A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper analyzes regret in bilateral trade mechanisms without prior valuations.
Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transp…
Improved GRU model with weighted time-delay feedback for long-term dependencies.
Proposes RPG-RT for red-teaming T2I models without internal access.
According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint dynamics of stock price,…
GPT-4 assesses its confidence in answering USMLE questions with and without feedback.
ELF improves FM forecasts by efficiently using online feedback.
Mechanisms for fair resource allocation learn user preferences online.
It is now well established empirically that financial price changes are distributed according to a power law, with cubic exponent. This is a fascinating regularity, as it holds for various classes of securities, on various markets, and on various time scales. The universality of this law suggests that there must be som…
Generative adversarial nets (GAN) has been successfully introduced for generating text to alleviate the exposure bias. However, discriminators in these models only evaluate the entire sequence, which causes feedback sparsity and mode collapse. To tackle these problems, we propose a novel mechanism. It first segments th…
New method tackles composite optimization with error feedback.
This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…
Study improves understanding and performance of FA learning rules in neural networks.
Although data may be abundant, complete data is less so, due to missing columns or rows. This missingness undermines the performance of downstream data products that either omit incomplete cases or create derived completed data for subsequent processing. Appropriately managing missing data is required in order to fully…
IAL uses interactive learning to improve model performance with minimal human feedback.
Mechanism designs for unknown agent values in stochastic bandit settings.
A new method uses counterfactual learning to improve recommendation system evaluation.
CNN-F uses generative feedback to improve neural networks' robustness to perturbations.
Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ra…
Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes the…
Paper eliminates warm-up phase for PO in linear MDPs, achieving optimal regret.
This paper presents a description of the mechanical operations of banking as used in modern banking systems regulated under the Basel Accords, in order to provide support for a verifiable and complete description of the banking system suitable for computer simulation. Feedback is requested on the contents of this docum…
Self-reinforcing feedback loops in personalization systems are typically caused by users choosing from a limited set of alternatives presented systematically based on previous choices. We propose a Bayesian choice model built on Luce axioms that explicitly accounts for users' limited exposure to alternatives. Our model…
Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class of feedback, filtered semibandit feedback. In a CMAB problem an agent pulls a combination of arms from a set in each round, g…
Much research has been conducted arguing that tipping points at which complex systems experience phase transitions are difficult to identify. To test the existence of tipping points in financial markets, based on the alternating offer strategic model we propose a network of bargaining agents who mutually either coopera…
New framework PBBO optimizes latent functions with preferential feedback.
Improved error feedback method reduces communication complexity in distributed training.
We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse system to a much smaller system with many-body and dense interactions with an effective energy on every configuration of the FVS, then learn…
The goal and the main result of the paper is to provide a complete description of the field of rational differential invariants of one class of second order ordinary differential equations with scalar control parameter with respect to Lie pseudo-group of local feedback transformations. In particular, considered class d…
Efficient algorithm for learning from indirect feedback in complex decision-making scenarios.
Variational Proximal Policy Optimization improves reinforcement learning from human feedback.
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
The paper offers a checklist for comparing human and machine visual perception.
Infrastructure monitors AI/ML radiology models across multiple sites.
Empirical data reveals that the liquidity flow into the order book (depositions, cancellations andmarket orders) is influenced by past price changes. In particular, we show that liquidity tends todecrease with the amplitude of past volatility and price trends. Such a feedback mechanism inturn increases the volatility, …
DEFINED uses decision feedback ICL to detect symbols with minimal pilot data.
EF21 improves convergence in distributed machine learning models.
We attempt to unveil the fine structure of volatility feedback effects in the context of general quadratic autoregressive (QARCH) models, which assume that today's volatility can be expressed as a general quadratic form of the past daily returns. The standard ARCH or GARCH framework is recovered when the quadratic kern…
This work improves generative models by using feedback from multiple dependent models.
Recently, matrix factorization-based recommendation methods have been criticized for the problem raised by the triangle inequality violation. Although several metric learning-based approaches have been proposed to overcome this issue, existing approaches typically project each user to a single point in the metric space…
Modern online platforms rely on effective rating systems to learn about items. We consider the optimal design of rating systems that collect binary feedback after transactions. We make three contributions. First, we formalize the performance of a rating system as the speed with which it recovers the true underlying ran…
Framework learns robust control policies from expert demonstrations.
Clip21 improves convergence of gradient-clipped methods in DP settings.
New method learns interpretable concepts from user feedback for high-dimensional data.
Generative model solves financial market equilibria with stable reinforcement learning.
New graph types help identify complex relationships.
AutoStan improves Bayesian models via predictive feedback.
Enhances AI models with human feedback for noisy data.