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Found 52 papers, 3 papers with code
ViziTex: Interactive Visual Sense-Making of Text Corpora
Information visualization is critical to analytical reasoning and knowledge discovery.
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights.
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations
Our experiments on two popular benchmarks -- Mind2Web & VisualWebArena -- show that using in-context demonstrations (for proprietary models) or meta-adaptation demonstrations (for meta-learned open-weights models) boosts task success rate by 3. 36% to 7. 21% over non-adapted state-of-the-art models, corresponding to a relative increase of 21. 03% to 65. 75%.
Behavioral Sequence Modeling with Ensemble Learning
We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavior.
Variational Neural Stochastic Differential Equations with Change Points
We develop two methodologies for modeling and estimating change points in time-series data with distribution shifts.
AI in Investment Analysis: LLMs for Equity Stock Ratings
Investment Analysis is a cornerstone of the Financial Services industry.
Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters
Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement.
Auditing and Enforcing Conditional Fairness via Optimal Transport
Many algorithmic fairness techniques exist to target demographic parity, but CDP is much harder to achieve, particularly when the conditioning variable has many levels and/or when the model outputs are continuous.
Ensemble Methods for Sequence Classification with Hidden Markov Models
We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs).
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
To mitigate this issue, several successful approaches have been proposed, including replacing the empirical distribution in training with: (i) a worst-case distribution within an ambiguity set, leading to a distributionally robust (DR) counterpart of ARO; or (ii) a mixture of the empirical distribution with one derived from an auxiliary dataset (e. g., synthetic, external, or out-of-domain).
LETS-C: Leveraging Language Embedding for Time Series Classification
Recent advancements in language modeling have shown promising results when applied to time series data.
HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies
This game is based on natural language schemas and importantly, ensures the security of the underlying data.
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more.
Six Levels of Privacy: A Framework for Financial Synthetic Data
In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well.
FlowMind: Automatic Workflow Generation with LLMs
FlowMind further simplifies user interaction by presenting high-level descriptions of auto-generated workflows, enabling users to inspect and provide feedback effectively.
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges.
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?
We make an assumption that LLMs can be used as implicit computational models of humans, and propose a framework to use synthetic demonstrations derived from LLMs to model subrational behaviors that are characteristic of humans (e. g., myopic behavior or preference for risk aversion).
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance.
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Fair Wasserstein Coresets
Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets.
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights.
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial.
INTAGS: Interactive Agent-Guided Simulation
To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning.
Exploring the Effectiveness of GPT Models in Test-Taking: A Case Study of the Driver's License Knowledge Test
The GPT-3 model achieved a 96% passing score on a set of 50 sample driving knowledge test questions.
Differentially Private Synthetic Data Using KD-Trees
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge.
Financial Time Series Forecasting using CNN and Transformer
In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly.
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data.
Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets
We evaluate the behavior of sub-rational human investors using hand-crafted market scenarios and SHAP value analysis, showing that our model accurately reproduces the observations in the previous studies and reveals insights of the driving factors of human behavior.
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Learning to simulate realistic limit order book markets from data as a World Agent
Our approach proposes to learn a unique "world" agent from historical data.
Equitable Marketplace Mechanism Design
The goal of this work is to design a dynamic fee schedule for the marketplace that is equitable and profitable to all traders while being profitable to the marketplace at the same time (from charging fees).
Optimal Stopping with Gaussian Processes
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets.
Online Learning for Mixture of Multivariate Hawkes Processes
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors.
Differentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.
CTMSTOU driven markets: simulated environment for regime-awareness in trading policies
Market regimes is a popular topic in quantitative finance even though there is little consensus on the details of how they should be defined.
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks.
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets
We introduce a general technique to wrap a DEMAS simulator into the Gym framework.
Towards Realistic Market Simulations: a Generative Adversarial Networks Approach
Unfortunately, this approach does not capture the market response to the experimental agents' actions.
Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets
Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments.
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators
In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable.
Visual Time Series Forecasting: An Image-driven Approach
In this work, we address time-series forecasting as a computer vision task.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
Deep Video Prediction for Time Series Forecasting
In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction.
SIM-GAN: Adversarial Calibration of Multi-Agent Market Simulators.
In this paper, we present SIM-GAN -- a multi-agent simulator calibration method that allows to tune simulator parameters and to support more accurate evaluations of candidate trading algorithm.
Visual Time Series Forecasting: An Image-driven Approach
In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting.
SURF: Improving classifiers in production by learning from busy and noisy end users
We show that conventional crowdsourcing algorithms struggle in this user feedback setting, and present a new algorithm, SURF, that can cope with this non-response ambiguity.
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it.
On the Importance of Opponent Modeling in Auction Markets
The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions.
Multiplayer AlphaZero
The AlphaZero algorithm has achieved superhuman performance in two-player, deterministic, zero-sum games where perfect information of the game state is available.
Trading via Image Classification
The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis.
The Effect of Visual Design in Image Classification
In this study, we examine whether binary decisions are better to be decided based on the numeric or the visual representation of the same data.
