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.
Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase ac…
Platform uses queries to elicit investor preferences for portfolio trades, improving allocation efficiency.
Enhances math problem-solving models with multi-turn preference learning.
The paper designs tests for comparing ranked preference data and finds significant differences.
Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models can often be transformed into an instance of the other, by switching between centered and differentiable non-centered parameterizations of …
Paper proposes a method to adapt classifiers using complementary labels instead of true labels.
A weakly-supervised learning framework named as complementary-label learning has been proposed recently, where each sample is equipped with a single complementary label that denotes one of the classes the sample does not belong to. However, the existing complementary-label learning methods cannot learn from the easily …
We show that any self-complementary graph with vertices contains a minor. We derive topological properties of self-complementary graphs.
In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that observations do \textbf{not} belong to. Let and be the true and complementary labels, respectively. We first model the annotati…
Proposes a method for multi-view clustering that integrates consistent and complementary graph regularizers.
We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distributed representations for items from available textual data, with the learned representations representing items in a latent space expressing f…
In this note we study the Seifert rational homology spheres with two complementary legs, i.e. with a pair of invariants whose fractions add up to one. We give a complete classification of the Seifert manifolds with 3 exceptional fibers and two complementary legs which bound rational homology balls. The result translate…
Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification. A complementary label specifies a class that a pattern does not belong to. Collecting compl…
Clarinet uses complementary labels to train classifiers with less source data.
Complementary products recommendation is an important problem in e-commerce. Such recommendations increase the average order price and the number of products in baskets. Complementary products are typically inferred from basket data. In this study, we propose the BB2vec model. The BB2vec model learns vector representat…
A supervised learning framework has been proposed for the situation where each training data is provided with a complementary label that represents a class to which the pattern does not belong. In the existing literature, complementary-label learning has been studied independently from ordinary-label learning, which as…
Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increa…
We establish a form of the h-principle for the existence of foliations quasi-complementary to a given one; the same methods also provide a proof of the classical Mather-Thurston theorem.
Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two feat…
UREs lead to overfitting in complex models, especially in complementary label learning.
Optimizes molecular generation for chemist preferences.
New method adapts to user preferences dynamically, improving recommendation models.
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to. The goal of this paper is to derive …
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
Despite all our great advances in science, technology and financial innovations, many societies today are struggling with a financial, economic and public spending crisis, over-regulation, and mass unemployment, as well as lack of sustainability and innovation. Can we still rely on conventional economic thinking or do …
Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for learning features relevant to discriminative tasks. Autoencoders, in particular, have proven to be an effective way to learn latent codes th…
Enhances preference learning by incorporating response times into binary choices.
Bayesian optimization learns DM preferences for multi-outcome experiments.
New study shows personalized content recommendations can lead to polarization of user preferences.
Bayesian optimization agent learns user preferences from pairwise comparisons.
New RLHF framework handles general preference oracles without reward functions.
This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil…
Study on identifying most preferred policy in bandits with vector-valued rewards.
Stable and consistent model alignment for language models without assuming human preference models.
Dropping a tiny fraction of preferences can significantly alter the rankings of top LLMs.
Paper explores limits and possibilities of aligning LLMs with human preferences.
Paper improves parameter estimation of continuous distributions using preference feedback.
Paper investigates monotonicity issues in AI preference learning.
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstrated the effectiveness of recommendation tasks, it is lack of explorations on integrating probabilis…
DOPL learns from preference feedback to solve RMAB problems.
Direct Density Ratio Optimization aligns LLMs with human preferences without assuming specific models.
Framework transfers complementary operating conditions to train anomaly detectors.
Bayesian optimization with preference learning identifies preferred solutions in multi-objective problems.
Training models to prefer certain responses can unintentionally shift probability to harmful ones.
This work proves win rate is key to understanding preference learning.
IDT learns human preferences from uncertain decisions, even when humans are suboptimal.