pwc-archive/papers-with-abstracts huggingface dataset:
Option-Based Pricing of Wrong Way Risk for CVA
Authors: Chris Kenyon, Andrew Green
Date: 2016-09-03 00:00:00

The two main issues for managing wrong way risk (WWR) for the credit valuation adjustment (CVA, i.e. WW-CVA) are calibration and hedging. Hence we start from a novel model-free worst-case approach based on static hedging of counterparty exposure with liquid options. We say "start from" because we demonstrate that a naive worst-case approach contains hidden unrealistic assumptions on the variance of the hazard rate (i.e. that it is infinite). We correct this by making it an explicit (finite) parameter and present an efficient method for solving the parametrized model optimizing the hedges. We also prove that WW-CVA is theoretically, but not practically, unbounded. The option-based hedges serve to significantly reduce (typically halve) practical WW-CVA. Thus we propose a realistic and practical option-based worst case CVA.
A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery
Authors: Anish rai, Ajit Mahata, Md. Nurujjaman, Sushovan Majhi, Kanish debnath
Date: 2021-10-08 00:00:00

Recently, a stock price model is proposed by A. Mahata et al. [Physica A, 574, 126008 (2021)] to understand the effect of COVID-19 on stock market. It describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp crisis and prolong drop followed by quick recovery (U-shaped) or slow recovery for longer period (Swoosh-shaped recovery). We propose a modified model by introducing a new variable $\theta$ that quantifies the sentiment of the investors. $\theta=+1,~0,~-1$ for positive, neutral and negative sentiment, respectively. The model explains the movement of sectoral indices with positive $\phi$ showing U- and Swoosh-shaped recovery. The simulation using synthetic fund-flow ($\Psi_{st}$) with different shock lengths ($T_S$), $\phi$, negative sentiment period ($T_N$) and portion of fund-flow ($\lambda$) during recovery period show U- and Swoosh-shaped recovery. The results show that the recovery of the indices with positive $\phi$ becomes very weak with the extended $T_S$ and $T_N$. The stocks with higher $\phi$ and $\lambda$ recover quickly. The simulation of the Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. The simulation result is consistent with the real stock price movement. The time-scale ($\tau$) of the shock and recovery of these indices during the COVID-19 are consistent with the time duration of the change of negative sentiment from the onset of the COVID-19. This study may help the investors to plan their investment during different crises.
Optimal Turnover, Liquidity, and Autocorrelation
Authors: Bastien Baldacci, Jerome Benveniste, Gordon Ritter
Date: 2021-10-07 00:00:00

The steady-state turnover of a trading strategy is of clear interest to practitioners and portfolio managers, as is the steady-state Sharpe ratio. In this article, we show that in a convenient Gaussian process model, the steady-state turnover can be computed explicitly, and obeys a clear relation to the liquidity of the asset and to the autocorrelation of the alpha forecast signals. Indeed, we find that steady-state optimal turnover is given by $\gamma \sqrt{n+1}$ where $\gamma$ is a liquidity-adjusted notion of risk-aversion, and $n$ is the ratio of mean-reversion speed to $\gamma$.
Active inference, Bayesian optimal design, and expected utility
Authors: Noor Sajid, Lancelot Da Costa, Thomas Parr, Karl Friston
Date: 2021-09-21 00:00:00

Active inference, a corollary of the free energy principle, is a formal way of describing the behavior of certain kinds of random dynamical systems that have the appearance of sentience. In this chapter, we describe how active inference combines Bayesian decision theory and optimal Bayesian design principles under a single imperative to minimize expected free energy. It is this aspect of active inference that allows for the natural emergence of information-seeking behavior. When removing prior outcomes preferences from expected free energy, active inference reduces to optimal Bayesian design, i.e., information gain maximization. Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i.e., expected utility maximization. Using these limiting cases, we illustrate how behaviors differ when agents select actions that optimize expected utility, expected information gain, and expected free energy. Our T-maze simulations show optimizing expected free energy produces goal-directed information-seeking behavior while optimizing expected utility induces purely exploitive behavior and maximizing information gain engenders intrinsically motivated behavior.
KOHTD: Kazakh Offline Handwritten Text Dataset
Authors: Nazgul Toiganbayeva, Mahmoud Kasem, Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Daniyar Nurseitov
Date: 2021-09-22 00:00:00
Tasks: Handwriting Recognition, Handwritten Text Recognition, HTR

Despite the transition to digital information exchange, many documents, such as invoices, taxes, memos and questionnaires, historical data, and answers to exam questions, still require handwritten inputs. In this regard, there is a need to implement Handwritten Text Recognition (HTR) which is an automatic way to decrypt records using a computer. Handwriting recognition is challenging because of the virtually infinite number of ways a person can write the same message. For this proposal we introduce Kazakh handwritten text recognition research, a comprehensive dataset of Kazakh handwritten texts is necessary. This is particularly true given the lack of a dataset for handwritten Kazakh text. In this paper, we proposed our extensive Kazakh offline Handwritten Text dataset (KOHTD), which has 3000 handwritten exam papers and more than 140335 segmented images and there are approximately 922010 symbols. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning. We used a variety of popular text recognition methods for word and line recognition in our studies, including CTC-based and attention-based methods. The findings demonstrate KOHTD's diversity. Also, we proposed a Genetic Algorithm (GA) for line and word segmentation based on random enumeration of a parameter. The dataset and GA code are available at https://github.com/abdoelsayed2016/KOHTD.
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference
Authors: Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, Orhan Firat
Date: 2021-09-24 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Mixture-of-Experts, Sentence

