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.
We develop an off-policy actor-critic algorithm for learning an optimal policy from a training set composed of data from multiple individuals. This algorithm is developed with a view towards its use in mobile health.
We develop efficient algorithms for robust PCA that handle outliers.
FedML aims to improve FL research by providing a library and benchmark.
This thesis advances algorithms and software for QMC, GP, and sciML.
Develops an actor-critic algorithm for risk-sensitive Markov decision processes.
Recently there have been exciting developments in Monte Carlo methods, with the development of new MCMC and sequential Monte Carlo (SMC) algorithms which are based on continuous-time, rather than discrete-time, Markov processes. This has led to some fundamentally new Monte Carlo algorithms which can be used to sample f…
The precise diagnosis is of great significance in developing precise treatment plans to restore neck function and reduce the burden posed by the cervical spondylosis (CS). However, the current available neck function assessment method are subjective and coarse-grained. In this paper, based on the relationship among CS,…
Developed a new algorithm to improve dynamic treatment regimens.
Algorithm computes knot Floer complex for knots of thickness one.
Develops parameter-free online mirror descent for optimal dynamic regret.
The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this work, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formu…
In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. In particular, I aim to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific cova…
Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment. The benchmark is designed to encourage the development of learning algorithms that are sa…
Develops a high-dimensional differentially-private EM algorithm with near-optimal statistical guarantees.
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contribu…
Developed an efficient iterative algorithm for SVI model.
Develops a new approach for algorithmic recourse in AI systems.
Paper develops federated GLMM algorithms for analyzing hierarchical data.
Paper develops algorithms to maximize AUC in imbalanced classification.
Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually the default kernel in KAF algorithms, but selecting the proper kernel size (bandwidth) is still an open important issue especially for learning with small sample s…
With the increasing power of computers and the rapid development of self-learning methodologies such as machine learning and artificial intelligence, the problem of constructing an automatic Financial Trading Systems (FTFs) becomes an increasingly attractive research topic. An intuitive way of developing such a trading…
Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received …
Julia accelerates machine learning in various fields with balance of efficiency and simplicity.
Paper develops momentum schemes with variance reduction for non-convex composition optimization.
PHOTONAI simplifies machine learning model development in Python.
New ML algorithms improve model interpretability without sacrificing performance.
Developed a simulation method for 3/2 stochastic volatility model.
Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dos…
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
New algorithms reduce communication costs in collaborative learning.
Develops a new RL algorithm for medical treatment regimes.
SGBD algorithm improves robustness in Bayesian sampling.
The paper develops ML algorithms for calibrating credit rating transition models for high and low default portfolios.
Optimal transport (OT) distances are finding evermore applications in machine learning and computer vision, but their wide spread use in larger-scale problems is impeded by their high computational cost. In this work we develop a family of fast and practical stochastic algorithms for solving the optimal transport probl…
Develops an algorithm for bilevel optimization with coupled constraints.
Paper presents a faster classical algorithm for principal component regression.
Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and …
Develops a privacy-preserving algorithm for sparse robust regression.
Develops first optimal algorithm for logistic bandits.
Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB me…
Improved algorithm for modular links provides upper volume bounds.
CIfly simplifies causal inference tasks with linear-time reachability primitives.
Develops framework to analyze pruning of neural networks.
This work develops secure distributed algorithms for machine learning to protect against data poisoning and network attacks.
Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of diff…
Boosted decision trees enjoy popularity in a variety of applications; however, for large-scale datasets, the cost of training a decision tree in each round can be prohibitively expensive. Inspired by ideas from the multi-arm bandit literature, we develop a highly efficient algorithm for computing exact greedy-optimal d…
Develops a two-stage approach for robust tensor completion of visual data.