arXiv research

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

Novel PCA method for high-dimensional inverse problems.

Paper proposes a policy gradient method for confounded POMDPs.

Gradient boosting enhances existing Mendelian models for genetic disease risk prediction.

Adaptive online learning algorithm improves history forgetting in nonstationary environments.

AutoClip automatically adjusts gradient clipping for better audio separation.

Bayesian inference over admissible histories leads to irreversible kinetics.

We propose a variance reduction framework for variational inference using the Multilevel Monte Carlo (MLMC) method. Our framework is built on reparameterized gradient estimators and "recycles" parameters obtained from past update history in optimization. In addition, our framework provides a new optimization algorithm …

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

Overview of affine surface area and its history.

The paper interprets policy-gradient algorithms using continuation theory.

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

We consider a composite convex minimization problem associated with regularized empirical risk minimization, which often arises in machine learning. We propose two new stochastic gradient methods that are based on stochastic dual averaging method with variance reduction. Our methods generate a sparser solution than the…

We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxiliary variable construction coupled with an inverse Hessian approximation computed using a receding history of iterates and gradients. It is th…

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.

In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by ex…

Neural networks improve cancer risk prediction from family history data.

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

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…

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…

Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We show that for simple overparameterized problems, adaptive methods often find drasti…

Paper tackles reinforcement learning for STL specifications with state history.

We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds $O(t)$ i.i.d. pseudo-rewards to its history in round $t$ and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). Th…

Federated Learning improves Firefox URL bar suggestions.

L2O-CFGD meta-learns hyperparameters for FGD, improving performance.

OptEx accelerates first-order optimization with parallelized iterations.

A new model integrates claim history and covariates for non-life insurance reserving.

We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model trained on its perturbed history. Therefore, we call it perturbed-history exploration in a linear bandit (LinPHE). The perturbed history is …

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…

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

We consider the fundamental problem in non-convex optimization of efficiently reaching a stationary point. In contrast to the convex case, in the long history of this basic problem, the only known theoretical results on first-order non-convex optimization remain to be full gradient descent that converges in $O(1/\varep…

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…

Optimal preconditioning improves Langevin sampling efficiency.

Combining deep learning and ensemble smoothers for better history matching.

New framework handles dynamic contexts in reinforcement learning.

A new Bayesian filtering method speeds up stochastic Newton optimization.

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.

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.

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.

Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit for achieving best performance in grid search by selecting an appropriate batch s…

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…

Gradient boosting detects insider purchases predicting abnormal returns in microcap stocks.

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

Adaptive financial dataflow system improves model robustness in dynamic markets.

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