Study shows how bad and good volatility spread differently in forex markets.
problem Understanding how volatility spreads asymmetrically in forex markets.
method High-frequency, intra-day data of major currencies from 2007-2015.
result Negative spillovers are linked to sovereign debt crisis, positive to subprime and monetary policies.
The abstract constructs a set of bad 3-orbifolds and shows how any bad 3-orbifold can be transformed into a good one.
problem Characterizing and transforming bad 3-orbifolds into good ones.
method Explicit construction of bad 3-orbifolds and a method of cutting-and-capping to transform them.
result Any bad 3-orbifold can be transformed into a good 3-orbifold through a finite number of operations.
This paper introduces a gradient analysis framework to improve language model performance by rewarding good examples and penalizing bad ones.
problem Improving language model output quality by penalizing bad examples.
method Gradient analysis of loss functions to reward good examples and penalize bad ones.
result ExMATE is superior to MLE and combining DPO with ExMATE enhances performance.
Unified-GAN improves SSL with both good and bad samples.
problem Improve semi-supervised learning performance with limited labeled data.
method Unified-GAN combines adversarial training with good and bad samples.
result Unified-GAN achieves state-of-the-art performance and robustness to varying labeled data.
Study finds stocks with common firm fears earn lower returns.
problem Identifying and quantifying firm-level investor fears.
method Analysis of equity options to identify common firm-level fears and their impact on stock returns.
result Stocks with exposure to common bad fears earn lower returns and require higher compensation.
The study examines how investor protection and past information affect stock returns and interest rates.
problem Empirical regularities related to investor protection and past information in asset pricing models.
method Developed a dynamic asset pricing model with a controlling shareholder and good/bad memory in budget dynamics.
result Good/bad memory of investors on historical market information affects stock returns and interest rates, strengthening investor protection in high ownership concentration.
Model predicts three market regimes: Good, Bad, and Ugly.
problem Understanding market dynamics and predicting different market states.
method Developed a nonlinear diffusion model of price formation with feedback from money flows and memory of past flows.
result The model predicts three distinct market regimes: Good, Bad, and Ugly.
Asymmetries in volatility spillovers are highly relevant to risk valuation and portfolio diversification strategies in financial markets. Yet, the large literature studying information transmission mechanisms ignores the fact that bad and good volatility may spill over at different magnitudes. This paper fills this gap…
Proposes a new factor to improve BAB strategies by recognizing bad-beta assets.
problem Investors often misprice assets based on beta, ignoring bad-beta.
method Double-sorting on beta and bad-beta to create a new factor.
result The Betting Against Bad Beta factor improves BAB strategies.
BO method improves model fitting for complex parameter landscapes.
problem Optimizing complex, noisy parameter landscapes for computational models.
method Bayesian adaptive direct search (BADS) algorithm.
result BADS consistently finds comparable or better solutions than other methods.
We detect and quantify asymmetries in volatility spillovers using the realized semivariances of petroleum commodities: crude oil, gasoline, and heating oil. During the 1987--2014 period we document increasing spillovers from volatility among petroleum commodities that substantially change after the 2008 financial crisi…
We show that every bad orbifold vector bundle can be realized as the restriction of a good orbifold vector bundle to a suborbifold of the base space. We give an explicit construction of this result in which the Chen-Ruan orbifold cohomology of the two base spaces are isomorphic (as additive groups). This construction i…
Bad models can teach well by replicating noise.
problem Overparameterized models can replicate noise in training data.
method Knowledge distillation from noisy samplers.
result Distillation from samplers approximates Bayes optimal classifier.
Improves VAT method for semi-supervised learning with simpler techniques.
problem Improving semi-supervised learning performance with robust classifiers.
method Adopts Bad GAN approach to generate bad samples and employs adversarial training.
result Achieves competitive performance with fewer computations.
Study finds economic cycle affects bank loan recovery rates.
problem Understanding how economic conditions impact bank loan recovery rates.
method Used a two-state Markov switching mechanism to model credit cycle states and analyze recovery rates.
result Recovery rates and their determinants differ between good and bad economic times.
New rule reduces exploration regret to logarithmic, improving bad episode handling.
problem Improving exploration regret in average reward MDPs.
method Replacing Doubling Trick with Vanishing Multiplicative rule in EVI-based algorithms.
result Regret is logarithmic under the new rule, significantly better than linear.
