Minimal domain knowledge used for malware detection with neural networks.
problem Malware detection without explicit feature construction.
method Restricting domain knowledge to extract PE header features and using neural networks.
result Neural networks can learn from raw bytes and perform better than explicit feature approaches.
We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of t…
New analysis shows PE in Transformers increases generalization gap and vulnerability.
problem Understanding the impact of PE on Transformer generalization and robustness.
method Generalization analysis and adversarial Rademacher bounds for a single-layer Transformer with trainable PE.
result PE systematically enlarges the generalization gap and makes models more vulnerable to attacks.
PE-GQNN improves spatial data prediction and uncertainty quantification.
problem Poor calibration of predictive distributions in spatial data models.
method Combines PE-GNNs with Quantile Neural Networks and recalibration techniques.
result PE-GQNN outperforms existing methods in predictive accuracy and uncertainty quantification.
Unified framework analyzes and compares RFF and RoPE PEs for music generation.
problem Efficiently modeling music generation with positional encodings.
method Kernel methods to analyze and compare RFF and RoPE PEs.
result RoPEPool outperforms other methods in melody harmonization.
Pipeline detects pulmonary embolisms from sparse CT images.
problem Manual diagnosis of pulmonary embolisms is laborious and prone to errors.
method Two-stage pipeline using AI, sparse annotations, and robust models.
result Achieved AUC scores of 0.94 on validation and 0.85 on test sets for severe PEs.
Paper explores how non-neural simulators can enhance DP synthetic data generation.
problem Generating differentially private synthetic data without access to foundation models.
method Private Evolution (PE) framework using inference APIs and simulators.
result Sim-PE framework improves downstream classification accuracy and FID scores.
Optimizes SSP's header bidding strategy using Thompson Sampling.
problem Maximizing ad revenue in a competitive SSP market.
method Thompson Sampling algorithm with particle filter for correlated contexts.
result Significantly outperforms classical approaches in real datasets.
K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both app…
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundam…
PES method reduces bias in gradient estimation for unrolled graphs.
problem High variance and bias in gradient estimation for unrolled computation graphs.
method Divide graph into unrolls, apply ES update, accumulate correction terms.
result PES provides unbiased, low-variance gradient estimates.
New method for directed graphs using learnable spectral positional encodings.
problem Challenges in magnetic Laplacians and unitary gauge invariance for directed graphs.
method Learnable spectral PEs of the form hθ(Aq)R, computed in Hermitian block Krylov subspace.
result Gauge-invariant and computationally efficient solution for directed graphs.
A new method for spectral positional encodings in directed graphs using Hermitian block Krylov subspaces.
problem Challenges in spectral positional encodings for directed graphs, including computational complexity and gauge invariance issues.
method Learnable spectral positional encodings of the form hθ(Aq)R, computed in a Hermitian block Krylov subspace from sparse matrix-vector products. result The method is gauge-invariant and converges to the exact eigendecomposition oracle as the depth grows.
SPADE improves demand forecasting accuracy by 4.5% for post-promotion periods.
problem Overreacting to peak events in demand forecasting leads to biased forecasts.
method SPADE splits forecasting into two tasks: one for peak events and another for post-peak events, using masked convolution filters and a specialized Peak Attention module.
result Overall PPE improvement of 4.5%, 30% improvement for most affected forecasts after promotions and holidays, and 3.9% improvement in PE accuracy.
Study of BSE sectors' behavior and indicators of financial crashes.
problem Understanding financial crashes in the Bombay Stock Exchange.
method Analysis of daily returns, cross correlation coefficients, and PE ratios.
result PE ratio can predict impending financial market crashes.
Sherlock uses deep learning to accurately detect data types from column headers.
problem Detecting accurate semantic types of data columns for data science tasks.
method Sherlock is a multi-input deep neural network trained on a corpus of 686,765 data columns.
result Sherlock achieves a support-weighted F1 score of 0.89, outperforming existing methods.
DeepQuarantine detects and quarantines suspicious emails.
problem High-quality spam detection and prevention.
method Convolutional Neural Networks on MIME headers for deep feature extraction.
result DQ enhances spam detection with high precision.
