pwc-archive/papers-with-abstracts huggingface dataset:
A Contextual Online Learning Theory of Brokerage
Authors: François Bachoc, Tommaso Cesari, Roberto Colomboni
Date: 2024-05-22 00:00:00
Tasks: Learning Theory

We study the role of contextual information in the online learning problem of brokerage between traders. At each round, two traders arrive with secret valuations about an asset they wish to trade. The broker suggests a trading price based on contextual data about the asset. Then, the traders decide to buy or sell depending on whether their valuations are higher or lower than the brokerage price. We assume the market value of traded assets is an unknown linear function of a $d$-dimensional vector representing the contextual information available to the broker. Additionally, we model traders' valuations as independent bounded zero-mean perturbations of the asset's market value, allowing for potentially different unknown distributions across traders and time steps. Consistently with the existing online learning literature, we evaluate the performance of a learning algorithm with the regret with respect to the gain from trade. If the noise distributions admit densities bounded by some constant $L$, then, for any time horizon $T$: - If the agents' valuations are revealed after each interaction, we provide an algorithm achieving $O ( L d \ln T )$ regret, and show a corresponding matching lower bound of $\Omega( Ld \ln T )$. - If only their willingness to sell or buy at the proposed price is revealed after each interaction, we provide an algorithm achieving $O(\sqrt{LdT \ln T })$ regret, and show that this rate is optimal (up to logarithmic factors), via a lower bound of $\Omega(\sqrt{LdT})$. To complete the picture, we show that if the bounded density assumption is lifted, then the problem becomes unlearnable, even with full feedback.
Entropy and Economics
Authors: Martin Pomares Calero
Date: 2024-05-07 00:00:00

Entropy is a very useful concept from physics that tries to explain how a system behaves from a point of view of the thermodynamics. However, there are two ways to explain entropy, and it depends on if we are studying a microsystem or a microsystem. From a macroscopically point of view, it is important to describe if the system is a reversible system or not. However, form the microscopically point of view, the concept of chaos is related to entropy. In such case, entropy measures the amount of disorder into the system. Otherwise, the idea of connecting at the same time the analysis of the macro and micro system with the use of entropy it is not very common.
Household Leverage Cycle Around the Great Recession
Authors: Bo Li
Date: 2024-03-15 00:00:00

This paper provides the first causal evidence that credit supply expansion caused the 1999-2010 U.S. business cycle mainly through the channel of household leverage (debt-to-income ratio). Specifically, induced by net export growth, credit expansion in private-label mortgages, rather than government-sponsored enterprise mortgages, causes a much stronger boom and bust cycle in household leverage in the high net-export-growth areas. In addition, such a stronger household leverage cycle creates a stronger boom and bust cycle in the local economy, including housing prices, residential construction investment, and house-related employment. Thus, our results are consistent with the credit-driven household demand channel (Mian and Sufi, 2018). Further, we show multiple pieces of evidence against the corporate channel, which is emphasized by other business cycle theories (hypotheses).
Regulating Cryptocurrency and Decentralized Finance for an Inclusive Economy
Authors: Amrutha Muralidhar, Muralidhar Lakkanna
Date: 2024-02-15 00:00:00
Tasks: Fairness

The evolution of cryptocurrency and decentralized finance (DeFi) marks a significant shift in the financial landscape, making it more accessible, inclusive, and participative for various societal groups. However, this transition from traditional financial institutions to DeFi demands a meticulous policy framework that strikes a balance between innovation and safeguarding consumer interests, security, and regulatory compliance. In this script we explore the imperative need for regulatory frameworks overseeing cryptocurrencies and DeFi, aiming to leverage their potential for inclusive economic advancement. It underscores the prevalent challenges within conventional financial systems, juxtaposing them with the transformative potential offered by these emergent financial paradigms. By highlighting the role of robust regulations, we examine their capacity to ensure user security, fortify market resilience, and spur innovative strides. We aim to proffer viable strategies for formulating regulatory structures that harmonize the twin objectives of fostering innovation and upholding fairness within financial ecosystems.
The not-so-hidden risks of 'hidden-to-maturity' accounting: on depositor runs and bank resilience
Authors: Zachary Feinstein, Grzegorz Halaj, Andreas Sojmark
Date: 2024-07-03 00:00:00

