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 presents a method to estimate joint interventional distributions from marginal interventional data.
New causal versions of MaxEnt and PIR avoid paradoxical probability updates.
In this paper, we propose a novel maximum causal Tsallis entropy (MCTE) framework for imitation learning which can efficiently learn a sparse multi-modal policy distribution from demonstrations. We provide the full mathematical analysis of the proposed framework. First, the optimal solution of an MCTE problem is shown …
We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL). Motivated by a limitation of the classical maximum entropy model in capturing the structure of the network of states, we propose an IRL model based on a generalized version of the causal entropy maximization pr…
New method uses entropy to generate multiple plausible causal maps.
New method identifies common cause in causal insufficiency, revealing complex phase transitions.
New algorithm reduces performance loss in IRL with mismatched transition dynamics.
Proposes PEID for analyzing synergistic causation in complex systems.
New method tightens bounds on causation probabilities using independent datasets.
In many settings (e.g., robotics) demonstrations provide a natural way to specify tasks; however, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the tasks, such as rewards or policies, can be safely composed and/or do not explicitly capture history dependen…
This work connects IRL methods from ML and economics.
New measures for causal entropy and information gain studied.
Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computation…
The MAXENT principle helps merge datasets to infer causal effects.
Study on merging predictors in causal and anticausal directions using CMAXENT.
Two hitherto disconnected threads of research, diverse exploration (DE) and maximum entropy RL have addressed a wide range of problems facing reinforcement learning algorithms via ostensibly distinct mechanisms. In this work, we identify a connection between these two approaches. First, a discriminator-based diversity …
Paper develops MRCs for supervised classification using generalized maximum entropy.
Paper proves causal direction can be inferred from data with limited randomness.
Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired ma…
New algorithm improves causal effect estimation for continuous treatments.
The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family, has been very popular in machine learning due to its "Occam's razor" interpretation. Unfortunately, calculating the potentials in the maximum-entro…
We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is sim…
Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
The study learns causal graphs from time series data using entropy measures.
Proposes efficient bounds for causal effect estimation under weak confounding.
MGD combines maximum entropy and diffusion methods for efficient sampling.
Researchers use Gaussian processes to approximate Lagrange multipliers for Maximum-Entropy distributions.
New causal measures improve feature selection in AI models.
Enhances RL by controlling policy stochasticity through trajectory entropy constraints.
Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a…
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes maximum entropy principle and from standard maximum likelihood estimation.We demonstrate the LME principle BY deriving …
Causal discovery is a fundamental problem in statistics and has wide applications in different fields. Transfer Entropy (TE) is a important notion defined for measuring causality, which is essentially conditional Mutual Information (MI). Copula Entropy (CE) is a theory on measurement of statistical independence and is …
This work presents entropic constraints from DAGs with hidden variables.
The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such appr…
We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent. The minimum entropy required for such a latent is known as common entropy in information theory. We extend this notion to Renyi common entropy by minimizing the Renyi entropy of the …
We discuss the systemic risk implied by the interbank exposures reconstructed with the maximum entropy method. The maximum entropy method severely underestimates the risk of interbank contagion by assuming a fully connected network, while in reality the structure of the interbank network is sparsely connected. Here, we…
Bayesian models use hyperparameters to indirectly assign priors, and this work shows how these priors can be derived from maximum entropy principles.
Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts, and scientists from many other fields. This paper takes the perspect…
The maximum entropy principle can be used to assign utility values when only partial information is available about the decision maker's preferences. In order to obtain such utility values it is necessary to establish an analogy between probability and utility through the notion of a utility density function. According…
The paper extends entropy maximization to multiscale settings and applies it to neural networks.
A novel framework infers causal direction from symbolic sequences using pattern entropy.
Improved exploration methods for reinforcement learning with reduced sample complexity.
IntDC framework uncovers causal relationships from non-interventional data.
Calculates local Granger causality for Gaussian and nonlinear systems.
Paper proposes a policy-search algorithm to learn entropy-maximizing exploration policies in reward-free environments.
New method calibrates reference distributions for bounded support.
The paper introduces a new intrinsic reward method for exploration in reinforcement learning.
We study the problem of identifying the causal relationship between two discrete random variables from observational data. We recently proposed a novel framework called entropic causality that works in a very general functional model but makes the assumption that the unobserved exogenous variable has small entropy in t…