arXiv research

Enhances Cox model for survival analysis with symbolic non-linear log-risk functions.

Develops pathwise analysis for log-optimal portfolios using rough paths theory.

The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have si…

The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.

Study on policy gradient for stochastic bandits using diffusion approximation.

LMC achieves sqrt(d) dependence in sampling error, improving previous bounds.

Improved convergence for non-log-concave sampling.

CAVI converges for log-concave measures via optimal transport.

Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs…

This paper extends compositional data analysis using graph signal processing.

Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the genera…

Differentially private log-location-scale regression models improve privacy in statistical analysis.

Improved sampling guarantees for weakly log-concave distributions.

Paper analyzes statistical properties of log-cosh loss function.

In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. --- An advanced version is included in arXiv:1703.02000 "Activation Maximization Generative Adversarial Nets". Please refer Section 6 in 1703.02000 for deta…

Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a non-Gaussian distribution. In this paper, we propose a novel NGCA algorithm based on log-density gradient estimation. Unlike existing methods, the proposed NGCA algorithm identifies the linear subspac…

New ANN method for imputing rounded zeros in compositional data.

PACMAN provides bounds for classification tasks considering accuracy vs. negative log-loss mismatch.

Study tests rough fractional volatility model across different time scales, revealing new volatility patterns.

We study the linear contextual bandit problem with finite action sets. When the problem dimension is $d$, the time horizon is $T$, and there are $n \leq 2^{d/2}$ candidate actions per time period, we (1) show that the minimax expected regret is $Ω(\sqrt{dT (\log T) (\log n)})$ for every algorithm, and (2) introduce a V…

Improves SGM convergence bounds in W2-distance without strict assumptions.

The paper assesses dimensionality reduction for cryptocurrency link prediction.

Study compares Bitcoin, gold, and gas price complexity using multifractal and multiscale entropy methods.

New analysis of signSGD with random reshuffling shows faster convergence rates.

Study identifies Kähler-Einstein, Kähler-Ricci soliton, and Sasaki-Einstein metrics on log del Pezzo surfaces.

The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is ver…

Corrected graph convolutions improve node classification on graphs.

Log-ergodic model improves velocity of money prediction.

Deep signature/log-signature FBSDE algorithm improves accuracy and training time.

GN algorithm solves batched bandit for nondegenerate functions near-optimally.

Improved sampling guarantees for underdamped Langevin Monte Carlo without restrictive assumptions.

ResNets approximate log-Gaussian at initialization, improving network performance.

This study was conducted to find an appropriate statistical model to forecast the volatilities of PSEi using the model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the R software, the log returns of PSEi is modeled using various ARIMA models and with the presence of heteroskedasticity, the l…

We propose that imitation between traders and their herding behaviour not only lead to speculative bubbles with accelerating over-valuations of financial markets possibly followed by crashes, but also to ``anti-bubbles'' with decelerating market devaluations following all-time highs. For this, we propose a simple marke…

Improved KLMC for sampling under various conditions.

The paper develops methods for sampling from log-concave distributions with constraints.

New framework predicts AMP behavior in spiked models for finite iterations.

The cyclic block coordinate descent-type (CBCD-type) methods, which performs iterative updates for a few coordinates (a block) simultaneously throughout the procedure, have shown remarkable computational performance for solving strongly convex minimization problems. Typical applications include many popular statistical…

Improved Langevin algorithms with prior diffusion achieve dimension-independent convergence for non-log-concave distributions.

LMC algorithm receives first convergence guarantees under weak smoothness conditions.

SurvMixClust clusters survival data and predicts individual survival curves.

The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience. Existing analysis on this problem, however, usually relies on $\ell_{2,\infty}$ projection or regularization that involves unknown model parameters, although th…

Unified analysis of multi-attribute graph learning with non-convex penalties.

New method reduces regret and communication costs in federated Q-learning.

Study on online regression with noise, achieving near-optimal regret bounds.

TDA detects financial bubbles through early warning signals.

VAEs improve representation learning by inverting the data-generating process through self-consistency.

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows rela…