arXiv research

The paper explains why estimating a history-dependent policy can reduce MSE in reinforcement learning.

Marden's Tameness Conjecture predicts that every hyperbolic 3-manifold with finitely generated fundamental group is homeomorphic to the interior of a compact 3-manifold. It was recently established by Agol and Calegari-Gabai. We will survey the history of work on this conjecture and discuss its many applications.

Neural networks improve cancer risk prediction from family history data.

Combining deep learning and ensemble smoothers for better history matching.

Bayesian inference over admissible histories leads to irreversible kinetics.

HS-FNO models non-Markovian PDEs by learning history and future states.

Rhino learns causal relationships from time series data with history-dependent noise.

Overview of affine surface area and its history.

Recurrent networks learn beliefs from history in partially observable environments.

Survey of Baum-Connes conjecture history and methods.

Survey on DDVV-type inequalities, their history, and recent developments.

SRMC framework reduces Monte Carlo variance by history-based sampling in high-dimensional spaces.

Paper analyzes history-based RL methods for MDPs, introduces a theoretical framework and practical algorithm.

New algorithm minimizes regret in stochastic linear bandits with perturbed history.

It is shown how the generating functional method of De Dominicis can be used to solve the dynamics of the original version of the minority game (MG), in which agents observe real as opposed to fake market histories. Here one again finds exact closed equations for correlation and response functions, but now these are de…

A new method detects and removes false trailing balances in credit data.

This paper suggests claim history will be deprecated in future auto insurance rates.

We present in this chapter (Chapter II) the history of ideas which lead up to the development of modern knot theory. We are more detailed when pre-XX century history is reported. With more recent times we are more selective, stressing developments related to Jones type invariants of links. In the Appendix, A.Przybyszew…

Rewriting history improves RL algorithms for solving multiple tasks.

PHE adds pseudo-rewards to history to minimize regret in stochastic bandits.

A new model uses past consumption history to predict user preferences.

DR-FRL learns functional states from irregular histories for causal inference.

Paper tackles reinforcement learning for STL specifications with state history.

Memory-Augmented Recurrent Networks improve dialogue coherence by expanding conversation history storage.

Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consi…

Cold-start PV forecasting uses synthetic histories to train time-series foundation models.

We give an explicit definition of decentralization and show you that decentralization is almost impossible for the current stage and Bitcoin is the first truly noncentralized currency in the currency history. We propose a new framework of noncentralized cryptocurrency system with an assumption of the existence of a wea…

Geometric Brownian motion simulates stock prices for Brazilian small caps index.

This paper explores deep learning in music generation, from history to current techniques.

Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…

New framework handles dynamic contexts in reinforcement learning.

Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.

Foundation models improve wage gap decomposition by capturing omitted career history factors.

A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.

The essay discusses Margulis' theorems and their implications.

This paper argues for decolonizing AI alignment by incorporating open-source Hinduism concepts.

Network growth processes can be understood as generative models of the structure and history of complex networks. This point of view naturally leads to the problem of network archaeology: reconstructing all the past states of a network from its structure---a difficult permutation inference problem. In this paper, we in…

Deep learning calibrates CO2 storage formations from seismic and well data.

Study designs incentives for adapting multi-agent systems without knowing their learning dynamics.

Deep learning improves history matching of complex facies models.

A new method uses a frozen language model to improve sample efficiency in reinforcement learning.

HDT improves MCMC on graphs with history-dependent sampling.

New scheme adapts batch size for faster variance-reduced algorithms.

Derives FPDE for equity-linked insurance pricing.

In this paper, we solve a problem posed by Rodica Simion regarding type B Gram determinants. We present this in a fashion influenced by the work of W.B.R.Lickorish on Witten-Reshetikhin-Turaev invariants of 3-manifolds. The roots of the determinant were predicted by Dabkowski and Przytycki, and the complete factorizati…

This work tackles large action spaces in RL by binarizing actions.

New algorithm for truncated linear regression without knowing the survival set.

We propose a general framework to describe the impact of different events in the order book, that generalizes previous work on the impact of market orders. Two different modeling routes can be considered, which are equivalent when only market orders are taken into account. One model posits that each event type has a te…