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.
The paper extends physics-based information maximization to complex bandit problems.
Efficiently maximizes AUC with deep nets, reducing communication rounds.
Efficiently selects seed nodes to maximize content influence in unknown social networks.
Maximizes mixing efficiency in surface braids.
Designs efficient algorithms to maximize the expectation of Gaussian random variables.
New algorithms solve linear bandits in high dimensions efficiently.
Optimizes energy efficiency in wireless sensor networks with limited information.
One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable propert…
The paper proposes a new method for dictionary learning using -norm maximization.
Study aims to optimize financial investments by balancing risk and reward efficiently.
In this paper, we introduce a novel, non-recursive, maximal matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. It differs from the usual equilibrium matching, which clears a market at the equilibrium price. We compare the two algorithms through experimental analyses, …
We optimize discounts to maximize influence spread in social networks.
Neural network MCMC sampler maximizes proposal entropy for efficient sampling.
New algorithm for maximizing submodular functions in real-time data changes.
Maximize to Explore integrates RL components for efficient policy discovery.
LITE efficiently estimates Gaussian PoM with linear time and memory complexity.
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate quadratically depending on the problem condition number. Besides, the dense communication is necessary to ensure the convergence even if the …
MADE improves exploration in RL by maximizing deviation from explored regions.
We propose an efficient algorithm for solving orthogonal canonical correlation analysis (OCCA) in the form of trace-fractional structure and orthogonal linear projections. Even though orthogonality has been widely used and proved to be a useful criterion for pattern recognition and feature extraction, existing methods …
Paper proposes efficient and accurate initialization and EM algorithm for PL mixture models.
New method uses diffusion models to optimize experimental design efficiently.
RIn-Close_CVC is an efficient (take polynomial time per bicluster), complete (find all maximal biclusters), correct (all biclusters attend the user-defined level of consistency) and non-redundant (all the obtained biclusters are maximal and the same bicluster is not enumerated more than once) enumerative algorithm for …
Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F, the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluat…
Proposes a value-based method for continuous control without an actor.
AQL uses amortized inference to handle high-dimensional action spaces in Q-learning.
Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains. However, task requester usually has a limited amount of budget, thus it is desirable to have a policy to wisely allocate the budget to achieve better quality. In this paper, we study the princi…
Paper develops algorithms to maximize AUC in imbalanced classification.
Maximal correlation framework improves fairness in machine learning algorithms.
In this paper the problem of optimal derivative design, profit maximization and risk minimization under adverse selection when multiple agencies compete for the business of a continuum of heterogenous agents is studied. The presence of ties in the agents' best-response correspondences yields discontinuous payoff functi…
New bandit algorithm maximizes information gain.
New algorithm reduces combinatorial semi-bandit regret efficiently.
Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we generalize the framework of modularity maximization for clustering on hypergraphs. To this end, we introduce a hypergraph null model, analogou…
Maximal Rate of Stepwise Uncertainty Reduction selects simulations to reduce uncertainty efficiently.
Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization …
The Cartan equivalence method is utilized to deduce an invariant characterization of the scalar third-order ordinary differential equation which admits the maximal seven-dimensional point symmetry Lie algebra. The method provides auxiliary functions which can be used to efficiently obtain the point t…
We propose a modified expectation-maximization algorithm by introducing the concept of quantum annealing, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. The expectation-maximization (EM) algorithm is an established algorithm to compute maximum likelihood estimates and appl…
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 …
TES optimizes black-box functions efficiently with minimal approximations.
Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it only involves continuous optimization of model parameters, which is substantially easie…
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effec…
Optimal trading patterns adjust based on market efficiency and slippage costs.
Maximizes Rényi entropy for efficient exploration in reward-free RL.
Enhanced decision-making through Dreamer's anticipatory trajectories and Online Decision Transformer.
Support vector regression (SVR) is one of the most popular machine learning algorithms aiming to generate the optimal regression curve through maximizing the minimal margin of selected training samples, i.e., support vectors. Recent researchers reveal that maximizing the margin distribution of whole training dataset ra…
Directly estimates Fisher score for likelihood maximization.
We propose two neural network based mixture models in this article. The proposed mixture models are explicit in nature. The explicit models have analytical forms with the advantages of computing likelihood and efficiency of generating samples. Computation of likelihood is an important aspect of our models. Expectation-…
Empirical evidence suggests that even the most competitive markets are not strictly efficient. Price histories can be used to predict near future returns with a probability better than random chance. Many markets can be considered as {\it favorable games}, in the sense that there is a small probabilistic edge that smar…
Software finds ideal polyhedra with rational dihedral angles and volume maxima.