SIGUA prevents overfitting in noisy data by forgetting unwanted memorization.
problem Overfitting in deep learning models with noisy labels.
method SIGUA combines gradient descent on good data and learning-rate-reduced gradient ascent on bad data.
result SIGUA improves the performance of two base learning methods significantly.
The paper explores learning good policies from past data in large state spaces.
problem Learning good policies from historical data in large state spaces.
method Introduces expressivity assumptions and data coverage for function approximation and algorithmic design.
result A variety of algorithms and their guarantees are presented based on assumptions and desired complexity.
News might trigger jump arrivals in financial time series. The "bad" and "good" news seems to have distinct impact. In the research, a double exponential jump distribution is applied to model downward and upward jumps. Bayesian double exponential jump-diffusion model is proposed. Theorems stated in the paper enable est…
Deep ReLU networks with extra parameters have mostly good loss landscapes.
problem Finding good local minima in the loss landscape of deep neural networks.
method Analyzing shallow and deep ReLU networks with extra parameters on a generic dataset.
result Most activation patterns correspond to regions with no bad local minima.
V-BAD is a first black-box video attack framework that successfully fools deep video recognition models.
problem Vulnerability of video recognition models to black-box adversarial attacks.
method Tentative perturbations transferred from image models and partition-based rectifications for patches of tentative perturbations.
result V-BAD can craft both untargeted and targeted attacks with high success rates using a manageable number of queries.
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
problem Lack of precision in conventional credit rating tools for accurate credit risk prediction.
method Constructs a credit risk assessment model using Linear Discriminant Analysis.
result Demonstrates improved accuracy in predicting good and bad creditors compared to conventional methods.
MCN improves deep neural networks by bettering local minima and generalizing well.
problem Bad local minima and poor generalization in deep neural networks.
method Introducing Maximum-and-Concatenation Networks (MCN) to eliminate bad local minima and improve generalization.
result MCN can autonomously improve local minima's goodness by increasing network depth.
The paper analyzes Thompson Sampling's sensitivity to prior distributions.
problem Understanding the impact of prior distributions on Thompson Sampling's performance.
method Characterizes Thompson Sampling's worst-case regret bounds using martingale theory.
result Proves matching upper and lower bounds for regret in terms of prior probability mass.
This paper compares EM and GD in two-component mixture models, finding EM escapes bad local optima more reliably.
problem Understanding the convergence of EM and GD in mixture models, especially in regions where one component is missing.
method Analyzing regions called one-cluster regions in two-component mixture models of Gaussians and Bernoullis, comparing the propensity of EM and GD to converge to these regions.
result EM escapes one-cluster regions exponentially fast, while GD escapes them linearly fast, indicating EM is less likely to converge to bad local optima.
We study the problem of identifying the top m arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that w…
Add expert knowledge to resolve ambiguities in ANN models.
problem Non-unique parameter fitting in material science.
method Augment a black-box ANN model with expert knowledge at two levels.
result Expert knowledge resolves ambiguities in parameter space.
This paper shows that Hamiltonians and operators can also be put to good use even in contexts which are not purely physics based. Consider the world of finance. The work presented here {models a two traders system with information exchange with the help of four fundamental operators: cash and share operators; a portfol…
The paper shows deep neural networks have no bad local minima and no diverging paths to infinity.
problem The risk of diverging to infinity in deep neural networks.
method Mathematical analysis of regularizers and loss functions.
result For a large class of over-parameterized deep neural networks, the loss function has no bad local minima and no decreasing paths to infinity.
Optimal persuasion involves projecting state vectors onto lower-dimensional 'optimal information manifolds'.
problem Optimal persuasion of another agent observing multi-dimensional data.
method Performing non-linear dimension reduction by projecting state vectors onto the 'optimal information manifold'.
result Optimal information design splits information into 'good' and 'bad' components, revealing only the direction of good information.
New findings suggest non-contrastive learning has many bad minima, not just collapsed ones.
problem The effectiveness of non-contrastive learning in unsupervised feature learning.
method Theoretical analysis and controlled experiments on simple data models.
result Non-contrastive losses have a preponderance of non-collapsed bad minima, and these minima are not avoided during training.
