arXiv research

This work extends balancing to various simulation-based inference algorithms for more conservative posterior approximations.

Estimates density ratio for two-sample comparison using tree models.

Unified theory for causal inference using various methods.

Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clusters, corresponding to a balanced $k$-cut of the graph, are either based on greedy techniques or heuristics which have weak connection to th…

We study Smoothed Online Convex Optimization, a version of online convex optimization where the learner incurs a penalty for changing her actions between rounds. Given a $Ω(\sqrt{d})$ lower bound on the competitive ratio of any online algorithm, where $d$ is the dimension of the action space, we ask under what conditio…

ADAPT method controls balance between sparse generator and discriminator in GANs.

In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning…

New method dynamically adjusts UTD ratio to balance under- and overfitting in RL.

We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisions between rounds. We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strong…

Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain fu…

Study introduces new financial ratios for better predicting company performance.

The study uses CoDa to analyze family business financial ratios, highlighting methodological issues.

GENIE balances domain-invariant feature learning and gradient alignment for improved DG performance.

Estimates causal effects using neural networks for balancing covariates.

A new law limits kurtosis contrast in balanced mixtures.

This article contains the first published example of a real economic balance sheet where the Solvency II ratio substantially depends on the seed selected for the random number generator (RNG) used. The theoretical background and the main quality criteria for RNGs are explained in detail. To serve as a gauge for RNGs, a…

This paper uses PCA and FA for feature selection in credit rating.

FORE evaluates occupancy ratios without requiring Bellman completeness.

BNRE improves simulation-based inference by producing more conservative posteriors.

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory ta…

MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio

Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.

Unified framework for counterfactual survival analysis improves treatment effect estimation.

Hybrid approach combines Markowitz's theory with reinforcement learning for optimal portfolio management.

We investigate the dynamical behavior in the large scale region of non-equilibrium systems, by employing data on the assessed value of land in 1983 -- 2006 Japan. In the system we find the detailed quasi-balance, which has the symmetry: x_1 -> a {x_2}^θ (x_1 and x_2 are two successive land prices). By using the detaile…

Trading system uses NP-hard optimization to select stocks for high Sharpe ratio trading.

The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.

Study tackles OPE in confounded settings, estimating policy value from proxies.

DiPriMe forests use private medians to create balanced tree splits for privacy-protected data.

New bounds on IDS for RL show how to balance computation and learning efficiency.

Unified framework for debiased machine learning using Riesz representer and Bregman divergence.

DINs use deep learning to optimize portfolio Sharpe ratio without manual feature engineering.

This work studies the impact of intra-/inter-class diversity on pre-training datasets and finds a balance for optimal performance.

Adapts Altman's model to compositional data for bankruptcy prediction.

Optimal reinsurance when Value at Risk and expected surplus is balanced through their ratio is studied, and it is demonstrated how results for risk-adjusted surplus can be utilized. Simplifications for large portfolios are derived, and this large-portfolio study suggests a new condition on the reinsurance pricing regim…

Flashback Learning balances model stability and plasticity in continual learning.

A new method improves density ratio estimation efficiency and accuracy.

SIGTRON improves classification accuracy for imbalanced datasets.

In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the de…

Paper proposes a new uncertainty measure for active learning in neural networks.

Q-learning is a simple and powerful tool in solving dynamic problems where environments are unknown. It uses a balance of exploration and exploitation to find an optimal solution to the problem. In this paper, we propose using four basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent. Simulation…

We prove that the Kontsevich tetrahedral flow $\dot{\mathcal{P}} = \mathcal{Q}_{a:b} (\mathcal{P})$, the right-hand side of which is a linear combination of two differential monomials of degree four in a bi-vector $\mathcal{P}$ on an affine real Poisson manifold $N^n$, does infinitesimally preserve the space of Poisson…

Paper uses DRL to optimize portfolios, balancing risk and return.

The thesis tackles overconfident approximations in simulation-based inference.

We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actions, each of which stochastically consumes some combination of resources, and returns different rewards for the resources consumed. We consid…

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

Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which requires a careful balancing of training between a generator network and a critic network. Maximum mean discrepancy networks (MMD-nets) avoid this i…

Develops a direct debiased machine learning framework using Bregman divergence.