Unified framework for PE and TD methods in continuous time and space.
problem Policy evaluation and TD learning in continuous settings.
method Martingale characterization for designing PE algorithms.
result Convergent time-discretized algorithms converge to continuous-time counterparts.
ES-Single uses ES to estimate gradients in unrolled graphs, reducing variance and improving performance.
problem Estimating gradients in unrolled computation graphs with low variance and stability.
method Evolution strategies (ES) applied to unrolled graphs, with a single perturbation per particle.
result ES-Single reduces variance compared to PES, leading to better performance in various tasks.
Discrete conformal maps on surfaces with vertex decorations are studied.
problem Discrete conformal equivalence for decorated piecewise Euclidean surfaces.
method Intimate relationship between decorated PE-surfaces, canonical tessellations of hyperbolic surfaces, and convex hyperbolic polyhedra; concave variational principle.
result Proof of discrete uniformization theorem for decorated PE-surfaces.
This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing …
New framework replicates private equity performance using AI and liquid strategies.
problem Inadequate trust and transparency in private equity markets.
method Advanced graphical models and asymmetric risk adjustments.
result Liquid, scalable solution that closely mimics private equity performance.
Bayesian optimization with binary auxiliary info for faster target function optimization.
problem Optimizing target functions with expensive binary auxiliary information.
method Mixed-type Gaussian process (MOGP) and information-based acquisition functions (MT-ES, MT-PES).
result Efficient approximation of mixed-type predictive ES via random features.
Packed-Ensembles improve uncertainty estimation in constrained hardware.
problem Hardware limitations restrict the size of ensembles and network capacity, degrading performance.
method Packed-Ensembles (PE) design and train lightweight structured ensembles by modulating encoding space and parallelizing into a single backbone.
result PE accurately preserves diversity and maintains performance on key metrics like accuracy, calibration, and out-of-distribution detection.
We employ random geometric digraphs to construct semi-parametric classifiers. These data-random digraphs are from parametrized random digraph families called proximity catch digraphs (PCDs). A related geometric digraph family, class cover catch digraph (CCCD), has been used to solve the class cover problem by using its…
The paper develops a model to predict IPO events in private equity investments.
problem Lack of publicly available quantitative information for predicting IPO events.
method Combines neural network and survival analysis for predicting IPO probability.
result The neuro-survival model accurately predicts IPO events across various sectors.
Improved GP bandit algorithms for noiseless, varying noise, and RKHS norms.
problem Minimizing regret in Gaussian process bandits with unknown reward functions.
method New upper bound on maximum posterior variance, refined MVR and PE algorithms.
result Optimal regret bounds for noiseless, varying noise, and RKHS norms.
The study finds obstructions for certain Weyl curvature tensors on manifolds.
problem Can manifolds admit metrics with purely electric or magnetic Weyl tensors?
method Analyzes algebraic curvature tensors and their Pontryagin classes on scalar product spaces.
result Obstructions to the existence of metrics with PE or PM Weyl tensors in top-degree cohomology.
Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the argmax of the unknown function; yet, both are plagued by the expensive computatio…
Simple method improves exploration in various decision problems.
problem Improving exploration in sequential decision problems.
method Parameterized Exploration (PE) method that considers time horizon and state of knowledge.
result PE outperforms un-tuned methods in various bandit and decision problem settings.
PE-SVI reduces SVI inference complexity by finding a suitable start point.
problem Complex posterior inference in graphical models leads to suboptimal learning.
method PE-SVI uses a pseudo-encoded start point to reduce gradient steps and step sizes.
result PE-SVI achieves the same ELBo objective as SVI with less than 1% of the required steps.
