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.
New RL algorithms learn QSE from strategic feedbacks with sample efficiency.
QSD enhances deep network performance through biologically plausible dropout.
New framework recovers reward and rationality parameters from game behavior.
Unified framework for estimating reward functions in competitive games.
Model captures decision-making under bounded rationality with prior beliefs and market feedback.
Experimental economics has repeatedly demonstrated that the Nash equilibrium makes inaccurate predictions for a vast set of games. Instead, several alternative theoretical concepts predict behavior that is much more in tune with observed data, with the quantal response equilibrium as the most prominent example. However…
Study compares cryptocurrency and stock markets using statistical equilibrium models.
Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents. This paper deals with the relatively under-explored but equally important "inverse" setting, where the param…
The dual crises of the sub-prime mortgage crisis and the global financial crisis has prompted a call for explanations of non-equilibrium market dynamics. Recently a promising approach has been the use of agent based models (ABMs) to simulate aggregate market dynamics. A key aspect of these models is the endogenous emer…
The Berezin quantization on a simply connected homogeneous Kähler manifold, which is considered as a phase space for a dynamical system, enables a description of the quantal system in a (finite-dimensional) Hilbert space of holomorphic functions corresponding to generalized coherent states. The Lie algebra associated w…
There are non-vanishing price responses across different stocks in correlated financial markets. We further study this issue by performing different averages, which identify active and passive cross-responses. The two average cross-responses show different characteristic dependences on the time lag. The passive cross-r…
Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that all…
Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be tr…
Study evaluates different price response definitions for NASDAQ stocks.
Interpretable text-response modelling for structured outcomes
Previous studies of the stock price response to trades focused on the dynamics of single stocks, i.e. they addressed the self-response. We empirically investigate the price response of one stock to the trades of other stocks in a correlated market, i.e. the cross-responses. How large is the impact of one stock on other…
LaRT models LLMs' response accuracy and CoT length to evaluate reasoning ability and speed.
New method handles correlated responses and interaction effects in multi-response regression.
Study analyzes price response and spread impact in foreign exchange markets.
PEER tackles multi-response regression with incomplete outcomes efficiently.
New method for explaining dialogue response generation models.
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality…
Enhances traditional MV model for socially responsible investors.
In this study we present a kernel based convolution model to characterize neural responses to natural sounds by decoding their time-varying acoustic features. The model allows to decode natural sounds from high-dimensional neural recordings, such as magnetoencephalography (MEG), that track timing and location of human …
Active learning suffers from biased non-response, which this paper addresses.
The paper proposes a new method to evaluate LLM agent responses using ECDF clustering.
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can no…
New framework for managing medical risks using convex responses.
Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performanc…
The paper provides risk bounds for learning many response functions using linear regression.
Study optimizes classifiers for credit card mail campaigns and default prediction.
We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given response surfaces over a continuous input space , the aim is to efficiently find the index of the minimal response across the entire . The response surfaces are not known and ha…
ESRLCM clusters similar responses, more broadly than traditional models.
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially va…
DFNNs predict non-Euclidean responses from Euclidean predictors.
Proposes a method for generating prediction intervals in dose-response models using conformal prediction.
New method predicts nonfactuality in LLM responses using semantic isotropy.
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…
Study analyzes non-Markovian effects in financial markets over multiple years.
A new model estimates mixed memberships for categorical data with weighted responses.
How to generate human like response is one of the most challenging tasks for artificial intelligence. In a real application, after reading the same post different people might write responses with positive or negative sentiment according to their own experiences and attitudes. To simulate this procedure, we propose a s…
A new model for latent class analysis with weighted responses.
Enhances preference learning by incorporating response times into binary choices.
Proposes active learning for meta-learning in graph node response prediction.
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are ei…
The paper enhances preference learning by incorporating response time data.
Private machine learning framework using randomised response.