier expansions.
problem Modeling portfolios with excess kurtosis.
method GC-like expansions of the hyperbolic-secant law to account for leptokurtosis.
result Portfolio distribution with risk factors modeled as GC-like expansions of the HS law.
This paper uncovers the low-rank structure of neural network Hessians.
problem Understanding the structure of Hessians in neural networks.
method Proposes a decoupling conjecture to decompose layer-wise Hessians into Kronecker products of smaller matrices.
result Proves the structure of top eigenspaces in 2-layer networks and shows high overlap in top eigenvectors across different models.
This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for mechanical component…
One of the ways to train deep neural networks effectively is to use residual connections. Residual connections can be classified as being either identity connections or bridge-connections with a reshaping convolution. Empirical observations on CIFAR-10 and CIFAR-100 datasets using a baseline Resnet model, with bridge-c…
This research introduces a control system for managing DeFi money supply.
problem Maintaining the value of issued currency in decentralized finance.
method Introduces a time-weighted Proportional-Integral-Derivative (PID) control system.
result Protects the value of issued currency by adapting to market activities.
This work characterizes how data augmentation shapes neural representations.
problem Understanding the impact of data augmentation on neural network representations.
method Embedding neural network hidden representations into a metric space invariant to transformations, analyzing shape-space trajectories.
result Increasing data augmentation strength leads to well-behaved trajectories in the embedded space, and different augmentation types steer representations in distinct directions.
Private credit markets have expanded significantly, offering unique lending technology to private equity firms.
problem Understanding the growth and characteristics of private credit markets.
method Systematic survey of academic literature, development of integrated theoretical framework, empirical evidence.
result Private credit markets offer a distinct lending technology with higher spreads over syndicated loans.
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…
New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.
problem Limitations of purely memorized models in recalling large amounts of factual information.
method Demonstrated the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall.
result Proved that tool-use enables unbounded factual recall via a simple and efficient circuit construction.
Study shows how macroeconomic news affects intraday price and order flow dynamics.
problem Understanding how macroeconomic news impacts intraday price and order flow dynamics.
method Structural VAR model identified through heteroskedasticity, estimated at one-second frequency for each 15-minute interval.
result Macroeconomic news announcements reshape price-flow dynamics, with significant impacts on price and flow impacts at the one-second horizon.
This paper investigates the phase retrieval problem, which aims to recover a signal from the magnitudes of its linear measurements. We develop statistically and computationally efficient algorithms for the situation when the measurements are corrupted by sparse outliers that can take arbitrary values. We propose a nove…
Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.
problem Managing venture capital portfolio risk, especially extreme outcomes.
method Gaussian-copula-based framework that learns deal-level dependence from observed joint success frequencies.
result Correlation amplifies extreme upside outcomes, shifting portfolio distribution toward heavier right tails.
This paper examines market misconduct in DeFi and proposes regulatory solutions.
problem Novel forms of market misconduct in DeFi.
method Comprehensive analysis, comparative study, empirical measurements, and tailored regulatory framework investigation.
result Identification of key areas for regulatory enhancement in DeFi.
Study optimal transport for robust optimization, showing how adversary's strategy relates to regularization.
problem Optimizing under uncertain parameters with a fictitious adversary reshaping a reference distribution.
method Introduces optimal transport and regularization to relate robustification to variation and Lipschitz norms.
result Conditions for existence and computability of Nash equilibrium between decision-maker and adversary.