arXiv research

Unified framework for drawdown risk computation under Markov models.

Unified theory for UCB policies in total and max bandit problems.

The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.

Algorithm selects optimal experiments in Markov chains to learn unknown quantities.

Differentiable algorithm for time-series changepoint inference.

New bounds on query learning complexity for various concept classes.

Study finds conserved quantities for two types of curves on conformal sphere.

Ecker's and Huisken's quantities agree for ancient mean curvature flows.

Enhanced framework selects features for unbiased causal inference.

New algorithm efficiently trains machine learning models to atomic forces data.

BackPACK extends PyTorch to compute additional gradient info.

We formalize and verify double auctions for multiple-quantity trades.

The paper extends Noether's theorem to contact systems, finding dissipated quantities instead of conserved ones.

In this paper, we consider two different monotone quantities defined for the Ricci flow and show that their asymptotic limits coincide for any ancient solutions. One of the quantities we consider here is Perelman's reduced volume, while the other is the local quantity discovered by Ecker, Knopf, Ni and Topping. This es…

Given a vector field on a manifold M, we define a globally conserved quantity to be a differential form whose Lie derivative is exact. Integrals of conserved quantities over suitable submanifolds are constant under time evolution, the Kelvin circulation theorem being a well-known special case. More generally, conserved…

The energy in a square membrane $Ω$ subject to constant viscous damping on a subset $ω\subset Ω$ decays exponentially in time as soon as $ω$ satisfies a geometrical condition known as the "Bardos-Lebeau-Rauch" condition. The rate $τ(ω)$ of this decay satisfies $τ(ω)= 2 \min(-μ(ω), g(ω))$ (see Lebeau [Math. Phys. Stud. …

Derive monotone quantities for harmonic functions on asymptotically flat 3-manifolds with nonnegative scalar curvature.

New algorithm finds nearest neighbors with less computation.

The paper introduces a method to incorporate expert opinion on observable quantities into statistical models.

New algorithms for interactive learning match minimax bounds efficiently.

Study linearized Schwarzschild spacetimes, proving decay of master quantities.

The study finds resonance points in polarised curves with polynomial conserved quantities.

New algorithms minimize regret with global costs in online learning.

We show that prices and shortfall risks of game (Israeli) barrier options in a sequence of binomial approximations of the Black--Scholes (BS) market converge to the corresponding quantities for similar game barrier options in the BS market with path dependent payoffs and the speed of convergence is estimated, as well. …

WP-SGD optimizes SGD for unevenly distributed data in distributed systems.

We introduce a new and improved characterization of the label complexity of disagreement-based active learning, in which the leading quantity is the version space compression set size. This quantity is defined as the size of the smallest subset of the training data that induces the same version space. We show various a…

Bond rating Transition Probability Matrices (TPMs) are built over a one-year time-frame and for many practical purposes, like the assessment of risk in portfolios or the computation of banking Capital Requirements (e.g. the new IFRS 9 regulation), one needs to compute the TPM and probabilities of default over a smaller…

Derives monotonic quantities for $p$-harmonic functions on manifolds.

This work connects symmetries and conserved quantities in machine learning.

Study almost rigidity of super Ricci flow with non-negative Muller quantity.

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…

New method uses SVD entropy to price artworks.

Two algorithms tackle heavy-tailed rewards in reinforcement learning with linear function approximation.

We construct a class of monotonic quantities along the normalized Ricci flow on closed n-dimensional manifolds.

New definitions of conserved quantities at null infinity resolve ambiguities in general relativity.

The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates th…

Derives fluctuation-dissipation relations for SGD, linking hyperparameters and measurable quantities.

Model for hedging price and quantity risks in electricity markets.

The paper introduces a new method to characterize cosmological models using observer-based invariants.

New quantity helps map homotopy classes in complex spaces.

Bayesian optimization outperforms other methods in hyperparameter tuning for reinforcement learning.

Event2vec learns object embeddings in HINs by considering both relation properties and quantities.

This paper introduces a method for efficiently inferring a high-dimensional distributed quantity from a few observations. The quantity of interest (QoI) is approximated in a basis (dictionary) learned from a training set. The coefficients associated with the approximation of the QoI in the basis are determined by minim…

We offer an algorithmic approach for determining Harnack quantities for the curve shortening flow and we show how, following this procedure, one can obtain Hamilton's Harnack inequality for this flow $κ_t + \frac{1}{2t}κ\geq \frac{κ_s^2}κ$, where $κ$ is the curvature of the curve being deformed by the flow.

New geometric quantities help classify manifolds and relate to entropy.

New inequalities for convex curves with multiple geometric factors.

Paper tackles online learning with interval regret, achieving adaptive bounds.

Optimistic algorithms achieve logarithmic regret bounds for MDPs without diameter dependence.