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.
KG-A2C agent learns natural language IF games by reasoning and constraining action spaces.
In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narrativ…
We explore the practicability of Nash's Embedding Theorem in vision and imaging sciences. In particular, we investigate the relevance of a result of Burago and Zalgaller regarding the existence of isometric embeddings of polyhedral surfaces in and we show that their proof does not extended directly to hi…
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
Multifractal analysis and extensive statistical tests are performed upon intraday minutely data within individual trading days for four stock market indexes (including HSI, SZSC, S&P500, and NASDAQ) to check whether the indexes (instead of the returns) possess multifractality. We find that the mass exponent is l…
RobKMR improves robustness in multi-omics data analysis for osteoporosis biomarker discovery.
We examine the question of when and how parametric models are most useful in reinforcement learning. In particular, we look at commonalities and differences between parametric models and experience replay. Replay-based learning algorithms share important traits with model-based approaches, including the ability to plan…
In this work we investigate on the concept of "restraining bolt", envisioned in Science Fiction. Specifically we introduce a novel problem in AI. We have two distinct sets of features extracted from the world, one by the agent and one by the authority imposing restraining specifications (the "restraining bolt"). The tw…
Language models are generally trained on data spanning a wide range of topics (e.g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e.g., restaurant reviews). In this paper, we first show that training on text outside the test distribution can degrade test performance whe…
This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical pheno…
We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real wor…
MCPCA analyzes shared factors across multiple data contexts.
The paper presents a method to make predictions robust to distributional shifts.
A society or country with income equally distributed among its people is truly a fiction! The phenomena of socioeconomic inequalities have been plaguing mankind from times immemorial. We are interested in gaining an insight about the co-evolution of the countries in the inequality space, from a data science perspective…
Random forests classify Pokemon names based on evolutionary status.
Optimizes asset allocation with illiquid assets using MPC.
Deep learning improves Bayes factor computation for likelihood-free models.
Surrogate-based analysis of interactions via local effect smooths
Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless featur…
A new method detects interactions in neural networks using topological analysis.
iKF method uncovers complex variable interactions for scientific discovery.
A graph neural network detects beneficial feature interactions for recommender systems.
We describe and extract time-ordered multibody interactions from complex systems.
Dropout regularizes against high-order interactions by canceling interaction rates.
Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent latent functions, all current MTGPs including the salient linear model of coregionalization (LMC) and convolution frameworks cannot effectively…
SocialInteractionGAN generates realistic human interactions from low-dimensional data.
A new method detects interactions in machine learning models.
xDeepInt learns both vector-wise and bit-wise feature interactions.
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions. Specifically, we propose a graph neural network that jointly predicts the discrete interaction modes and 5-second future trajectories for all agents in the scene. Our model infers an inte…
Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convo…
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a…
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structu…
Paper introduces IC-index to evaluate interaction prediction methods.
This paper uses Gaussian mixtures to mimic interactions in large language models.
Study reveals self-attention's role in learning and generalizing interactions.
Archipelago provides interpretable explanations of feature interactions in machine learning models.
Automated tests detect interactions in unstructured data.
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of exist…
PIN models feature interactions using a neural network that mimics decision trees.
The paper explains DNNs by quantifying interactions among input variables.
Modeling continuous movement of entities in latent space for interaction timing.
The paper improves dropout's utility by reducing interactions in deep neural networks.
New model quantifies interactions' role in real-world phenomena.
MEI model improves knowledge graph completion by efficiently modeling interactions between embeddings.
Paper proposes FINN for better CTR prediction by modeling feature interactions.
We discuss possible relationships between geometric and topological interactions on one side and physical interactions on the other side.