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.
RFX-Fuse combines Breiman and Cutler's Random Forest with modern ML capabilities.
In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic sol…
RFX accelerates and compresses Random Forests for large datasets.
Improved random forest proximities capture data geometry.
New method for efficient proximal mapping of 1-path-norm in shallow networks.
SMP model preserves proximity and permutation in graph neural networks.
Unified framework for training neural networks with non-smooth, non-convex regularizers.
The need for parameter estimation with massive datasets has reinvigorated interest in stochastic optimization and iterative estimation procedures. Stochastic approximations are at the forefront of this recent development as they yield procedures that are simple, general, and fast. However, standard stochastic approxima…
Wasserstein distance plays increasingly important roles in machine learning, stochastic programming and image processing. Major efforts have been under way to address its high computational complexity, some leading to approximate or regularized variations such as Sinkhorn distance. However, as we will demonstrate, regu…
Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estim…
Proposes a new method for localized uncertainty quantification in random forests using proximity measures.
Curvature regularization prevents distortion in graph embeddings.
Many scientific and engineering applications feature nonsmooth convex minimization problems over convex sets. In this paper, we address an important instance of this broad class where we assume that the nonsmooth objective is equipped with a tractable proximity operator and that the convex constraint set affords a self…
Proximal gradient method has been playing an important role to solve many machine learning tasks, especially for the nonsmooth problems. However, in some machine learning problems such as the bandit model and the black-box learning problem, proximal gradient method could fail because the explicit gradients of these pro…
AGS-CL selectively updates penalties based on node importance for continual learning.
In this paper, we propose a new algorithm to speed-up the convergence of accelerated proximal gradient (APG) methods. In order to minimize a convex function , our algorithm introduces a simple line search step after each proximal gradient step in APG so that a biconvex function is minimi…
Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s), and these proximity matrices are induced kernels. While there has been extensive research on the applications and properties of kernels, …
Optimization is at the heart of machine learning, statistics and many applied scientific disciplines. It also has a long history in physics, ranging from the minimal action principle to finding ground states of disordered systems such as spin glasses. Proximal algorithms form a class of methods that are broadly applica…
The study combines social interaction data into a single network, identifying stable groups of chimpanzees.
Consider the stochastic composition optimization problem where the objective is a composition of two expected-value functions. We propose a new stochastic first-order method, namely the accelerated stochastic compositional proximal gradient (ASC-PG) method, which updates based on queries to the sampling oracle using tw…
Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization pro…
Study shows code-level optimizations significantly impact deep RL algorithms.
New algorithms accelerate model-based optimization for stochastic problems.
New method detects close contacts to prevent SARS-CoV-2 spread.
Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions. While there is a large variety of algorithms available to solve convex problems, we can argue that it becomes more and more important to focus on efficient, …
Improved shuffling gradient methods converge faster for nonsmooth convex optimization.
The classical multi-set split feasibility problem seeks a point in the intersection of finitely many closed convex domain constraints, whose image under a linear mapping also lies in the intersection of finitely many closed convex range constraints. Split feasibility generalizes important inverse problems including con…
ProxSPS improves on SPS for regularization tasks, offering better stability and performance.
Paper proposes a new combined regression strategy for conditional survival prediction.
Improves time series classification with forest proximities.
Improved sampling guarantees for weakly log-concave distributions.
Modern proximal and stochastic gradient descent (SGD) methods are believed to efficiently minimize large composite objective functions, but such methods have two algorithmic challenges: (1) a lack of fast or justified stop conditions, and (2) sensitivity to the objective function's conditioning. In response to the firs…
We propose Trusted Neural Network (TNN) models, which are deep neural network models that satisfy safety constraints critical to the application domain. We investigate different mechanisms for incorporating rule-based knowledge in the form of first-order logic constraints into a TNN model, where rules that encode safet…
Very recently proximal policy optimization (PPO) algorithms have been proposed as first-order optimization methods for effective reinforcement learning. While PPO is inspired by the same learning theory that justifies trust region policy optimization (TRPO), PPO substantially simplifies algorithm design and improves da…
CFR-Pro enhances treatment effect estimation by incorporating local proximity.
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challengin…
Introduces PPMM algorithm for nonconvex robust regression problems.
In this paper, we focus on solving an important class of nonconvex optimization problems which includes many problems for example signal processing over a networked multi-agent system and distributed learning over networks. Motivated by many applications in which the local objective function is the sum of smooth but po…
Paper analyzes convergence of proximal algorithm in metric spaces without geodesic convexity.
Extends RF proximities to all supervised distance-based machine learning contexts.
The paper tackles counterfactual learning for stochastic policies with continuous actions.
Improved bounds for proximal gradient algorithms with computational errors.
Paper extends theorem on covering spaces and Jordan curves.
Proximal algorithms applied to current deformation into cycles.
Innovative method solves nonconvex optimization on manifolds.
New PnP algorithm converges with relaxed proximal gradient descent.
DLC enhances distillation-based continual learning with lightweight plugins.
EPINE enhances network embedding by improving adjacency matrix-based high-order proximity.