We build a balance sheet-based model to capture run risk, i.e., a reduced potential to raise capital from liquidity buffers under stress, driven by depositor scrutiny and further fueled by fire sales in response to withdrawals. The setup is inspired by the Silicon Valley Bank (SVB) meltdown in March 2023 and we apply our model to assess the build-up of balance sheet vulnerabilities before its default. More generally, we analyze which characteristics of the balance sheet are critical for banking system regulators to adequately assess run risk and resilience. By bringing a time series of SVB's balance sheet data to our model, we are able to demonstrate how changes in the funding and respective asset composition made SVB prone to run risk, as they were increasingly relying on held-to-maturity accounting standards, masking revaluation losses in securities portfolios. Next, we formulate a tractable optimization problem to address the designation of held-to-maturity assets and quantify banks' ability to hold these assets without resorting to remarking. By calibrating this to SVB's balance sheet data, we shed light on the bank's funding risk and implied risk tolerance in the years 2020--22 leading up to its collapse. We conclude by validating our model on the balance sheets of First Republic Bank, US Bancorp, and PNC Financial Services Group Inc.
DeFi's Concentrated Liquidity From Scratch
Authors: Mark B. Richardson, Stefan Loesch
Date: 2024-04-30 00:00:00

The scope of this article includes the three preeminent descriptions of concentrated liquidity from Bancor (2020 and 2022), and Uniswap (2021), as well as three additional descriptions informed by trigonometric analysis of the same. The purpose of this contribution is to organize the seminal and derivative forms of this cornerstone DeFi technology, and algebraically and geometrically elaborate these descriptions to achieve an authoritative and near-exhaustive overview of the underlying theory powering the current state-of-the-art in decentralized exchange infrastructure. This material was created for the Token Engineering Academy Study Season 2024, a cohort-based online program scheduled for April-July 2024. The Study Season offers access to a bachelor-level online learning program, and complementary live tracks with the most influential practitioners and researchers in the sector - all provided as free, public goods.
Benchmark-Neutral Pricing
Authors: Eckhard Platen
Date: 2024-04-09 00:00:00

The paper introduces benchmark-neutral pricing and hedging for long-term contingent claims. It employs the growth optimal portfolio of the stocks as numeraire and the new benchmark-neutral pricing measure for pricing. For a realistic parsimonious model, this pricing measure turns out to be an equivalent probability measure, which is not the case for the risk-neutral pricing measure. Many risk-neutral prices of long-term contracts are more expensive than necessary. Benchmark-neutral pricing identifies the minimal possible prices of contingent claims, which is illustrated with remarkable accuracy for a long-term zero-coupon bond.
Quantitative Investment Diversification Strategies via Various Risk Models
Authors: Maysam Khodayari Gharanchaei, Prabhu Prasad Panda, Xilin Chen
Date: 2024-04-27 00:00:00

This paper focuses on the developing of high-dimensional risk models to construct portfolios of securities in the US stock exchange. Investors seek to gain the highest profits and lowest risk in capital markets. We have developed various risk models and for each model different investment strategies are tested. Out of sample tests are performed on a long-term horizon from 1970 until 2023.
Liquidity Adjustment in Multivariate Volatility Modeling: Evidence from Portfolios of Cryptocurrencies and US Stocks
Authors: Qi Deng
Date: 2024-03-30 00:00:00
Tasks: Management

We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adjusted return and volatility that reflect real-time liquidity variability. These liquidity-adjusted inputs are integrated into a VECM-DCC/ADCC-Bayesian model, allowing for conditional and posterior covariance estimation under liquidity stress. Applying this framework to portfolios of cryptocurrencies and US stocks, we find that traditional models misrepresent volatility and co-movement, while liquidity-adjusted models yield more stable and interpretable risk structures, particularly for portfolios of cryptocurrencies. The findings support the use of liquidity-adjusted multivariate models as statistically grounded tools for assessing the propagation of portfolio risk under market frictions, with implications for asset pricing, market microstructure design, and portfolio management.
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading
Authors: Xi Cheng, Jinghao Zhang, Yunan Zeng, Wenfang Xue
Date: 2024-06-03 00:00:00
Tasks: Algorithmic Trading, Imitation Learning, Reinforcement Learning (RL), Representation Learning

Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remarkable capabilities in addressing algorithmic trading problems. However, the trading patterns differ among market conditions due to shifted distribution data. Ignoring multiple patterns in the data will undermine the performance of RL. In this paper, we propose MOT,which designs multiple actors with disentangled representation learning to model the different patterns of the market. Furthermore, we incorporate the Optimal Transport (OT) algorithm to allocate samples to the appropriate actor by introducing a regularization loss term. Additionally, we propose Pretrain Module to facilitate imitation learning by aligning the outputs of actors with expert strategy and better balance the exploration and exploitation of RL. Experimental results on real futures market data demonstrate that MOT exhibits excellent profit capabilities while balancing risks. Ablation studies validate the effectiveness of the components of MOT.
Coupling Machine Learning with Ontology for Robotics Applications
Authors: Osama F. Zaki
Date: 2024-06-08 00:00:00
Tasks: valid

