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.
Paper shows re-solving heuristics have constant regret for price-based revenue management.
New algorithm tackles dynamic assortment optimization with knapsack constraints.
New algorithm reduces online decision-making regret with efficient LP re-solving and parallel first-order method.
Meta Optimal Transport learns from past problems to solve similar OT problems faster.
A new pricing controller handles resource constraints to infer target prices effectively.
Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of…
This work improves chemistry modeling by jointly learning reaction progress variables and look-up models.
This text is the rejoinder following the discussion of a survey paper about minimal penalties and the slope heuristics (Arlot, 2019. Minimal penalties and the slope heuristics: a survey. Journal de la SFDS). While commenting on the remarks made by the discussants, it provides two new results about the slope heuristics …
Proposes a method to imitate active learning heuristics for better performance.
This research evaluates and introduces new heuristics for clustering Bitcoin blockchain entities.
Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, ev…
Nemo improves WS learning pipeline by 20%.
This paper evaluates heuristics and hyperparameters in weight-sharing NAS methods.
Interactive weak supervision learns useful heuristics from user feedback.
We propose a general approach to modeling semi-supervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domain-specific heuristics. In ad…
We implement a market microstructure model including informed, uninformed and heuristic-driven investors, which latter behave in line with loss-aversion and mental accounting. We show that the probability of informed trading (PIN) varies significantly during 2008. In contrast, the probability of heuristic-driven tradin…
The report evaluates heuristics for learning timescale graphical event models.
New heuristics for predicting links in multiplex networks.
Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that optimize a scoring function. The heuristic represents a relaxation of the learning problem such that each variable chooses optimal parents in…
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
Physics-informed neural networks and neural operators speed up solving parametric PDEs by orders of magnitude.
Heuristic algorithms such as simulated annealing, Concorde, and METIS are effective and widely used approaches to find solutions to combinatorial optimization problems. However, they are limited by the high sample complexity required to reach a reasonable solution from a cold-start. In this paper, we introduce a novel …
Bayesian symbolic regression automates model discovery from data.
This work explores the non-convex optimization in compressive learning and the performance of heuristics.
In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels. While its empirical performances make it a safe choice under many circumstances, there is little theoretical understanding of why this is the case. Our aim in this paper is to advance our understanding of the …
Heuristic weighting improves denoising score matching without requiring noise distribution assumptions.
We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). The proposed method can be used to specify ensembles of semi-supervised learning, as well as agreement constraints and entropic regularization constraints between these learners, and can be used to model both well-known h…
Two heuristics solve dynamic multiple travelling salesmen problems.
A* is a popular path-finding algorithm, but it can only be applied to those domains where a good heuristic function is known. Inspired by recent methods combining Deep Neural Networks (DNNs) and trees, this study demonstrates how to train a heuristic represented by a DNN and combine it with A*. This new algorithm which…
Deep RL learns 2-opt heuristics to improve TSP solutions.
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heurist…
RL improves combinatorial optimization by automating heuristic search.
This paper investigates Shampoo's heuristics and decouples preconditioner updates.
Developed Forex trading heuristics with high profit potential.
New approach improves black-box planning efficiency by discovering focused macros.
Decision tree learning heuristics fail even in smoothed analysis for complex targets.
New method detects heuristics in complex game strategies.
This research develops heuristics to detect CoinJoin transactions on Bitcoin blockchain.
A new algorithm improves efficiency and robustness of heuristic optimization in simulation-based problems.
Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained i…
AdamQLR optimizes Adam with K-FAC heuristics, achieving comparable performance to tuned benchmarks.
The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strate…
New heuristic selects fewer assets for efficient portfolios, reducing costs.
Link prediction is a popular research topic in network analysis. In the last few years, new techniques based on graph embedding have emerged as a powerful alternative to heuristics. In this article, we study the problem of systematic biases in the prediction, and show that some methods based on graph embedding offer le…
Paper presents a probabilistic framework for diffusion synchronization.
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal…
GE finds failures in autonomous systems without domain heuristics.
Learning sentence vectors from an unlabeled corpus has attracted attention because such vectors can represent sentences in a lower dimensional and continuous space. Simple heuristics using pre-trained word vectors are widely applied to machine learning tasks. However, they are not well understood from a theoretical per…