arXiv research

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

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

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.

Study examines insider trading in short-selling restricted markets.

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.

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…

Active learning improves neutron spectroscopy experiments by automating measurement selection.

New method speeds up Bayesian inference for complex simulators.

In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function. We show that GANs without restriction on the discriminative function space commonly suffer from the problem that the gradient produced by …

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…

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

Reconstruction-based learning produces uninformative features for perception tasks.

New optimal prior avoids bias in complex models with limited data.

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

DWTS uses observational data to improve clinical trial efficiency.

Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting. A natural idea is to visualize the deep network's representations, so as to "see what the network sees". In this paper, we demonstrate that standard dimension reduction m…

Neural networks compress uninformative input directions, improving test error.

New analysis shows over-squashing limits GNNs' power.

The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research. We present an evaluation metric that can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric…

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

Algorithm recovers factors of rank-1 matrices from noisy measurements.

This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare. Micro average, macro average, a…

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…

Method identifies variable matches from data values.

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

Exploratory data analysis is crucial for developing and understanding classification models from high-dimensional datasets. We explore the utility of a new unsupervised tree ensemble called uncharted forest for visualizing class associations, sample-sample associations, class heterogeneity, and uninformative classes fo…

PARIS reduces imbalanced regression datasets by pruning uninformative samples.

New algorithm reduces online learning regret in uninformed Markov games.

An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment. Such interactive d…

Combines BO with context to optimize binary feedback.

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…

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

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

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…

Paper identifies problematic baselines in Shapley value explanations and proposes a reweighting mechanism.

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

New method selects causal features from diverse data types.

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…

Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Data Sources: Papers published in the Neural Information Processing Systems (NeurIPS, née NIPS) journal where the official record showed a 2017…

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…