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.
Fast algorithms developed for adaptive and fully adaptive submodular maximization problems.
This paper improves test-time adaptation for distribution shifts using confidence maximization and input transformation.
New algorithm maximizes non-monotone adaptive submodular functions in linear time.
We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adapt…
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called \textit{Type II maximum likelihood} or ML-II). An alternative learning proced…
The paper explores fully affine maximal curves and their properties.
Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy that plans for an action to take, from a finite set of actions, given some partial observations. It has been shown that in many applicat…
New algorithm for maximizing submodular functions in real-time data changes.
Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff between cheap but simple variational families (e.g.~fully factorized) or expensive and complicated inference procedures. We show that natural gradient ascent with adaptive w…
Deep learning solves non-Markovian FBSDEs for utility maximization.
We propose a new image denoising algorithm, dubbed as Fully Convolutional Adaptive Image DEnoiser (FC-AIDE), that can learn from an offline supervised training set with a fully convolutional neural network as well as adaptively fine-tune the supervised model for each given noisy image. We significantly extend the frame…
New guarantees for adaptive combinatorial maximization with various objectives.
Paper tackles test-time adaptation for tabular data.
Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution over policies in a Bayesian deep reinforcement learning setup to propose a transfer strategy. Recent works have shown to induce diversity i…
Efficiently selects seed nodes to maximize content influence in unknown social networks.
Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.
Adaptive cascade submodular maximization tackles sequential selection under uncertainty.
Random matrix theory explains how neural networks adapt to data.
Tent adapts models during testing by minimizing entropy of predictions.
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully …
New filters match advanced composition for adaptive privacy, with practical constants.
We consider the optimal solutions to the trade execution problem in the two different classes of i) fully adapted or adaptive and ii) deterministic or static strategies, comparing them. We do this in two different benchmark models. The first model is a discrete time framework with an information flow process, dealing w…
Bayesian adaptive PCE method improves surrogate modeling and sensitivity analysis.
Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that entropy minimization only may result into collapsed trivial solutions. In this paper, we propose to avoid trivial solutions by further introducing diversity maximization. In order to achieve the possib…
A decentralized approach for multi-source domain adaptation.
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parameter linearly. While existing methods partially or entirely fix sequences of arm selections before observing rewards, our method adaptively c…
New method separates objects from images using deep neural networks trained to inpaint.
Paper analyzes convergence rates for multi-agent learning in games.
A characterization of maximal domains of existence of adapted complex structures for Riemannian homogeneous manifolds under certain extensibility assumptions on their geodesic flow is given. This is applied to generalized Heisenberg groups and naturally reductive Riemannian homogeneous spaces. As an application it is s…
We provide a verification and characterization result of optimal maximal sub-solutions of BSDEs in terms of fully coupled forward backward stochastic differential equations. We illustrate the application thereof in utility optimization with random endowment under probability and discounting uncertainty. We show with ex…
Localizes curvature estimates for evolving hypersurfaces under various flows.
Adaptive learning method identifies and corrects corrupted data.
A new algorithm trains experts to safely guide agents in partially observed environments.
We introduce the {\it diffusion -means} clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion -means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as …
MaxVA improves Adam's step sizes by maximizing gradient variance.
Self-adaptive PINNs improve accuracy in stiff PDEs.
We prove that for closed surfaces with Riemannian metrics without conjugate points and genus the geodesic flow on the unit tangent bundle has a unique measure of maximal entropy. Furthermore, this measure is fully supported on and the flow is mixing with respect to this measure. We formulate …
We study an optimal portfolio problem designed for an agent operating in intraday electricity markets. The investor is allowed to trade in a single risky asset modelling the continuously traded power and aims to maximize the expected terminal utility of his wealth. We assume a mean-reverting additive process to drive t…
Alpha-trimming prunes trees in random forests to improve predictive performance.
Algorithm learns interference network and optimizes treatment allocation for unknown network effects.
Meta-learning for discrete tasks using submodular optimization.
The paper solves a complex financial optimization problem using a novel mathematical technique.
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions …
Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers. We adapt this approach to the convolutional setting.
Subspace learning and matrix factorization problems have great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow. Many relevant problem formulations are non-convex, and in a variety of contexts it has been observed that solving the non-convex problem d…
This paper examines number theoretic and topological properties of fully augmented pretzel link complements. In particular, we determine exactly when these link complements are arithmetic and exactly which are commensurable with one another. We show these link complements realize infinitely many CM-fields as invariant …
The paper tackles adaptive policy selection to maximize social welfare, achieving optimal regret bounds.
Given a mixture between two populations of coins, "positive" coins that each have -- unknown and potentially different -- bias and "negative" coins with bias , we consider the task of estimating the fraction of positive coins to within additive error . We achieve an upper a…