New framework removes harmful momentum effect for long-tailed classification.
problem Challenges in maintaining balanced datasets with long-tailed data.
method Causal inference framework to disentangle and remove harmful effects of momentum.
result Achieves state-of-the-art performance on long-tailed visual recognition benchmarks.
Removes all bad local minima without adding extra units.
problem Neural network loss landscapes have bad local minima.
method Converts all bad local minima to non-local minima at infinity using auxiliary parameters.
result Removes all bad local minima from any loss landscape.
Paper measures asymmetric fear network connectedness for risk prediction.
problem Predicting macroeconomic conditions and economic uncertainty.
method Forward-looking measures from bank option prices.
result Asymmetric network structure predicts economic conditions.
Deep networks avoid bad local minima with no bad valleys.
problem Finding sub-optimal local minima in deep neural networks.
method Analyzing over-parameterized networks with specific activation and loss functions.
result No bad local valleys, implying no sub-optimal strict local minima.
The study measures feature importance in models by randomly ablating features and averaging loss changes.
problem Measuring feature importance in machine learning models.
method Randomly ablating features and averaging loss changes over many points and replacements.
result Uncertainty measures of feature importance are presented.
Study reveals bad local maxima in Gaussian mixture models, affecting EM algorithm performance.
problem Bad local maxima in Gaussian mixture models' likelihood function.
method Analyzes population likelihood function and EM algorithm convergence.
result EM algorithm can converge to bad local maxima with high probability.
A test assesses how well data fits a target density function.
problem Measuring how well data fits a target density function without assuming a specific form.
method Stein's method using Reproducing Kernel Hilbert Space functions to construct a divergence measure, estimated via V-statistic.
result The proposed test accurately assesses goodness of fit for various data types and contexts.
Gradient-flow helps find good minima in complex models.
problem Understanding why gradient-based algorithms work in non-convex optimization.
method Kac-Rice analysis and gradient-flow from statistical physics.
result Gradient-flow finds good global minima in the presence of many spurious local minima.
New attacks reduce bad queries in black-box classifiers, improving effectiveness.
problem Evasion attacks often issue many bad queries, which are costly.
method Design new attacks that minimize bad queries while increasing non-bad queries.
result Reduces the number of bad queries by 1.5-7.3 times, improving attack effectiveness.
The paper analyzes team formation on online platforms, tackling the exploration vs. exploitation dilemma.
problem Matching workers with tasks on online platforms, especially complex ones.
method Analyzed two settings: strongest member vs. weakest member, using regret bounds and optimal algorithms.
result Established fundamental regret bounds and designed near-optimal algorithms for team matchings.
The paper compares two reinforcement learning criteria and finds the Bellman residual a poor proxy.
problem Comparing mean value maximization and Bellman residual minimization in reinforcement learning.
method Derives a method to minimize the Bellman residual over policies and conducts theoretical and empirical analysis.
result The Bellman residual is generally a poor proxy for policy optimization, contrary to its value-based counterpart.
Sparse neural networks training is difficult due to optimization failures and energy landscape issues.
problem Training sparse neural networks leads to suboptimal solutions and optimization failures.
method Investigated optimization dynamics and energy landscape in sparse neural networks.
result Sparse neural networks have a linear path with a monotonically decreasing objective from initialization to a good solution, but not from a bad solution.
Depth alone does not create bad local minima without nonlinearity.
problem Understanding the role of depth and nonlinearity in creating local minima in deep learning models.
method Analyzing the properties of non-convex loss surfaces in deep linear neural networks and proving the absence of bad local minima without nonlinearity.
result Depth alone does not create bad local minima in deep linear neural networks.
PNNs ensure most local optima are global for 2-layer networks.
problem Non-convex optimization challenges in neural networks.
method Constrain weights to lie over a finite set of lines.
result Most local optima are global in PNN optimizations.
RMCSE improves voltage estimation in low-observability distribution systems.
problem Insufficient measurements in distribution system state estimation.
method Combines matrix completion and power system model, minimizes rank and residual with different weights.
result Robust voltage estimation in low-observability systems without bad data detection.
The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data based on Russian Accounting Standards. The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36% of total number of corporate bonds …
The aim of this work is to explore the possible types of phenomena that simple macroeconomic Agent-Based models (ABM) can reproduce. We propose a methodology, inspired by statistical physics, that characterizes a model through its 'phase diagram' in the space of parameters. Our first motivation is to understand the lar…