We consider statistical and algorithmic aspects of solving large-scale least-squares (LS) problems using randomized sketching algorithms. Prior results show that, from an \emph{algorithmic perspective}, when using sketching matrices constructed from random projections and leverage-score sampling, if the number of sampl…
Punctuated Equilibrium (PE) states that after long periods of evolutionary quiescence, species evolution can take place in short time intervals, where sudden differentiation makes new species emerge and some species extinct. In this paper, we introduce and study the effect of punctuated equilibrium on two different ass…
A collaborative algorithm reduces regret in federated linear contextual bandits.
problem Optimizing decision-making in federated learning with heterogeneous data.
method Fed-PE algorithm, leveraging geometric structure of rewards, multi-client G-optimal design.
result Achieves near-optimal regrets with logarithmic communication costs.
Enhancing malware detection with icon features.
problem Improving accuracy in detecting malware.
method Extract icon features using summary statistics, HOG, and a convolutional autoencoder. Cluster icons and integrate these clusters into machine learning models.
result Significant increase in malware prediction model accuracy (10%) when icon clusters are used.
Efficiently clusters noisy data with minimal queries.
problem Clustering elements with noisy oracle feedback.
method Combination of sampling strategy and correlation clustering algorithm.
result First polynomial-time algorithms for NP-hard optimization problem.
Proposes PE-GP-UCB for time-varying Bayesian optimisation.
problem Time-varying Gaussian process bandits with unknown prior.
method PE-GP-UCB algorithm, relying on consistency of function values with priors.
result Regret bound provided for the proposed algorithm.
Gaussian Process Regression accurately models daily pan evaporation in humid climates.
problem Precise estimation of pan evaporation in humid climates using data-based methods.
method Gaussian Process Regression and other machine learning techniques were used to estimate pan evaporation.
result GPR models with specific meteorological parameters performed best in estimating pan evaporation.
Paper extends Steklov eigenvalue estimate to weighted graphs.
problem Steklov eigenvalue estimation on weighted graphs.
method Extended Perrin's estimate to general weighted graphs.
result Characterized rigidity of the extended estimate.
Neural networks for stock price prediction often misrepresent model performance due to flawed error metrics.
problem Flawed prediction error metrics lead to unreliable model evaluations in the securities market.
method Used data from 20 stock datasets across multiple markets and evaluated with four prediction error measures.
result Prediction error value only partially reflects model accuracy and fails to represent stock price direction.
Paper proposes detecting video manipulation using stream descriptors.
problem Misuse of manipulated video content.
method Binary classifiers on multimedia stream descriptors.
result Scalable approach can detect high-quality manipulations.
We provide an alternative, simpler proof of the existence of thick triangulations for noncompact C1 manifolds. Moreover, this proof is simpler than the original one given in \cite{pe}, since it mainly uses tools of elementary differential topology. The role played by curvatures in this construction is also…
AD-HOC simplifies high-order derivative calculations in C++.
problem Efficiently computing high-order derivatives in C++.
method A C++ package that calculates derivatives of arbitrary order without code generation.
result Derivatives of arbitrary order computed in a single pass.
Paper introduces a new method for Transformers with linear complexity.
problem No efficient relative positional encoding for linear Transformer models.
method Stochastic Positional Encoding (SPE) that replaces classical RPE.
result SPE behaves like RPE and performs well on benchmarks.
In this paper, we consider the challenge of maximizing an unknown function f for which evaluations are noisy and are acquired with high cost. An iterative procedure uses the previous measures to actively select the next estimation of f which is predicted to be the most useful. We focus on the case where the function ca…
New model predicts sales of new products with short life cycles.
problem Forecasting sales of new products with short lead times and life cycles.
method Developed an exponential factorization machine (EFM) to consider attributes and pairwise interactions.
result EFM model outperforms existing models in terms of MAPE and MAE.
We consider statistical as well as algorithmic aspects of solving large-scale least-squares (LS) problems using randomized sketching algorithms. For a LS problem with input data (X,Y)∈Rn×p×Rn, sketching algorithms use a sketching matrix, S∈Rr×n with $r \…
Study policy gradient and actor-critic methods for continuous-time reinforcement learning.
problem Continuous-time reinforcement learning with policy gradient and actor-critic approaches.
method Regularized exploratory formulation, martingale approach, simultaneous policy and value function updates.
result Proposed two types of actor-critic algorithms for online and offline learning.