arXiv research

Lipschitz GANs solve gradient uninformativeness in GANs.

New method speeds up Bayesian inference for complex simulators.

The paper sets bounds on how much regret is unavoidable in adaptive LQR with unknown B-matrix.

Neural networks compress uninformative input directions, improving test error.

Modeling market dynamics with informed and uninformed traders and fads.

Adding uninformative labels improves tumor segmentation in low-data mammography.

The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the…

Study shows mutual funds add little value for uninformed investors.

SpecGD mitigates misalignment in phase retrieval models with anisotropic inputs.

Informed traders strategically reveal noisier signals, making prices less responsive to public information.

New approach for uninformed investors to optimize execution costs.

Study dynamic equilibrium with insider and general uninformed agent preferences.

MSG-GAN stabilizes GANs by passing gradients at multiple scales.

Study shows overparametrization can shift and bend loss landscapes, affecting signal recovery.

Hybrid Policy Optimization tackles reinforcement learning in hybrid spaces, improving performance over PPO.

A simple strategy optimizes broker-client trading, reducing price discounts for informed traders.

Reconstruction-based learning produces uninformative features for perception tasks.

Boosted GFlowNets improve exploration by sequentially training GFlowNets with residual rewards.

This paper studies how AMMs can minimize losses from arbitrage while retaining uninformed trading activity.

Study SGD dynamics in sequence models, revealing training phases and influence of sequence length.

VDB constrains discriminator to improve imitation learning and GANs.

New method samples Jeffreys prior for objective Bayesian inference.

We extend the theory of asymmetric information in mispricing models for stocks following geometric Brownian motion to constant relative risk averse investors. Mispricing follows a continuous mean--reverting Ornstein--Uhlenbeck process. Optimal portfolios and maximum expected log--linear utilities from terminal wealth f…

The paper discusses the impact of prior densities on Bayesian model selection.

PARIS reduces imbalanced regression datasets by pruning uninformative samples.

New algorithm reduces online learning regret in uninformed Markov games.

Study automates feature selection and clustering for HFT stock price forecasting.

Letter analyzes training dynamics of a nonlinear contrastive learning model in high dimensions.

A technique called 'prior laundering' uses legacy reconstructions to create uncertainty in Bayesian inverse problems.

Paper introduces ENZ to measure significant coefficients in sparse recovery, improving over classical methods.

We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent feat…

MIM adds indicator variables to improve model performance on incomplete data.

This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…

A new approach simplifies Sliced-Wasserstein distances to improve learning performance.

The study examines when large trades are considered news or liquidity shocks in a market model.

Traditional market makers are losing their importance as automated systems have largely assumed the role of liquidity provision in markets. We update the model of Glosten and Milgrom (1985) to analyze this new world: we add multiple securities and introduce an automated market maker who uses the relationships between s…

We implement a market microstructure model including informed, uninformed and heuristic-driven investors, which latter behave in line with loss-aversion and mental accounting. We show that the probability of informed trading (PIN) varies significantly during 2008. In contrast, the probability of heuristic-driven tradin…

Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for…

This paper studies an optimal trading problem that incorporates the trader's market view on the terminal asset price distribution and uninformative noise embedded in the asset price dynamics. We model the underlying asset price evolution by an exponential randomized Brownian bridge (rBb) and consider various prior dist…

We consider trading against a hedge fund or large trader that must liquidate a large position in a risky asset if the market price of the asset crosses a certain threshold. Liquidation occurs in a disorderly manner and negatively impacts the market price of the asset. We consider the perspective of small investors whos…

99% of papers use real-world data, but only 3% provide formal comparisons.

Study examines insider trading with penalties, finding optimal penalties increase quickly for small orders.

The paper studies a stochastic majority vote approach to improve classifier accuracy.

Study examines insider trading in short-selling restricted markets.

We consider a continuous-time financial market that consists of securities available for dynamic trading, and securities only available for static trading. We work in a robust framework where a set of non-dominated models is given. The concept of semi-static completeness is introduced: it corresponds to having exact re…

Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact…

Visualizes deep neural networks for speech recognition using learned topographic filter maps.

Optimal dynamic fees for AMMs: A stochastic control approach