In this paper I present a practical approach for coupling machine learning (ML) algorithms with knowledge bases (KB) ontology formalism. The lack of availability of prior knowledge in dynamic scenarios is without doubt a major barrier for scalable machine intelligence. My view of the interaction between the two tiers intelligence is based on the idea that when knowledge is not readily available at the knowledge base tier, more knowledge can be extracted from the other tier, which has access to trained models from machine learning algorithms. To analyse this hypothesis, I create two experiments based on different datasets, which are related directly to risk-awareness of autonomous systems, analysed by different machine learning algorithms (namely; multi-layer feedforward backpropagation, Naive Bayes, and J48 decision tree). My analysis shows that the two-tiers intelligence approach for coupling ML and KB is computationally valid and the time complexity of the algorithms during the robot mission is linear with the size of the data and knowledge.
A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)
Authors: Lam Pham, Phat Lam, Tin Nguyen, Hieu Tang, Alexander Schindler
Date: 2024-05-02 00:00:00
Tasks: Acoustic Scene Classification, Event Detection, Image Captioning, object-detection, Object Detection, Scene Classification, Speech-to-Text, Video Captioning

In this paper, we present a toolchain for a comprehensive audio/video analysis by leveraging deep learning based multimodal approach. To this end, different specific tasks of Speech to Text (S2T), Acoustic Scene Classification (ASC), Acoustic Event Detection (AED), Visual Object Detection (VOD), Image Captioning (IC), and Video Captioning (VC) are conducted and integrated into the toolchain. By combining individual tasks and analyzing both audio \& visual data extracted from input video, the toolchain offers various audio/video-based applications: Two general applications of audio/video clustering, comprehensive audio/video summary and a specific application of riot or violent context detection. Furthermore, the toolchain presents a flexible and adaptable architecture that is effective to integrate new models for further audio/video-based applications.
Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness
Authors: Yingfang Yuan, Kefan Chen, Mehdi Rizvi, Lynne Baillie, Wei Pang
Date: 2024-05-24 00:00:00
Tasks: Decision Making, Fairness

The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing services, resources, and opportunities, emphasising the significance of fairness in AI. This is particularly relevant as an increasing number of AI tools are applied to decision-making processes, such as resource allocation and service scheme development, across various sectors such as health, energy, and housing. Therefore, exploring joint inequalities in these sectors is significant and valuable for thoroughly understanding overall inequality and unfairness. This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies among user-defined groups using latent class analysis. These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues. We validate our approach using both proprietary and public datasets, including both EVENS and Census 2021 (England & Wales) datasets, to examine cross-sectoral intersecting discrepancies among different ethnic groups. We also verify the reliability of the quantified discrepancy by conducting a correlation analysis with a government public metric. Our findings reveal significant discrepancies both among minority ethnic groups and between minority ethnic groups and non-minority ethnic groups, emphasising the need for targeted interventions in policy-making processes. Furthermore, we demonstrate how the proposed approach can provide valuable insights into ensuring fairness in machine learning systems.
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach
Authors: Yunpeng Jiang, Yutong Ban, Paul Weng
Date: 2024-05-31 00:00:00
Tasks: Data Augmentation, Fairness, Multi-class Classification

Data augmentation is widely applied and has shown its benefits in different machine learning tasks. However, as recently observed, it may have an unfair effect in multi-class classification. While data augmentation generally improves the overall performance (and therefore is beneficial for many classes), it can actually be detrimental for other classes, which can be problematic in some application domains. In this paper, to counteract this phenomenon, we propose CLAM, a CLAss-dependent Multiplicative-weights method. To derive it, we first formulate the training of a classifier as a non-linear optimization problem that aims at simultaneously maximizing the individual class performances and balancing them. By rewriting this optimization problem as an adversarial two-player game, we propose a novel multiplicative weight algorithm, for which we prove the convergence. Interestingly, our formulation also reveals that the class-dependent effects of data augmentation is not due to data augmentation only, but is in fact a general phenomenon. Our empirical results over six datasets demonstrate that the performance of learned classifiers is indeed more fairly distributed over classes, with only limited impact on the average accuracy.
An Efficient Framework for Crediting Data Contributors of Diffusion Models
Authors: Chris Lin, Mingyu Lu, Chanwoo Kim, Su-In Lee
Date: 2024-06-09 00:00:00
Tasks: Diversity, Fairness

