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.
New algorithms estimate and test collision probability with near-optimal sample complexity.
Simplifies machine learning validation using kNN and conditional probability algorithms.
Study on optimal rates for sequential probability assignment using smoothed analysis.
Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabil…
OPAA estimates probability densities using functional analysis.
We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability of a subnetwork, e.g., a single edge, in O(n2n) time and the posterior probabilities for all n(n-1) potential edges in O(n2n…
New algorithms minimize MMD to approximate probability measures efficiently.
Paper recovers top-two answers and confusion probability in multi-choice crowdsourcing.
We study the problem of identifying a probability distribution for some given randomly sampled data in the limit, in the context of algorithmic learning theory as proposed recently by Vinanyi and Chater. We show that there exists a computable partial learner for the computable probability measures, while by Bienvenu, M…
New MCMC method for complex models with large variables.
New algorithm FLUTE achieves uniform-PAC convergence in RL with linear approx.
We provide an elementary proof of a simple, efficient algorithm for computing the Euclidean projection of a point onto the probability simplex. We also show an application in Laplacian K-modes clustering.
We provide theoretical complexity analysis for new algorithms to compute the optimal transport (OT) distance between two discrete probability distributions, and demonstrate their favorable practical performance over state-of-art primal-dual algorithms and their capability in solving other problems in large-scale, such …
An algorithm finds the most probable best solution in uncertain parameter settings.
We consider the problem of selecting a seed set to maximize the expected number of influenced nodes in the social network, referred to as the \textit{influence maximization} (IM) problem. We assume that the topology of the social network is prescribed while the influence probabilities among edges are unknown. In order …
New algorithms achieve high-probability parameter-free regret in online convex optimization with heavy-tailed data.
A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probability estimation trees (Bagged-PETs) and also presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using several benchmark …
New algorithm reduces high-probability regret for time-varying feedback graphs.
We develop importance sampling based efficient simulation techniques for three commonly encountered rare event probabilities associated with random walks having i.i.d. regularly varying increments; namely, 1) the large deviation probabilities, 2) the level crossing probabilities, and 3) the level crossing probabilities…
This paper presents a methodology and numerical algorithms for constructing accelerated gradient flows on the space of probability distributions. In particular, we extend the recent variational formulation of accelerated gradient methods in (wibisono, et. al. 2016) from vector valued variables to probability distributi…
Master algorithm selects best contextual bandit from a collection.
We introduce Fisher consistency in the sense of unbiasedness as a desirable property for estimators of class prior probabilities. Lack of Fisher consistency could be used as a criterion to dismiss estimators that are unlikely to deliver precise estimates in test datasets under prior probability and more general dataset…
A new method for adapting to label shifts using class probability matching.
This paper studies geometrical structure of the manifold of escort probability distributions and shows its new applicability to information science. In order to realize escort probabilities we use a conformal transformation that flattens so-called alpha-geometry of the space of discrete probability distributions, which…
Algorithm removes leaves to find root in uniform trees.
Paper introduces Generalized Naive Bayes for better data fitting.
Paper simplifies calculating causation probabilities and ranks root causes.
Paper presents efficient algorithms for reconstructing noisy pooled data.
This paper examines how different decoding algorithms for LLMs align with various goals.
The paper proposes a new method to approximate Wasserstein-Fisher-Rao flows using Monte Carlo techniques.
Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Stochastic acceptors or probabilistic automata are stochastic automata without output that can model components in machine learning scenarios. In this paper, we provide dynamic programming algorithms for the comput…
Refined analysis of Mitra's algorithm for discrete mixtures.
Algorithm finds safe zones in policy Markov Decision Processes to limit trajectory escape.
AdaBoost cycles in probability simplex dynamics.
We investigate Monte Carlo based algorithms for solving stochastic control problems with probabilistic constraints. Our motivation comes from microgrid management, where the controller tries to optimally dispatch a diesel generator while maintaining low probability of blackouts. The key question we investigate are empi…
The article introduces inferential moments for analyzing uncertain multivariable systems.
Paper analyzes high probability convergence of adaptive SGD with momentum.
This paper provides a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement l…
This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing ma…
New method optimizes non-linear functionals over probability measures.
New algorithms for efficient return distribution approximation in reinforcement learning.
This paper compares ML algorithms for PD prediction, finding XGBoost to be the most effective.
A new algorithm approximates logistic regression probabilities efficiently.
UAV enabled communications and networking can enhance wireless connectivity and support emerging services. However, this would require system-level understanding to modify and extend the existing terrestrial network infrastructure. In this paper, we integrate UAVs both as user equipment and base stations into existing …
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable fo…
Improved algorithm for selecting a hypothesis locally privately with fewer queries.
Expands Hidden Markov Model to include Markov chain observations.
New algorithm for competing influence spread in unknown networks.