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.
Algorithm solves two-sided matching markets with unknown preferences and constraints.
New model for shape graph registration with partial matching constraints.
A novel method relaxes binary constraints to non-negative spheres for multi-matching and clustering.
RFM improves CNFs by adding a boundary constraint term and matching velocity fields.
NeuroMatch efficiently matches subgraphs in large graphs using neural networks.
Adaptive allocation with constraints using Thompson sampling.
New approach to sparse optimal transport for matching tokens with experts.
Efficiently learns matching rewards in two-sided markets with matrix completion.
Robust optimization is becoming increasingly important in machine learning applications. In this paper, we study a unified framework of robust submodular optimization. We study this problem both from a minimization and maximization perspective (previous work has only focused on variants of robust submodular maximizatio…
Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate label…
The paper models market dynamics using a limit order book system to explain slippage and inefficiency.
Proposes a non-adversarial method for distribution matching.
Level-set optimization formulations with data-driven constraints minimize a regularization functional subject to matching observations to a given error level. These formulations are widely used, particularly for matrix completion and sparsity promotion in data interpolation and denoising. The misfit level is typically …
The pair-matching problem appears in many applications where one wants to discover good matches between pairs of entities or individuals. Formally, the set of individuals is represented by the nodes of a graph where the edges, unobserved at first, represent the good matches. The algorithm queries pairs of nodes and obs…
Study dynamic matching in heterogeneous networks using ODE model.
We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in the future. We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time. Job typ…
Registration, which aims to find an optimal 1-1 correspondence between shapes, is an important process in different research areas. Conformal mappings have been widely used to obtain a diffeomorphism between shapes that minimizes angular distortion. Conformal registrations are beneficial since it preserves the local ge…
A new algorithm tackles submodular bandit problems with multiple constraints.
We consider a non-stationary sequential stochastic optimization problem, in which the underlying cost functions change over time under a variation budget constraint. We propose an -variation functional to quantify the change, which yields less variation for dynamic function sequences whose changes are constrai…
In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Sy…
Study develops a new method for creating fair models.
This paper tackles robust submodular minimization for image segmentation and correspondence.
DALI improves inference for GANs by matching prior and conditional distributions.
Unified framework for combinatorial and rounding algorithms in experimental design.
New framework improves generative models with prediction and consistency constraints.
Paper tackles constrained bandit problems with a new learning framework.
Graph Energy Matching improves generation quality for molecular graphs.
New algorithm ensures fair matching in resource allocation.
Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors. However, relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices---for one reason, compositio…
This paper characterizes the equilibrium in a continuous time financial market populated by heterogeneous agents who differ in their rate of relative risk aversion and face convex portfolio constraints. The model is studied in an application to margin constraints and found to match real world observations about financi…
New method extracts joint and individual signals from multi-view data.
Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.
In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints. We analyze a primal-dual algorithm, called the Generalized Sequential algorithm, and we obtain t…
New bounds for learning near-optimal policies in CMDPs with constraints.
The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric consistency between points into account. Computationally, the multi-match…
ACOL learns constraints from human preferences in driving simulations.
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is tr…
In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints. For instance, the designer may want to limit the use of unsafe actions, increase the diversity of trajectories to enable exploratio…
AR-CSM models use derivatives of univariate log-conditionals to estimate joint distributions efficiently.
Improves k-NN for monotonic data with robustness against noise.
Optimization results are one method for understanding neural computation from Nature's perspective and for defining the physical limits on neuron-like engineering. Earlier work looks at individual properties or performance criteria and occasionally a combination of two, such as energy and information. Here we make use …
BAICS identifies best arm with fairness constraints on subpopulations.
This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. Entity resolution-the data integration problem of performing noisy joins on structured data-typically proceeds by first hashing each record into zero or mor…
Study best arm identification with safety constraints in bandit problems.
This paper introduces a new mathematical formulation and numerical approach for the computation of distances and geodesics between immersed planar curves. Our approach combines the general simplifying transform for first-order elastic metrics that was recently introduced by Kurtek and Needham, together with a relaxatio…
Unified framework for unlearning in diffusion models using KL divergence and likelihood constraints.
New framework for distributed nonparametric estimation under slow communication.
Study connects database alignment and planted matching using Gaussian features.