As diffusion models are deployed in real-world settings, and their performance is driven by training data, appraising the contribution of data contributors is crucial to creating incentives for sharing quality data and to implementing policies for data compensation. Depending on the use case, model performance corresponds to various global properties of the distribution learned by a diffusion model (e.g., overall aesthetic quality). Hence, here we address the problem of attributing global properties of diffusion models to data contributors. The Shapley value provides a principled approach to valuation by uniquely satisfying game-theoretic axioms of fairness. However, estimating Shapley values for diffusion models is computationally impractical because it requires retraining on many training data subsets corresponding to different contributors and rerunning inference. We introduce a method to efficiently retrain and rerun inference for Shapley value estimation, by leveraging model pruning and fine-tuning. We evaluate the utility of our method with three use cases: (i) image quality for a DDPM trained on a CIFAR dataset, (ii) demographic diversity for an LDM trained on CelebA-HQ, and (iii) aesthetic quality for a Stable Diffusion model LoRA-finetuned on Post-Impressionist artworks. Our results empirically demonstrate that our framework can identify important data contributors across models' global properties, outperforming existing attribution methods for diffusion models.
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
Authors: Qiong Wu, Wei Yu, Yiyi Zhou, Shubin Huang, Xiaoshuai Sun, Rongrong Ji
Date: 2023-09-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Reinforcement Learning (RL), Transfer Learning

With ever increasing parameters and computation, vision-language pre-trained (VLP) models exhibit prohibitive expenditure in downstream task adaption. Recent endeavors mainly focus on parameter efficient transfer learning (PETL) for VLP models by only updating a small number of parameters. However, excessive computational overhead still plagues the application of VLPs. In this paper, we aim at parameter and computation efficient transfer learning (PCETL) for VLP models. In particular, PCETL not only needs to limit the number of trainable parameters in VLP models, but also to reduce the computational redundancy during inference, thus enabling a more efficient transfer. To approach this target, we propose a novel dynamic architecture skipping (DAS) approach towards effective PCETL. Instead of directly optimizing the intrinsic architectures of VLP models, DAS first observes the significances of their modules to downstream tasks via a reinforcement learning (RL) based process, and then skips the redundant ones with lightweight networks, i.e., adapters, according to the obtained rewards. In this case, the VLP model can well maintain the scale of trainable parameters while speeding up its inference on downstream tasks. To validate DAS, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of VL tasks. The experimental results not only show the great advantages of DAS in reducing computational complexity, e.g. -11.97% FLOPs of METER on VQA2.0, but also confirm its competitiveness against existing PETL methods in terms of parameter scale and performance. Our source code is given in our appendix.
Personalized Federated Continual Learning via Multi-granularity Prompt
Authors: Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang, Tianrui Li
Date: 2024-06-27 00:00:00
Tasks: Continual Learning, Federated Learning, Personalized Federated Learning

Personalized Federated Continual Learning (PFCL) is a new practical scenario that poses greater challenges in sharing and personalizing knowledge. PFCL not only relies on knowledge fusion for server aggregation at the global spatial-temporal perspective but also needs model improvement for each client according to the local requirements. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it allows more effectively to personalized shared knowledge, thus serving its own purpose. To this end, we propose a novel concept called multi-granularity prompt, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. In addition, we design a selective prompt fusion mechanism for aggregating knowledge of global prompts distilled from different clients. By the exclusive fusion of coarse-grained knowledge, we achieve the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization. Extensive experiments demonstrate the effectiveness of the proposed method in addressing STCF as well as improving personalized performance. Our code now is available at https://github.com/SkyOfBeginning/FedMGP.
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Authors: Alan Q. Wang, Minh Nguyen, Mert R. Sabuncu
Date: 2023-09-23 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Domain Generalization

Machine learning models will often fail when deployed in an environment with a data distribution that is different than the training distribution. When multiple environments are available during training, many methods exist that learn representations which are invariant across the different distributions, with the hope that these representations will be transportable to unseen domains. In this work, we present a nonparametric strategy for learning invariant representations based on the recently-proposed Nadaraya-Watson (NW) head. The NW head makes a prediction by comparing the learned representations of the query to the elements of a support set that consists of labeled data. We demonstrate that by manipulating the support set, one can encode different causal assumptions. In particular, restricting the support set to a single environment encourages the model to learn invariant features that do not depend on the environment. We present a causally-motivated setup for our modeling and training strategy and validate on three challenging real-world domain generalization tasks in computer vision.