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.
Efficiently computes quasiconcave envelope with limited data.
New method learns better branching policies for MILP problems.
We study the robustness verification problem for tree-based models, including decision trees, random forests (RFs) and gradient boosted decision trees (GBDTs). Formal robustness verification of decision tree ensembles involves finding the exact minimal adversarial perturbation or a guaranteed lower bound of it. Existin…
ReLU networks trained with MILPs match deep learning accuracy.
New hybrid model reduces MILP solver time by up to 26%.
Binarized Neural Networks (BNNs) have recently attracted significant interest due to their computational efficiency. Concurrently, it has been shown that neural networks may be overly sensitive to "attacks" - tiny adversarial changes in the input - which may be detrimental to their use in safety-critical domains. Desig…
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outl…
Graph Neural Networks learn to mimic strong branching in MILP solvers.
MIP-GNN uses graph neural networks to predict variable biases for MIP solvers.
NeuralCut learns to select cutting planes by looking ahead, outperforming traditional methods.
Algorithm finds near-optimal VaR portfolios using MILP, improving risk management.
The paper verifies the robustness of classifier ensembles against randomized attacks.
The paper analyzes how behavioral investors make portfolio decisions using Markowitz Stochastic Dominance criteria.
Optimal design for multinomial logit models improves assortment selection efficiency.
Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACV…
We can compare the expressiveness of neural networks that use rectified linear units (ReLUs) by the number of linear regions, which reflect the number of pieces of the piecewise linear functions modeled by such networks. However, enumerating these regions is prohibitive and the known analytical bounds are identical for…
Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix…
Glitches cause unreliable AI decisions with steep boundaries.
We address the problem of verifying neural-based perception systems implemented by convolutional neural networks. We define a notion of local robustness based on affine and photometric transformations. We show the notion cannot be captured by previously employed notions of robustness. The method proposed is based on re…
New framework detects model weaknesses in decision tree ensembles.
This work analyzes machine learning for Lagrangian Relaxation in MILP.
This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modelled in this formulation by including a budget constraint that sets in advance a limit on the number of features …
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several e…
Reformulated Markov's conjecture in combinatorial terms.
We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing strategies. We introduce Checkmate, a system that solves for optimal rematerialization schedules in reasonable times (under an hour) using off-t…
This paper proposes exact and approximation algorithms for Sparse PCA, improving interpretability and scalability.
After reconsidering the Dasbach-Hougardy counterexample to the Kauffman Conjecture on alternating knots, we reformulate the conjecture and consider Dasbach-Hougardy counterexample and similar counterexamples in the light of the reformulated conjecture.
Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition, where large neural networks are often used to obtain good accuracy. Consequently, it is challenging to deploy these networks under limited computational resources, such as in mobile devices. In this work…
We reformulate LIPs as min-max problems for easier solution.
Virtual index cocycles reformulate virtual link invariants.
DNNs with regularization reveal feature learning dynamics and sparsity.
Reformulated sigma models for complex Grassmannians using Gross-Neveu formalism.
We reformulate data-dependent constraints to ensure they are always met with high probability.
We establish a characterization of adequate knots in terms of the degree of their colored Jones polynomial. We show that, assuming the Strong Slope conjecture, our characterization can be reformulated in terms of "Jones slopes" of knots and the essential surfaces that realize the slopes .For alternating knots the refor…
We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to…
A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction, data representation and recommendation systems in the literature. The traditional RBM requires a probabilistic interpretation of the values on both layers and a Markov ch…
Paper reformulates UOT as non-negative penalized linear regression for efficient algorithms.
The paper reformulates an invariant and calculates it for lens spaces.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
Quaternionic reformulation simplifies surface curvature theory.
The paper explores handlebody versions of various diagram algebras.
Introduces CCR for constructing confidence regions from conformal predictions.
Paper tackles SMPC for linear systems with unknown noise distribution.
Study reformulates Finsler metrizability problems using geodesic invariance.
Optimizes distributions robustly with Sinkhorn distance.
The topology of symplectic 4-manifolds is related to that of singular plane curves via the concept of branched covers. Thus, various classification problems concerning symplectic 4-manifolds can be reformulated as questions about singular plane curves. Moreover, using braid monodromy, these can in turn be reformulated …
We claim that the recently discovered universal-matrix precursor for the functions, which define the differential expansion of colored polynomials for twist and double braid knots, can be extended from rectangular to non-rectangular representations. This case is far more interesting, because it involves multiplicit…
The concept of refinement from probability elicitation is considered for proper scoring rules. Taking directions from the axioms of probability, refinement is further clarified using a Hilbert space interpretation and reformulated into the underlying data distribution setting where connections to maximal marginal diver…