Sparse Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. However, MoE models are prohibitively large and practitioners often resort to methods such as distillation for serving. In this work, we investigate routing strategies at different granularity (token, sentence, task) in MoE models to bypass distillation. Experiments on WMT and a web-scale dataset suggest that task-level routing (task-MoE) enables us to extract smaller, ready-to-deploy sub-networks from large sparse models. On WMT, our task-MoE with 32 experts (533M parameters) outperforms the best performing token-level MoE model (token-MoE) by +1.0 BLEU on average across 30 language pairs. The peak inference throughput is also improved by a factor of 1.9x when we route by tasks instead of tokens. While distilling a token-MoE to a smaller dense model preserves only 32% of the BLEU gains, our sub-network task-MoE, by design, preserves all the gains with the same inference cost as the distilled student model. Finally, when scaling up to 200 language pairs, our 128-expert task-MoE (13B parameters) performs competitively with a token-level counterpart, while improving the peak inference throughput by a factor of 2.6x.
Efficient blockchain-based IoT firmware update considering distribution incentives
Authors: Tatsuhiro Fukuda, Kazumasa Omote
Date: 2021-02-10 00:00:00
Proceeding: Conference 2021 2
Tasks: Management

The number of Internet of Things (IoT) devices
has increased recently and will increase further in the future.
IoT devices are vulnerable, so vendors update and distribute
firmware. At the time of firmware distribution, a huge number
of IoT devices may connect to the firmware server and excessive
network traffic may occur. Consequently, a method using a peerto-
peer file sharing system and a blockchain has been proposed.
One study proposed an update scheme considering incentives
for distributors to help with updating. However, this method
requires the IoT devices, which have limited processing power,
to decrypt encrypted files, and the distribution nodes to pay the
transaction cost. This paper proposes a firmware distribution
method that provides incentives for distributors to help with
distribution to reduce the gas costs, using a smart contract
and access control based on update records. The additional
computations performed by IoT devices and distributors key
management were reduced compared to those in previous studies
by using access control instead of encryption. Further, the
proposed approach was implemented and evaluated using a
Raspberry Pi and laptop. The evaluation demonstrated that the
gas cost per update was successfully lowered compared to the
costs in previous studies.
Diverse Imitation Learning via Self-OrganizingGenerative Models
Authors: Arash Vahabpour, QIUJING LU, Tianyi Wang, Omead Pooladzandi, Vwani Roychowhury
Date: 2021-09-29 00:00:00
Tasks: Imitation Learning, MuJoCo

Imitation learning is the problem of teaching an agent to replicate expert policy from demonstrations when the underlying reward function is unavailable. This task becomes particularly challenging when the expert demonstrates a mixture of behaviors, often modeled by a discrete or continuous latent variable.
Prior work has addressed imitation learning in such mixture scenarios by recovering the underlying latent variables, in the context of both supervised learning (behavior cloning), and generative adversarial imitation learning (GAIL). In several robotic locomotion tasks, simulated in the MuJoCo platform, we observe that existing models fail in distinguishing and imitating different modes of behavior in both cases of discrete and continuous latent variables. To address this problem, we introduce a novel generative model for behavior cloning, in a mode-separating manner. We also integrate our model with GAIL, to achieve robustness to the problem of compounding error caused by unseen states. We show that our models outperform the state-of-the-art in aforementioned experiments.
Modeling label correlations implicitly through latent label encodings for multi-label text classification
Authors: Zhizhong Zeng, Yufen Liu, Wenpeng Gao, Baihong Li, Ting Zhang, Xinguo Yu, Zongkai Yang
Date: 2021-09-29 00:00:00
Tasks: Inductive Bias, Multi Label Text Classification, Multi-Label Text Classification, Representation Learning, text-classification, Text Classification

Multi-label text classification (MLTC) aims to assign a set of labels to each given document. Unlike single-label text classification methods that often focus on document representation learning, MLTC faces a key challenge of modeling label correlations due to complex label dependencies. Previous state-of-the-art works model label correlations explicitly. It lacks flexibility and is prone to introduce inductive bias that may not always hold, such as label-correlation simplification, sequencing label sets, and label-correlation overload. To address this issue, this paper uses latent label representations to model label correlations implicitly. Specifically, the proposed method concatenates a set of latent labels (instead of actual labels) to the text tokens, inputs them to BERT, then maps the contextual encodings of these latent labels to actual labels cooperatively. The correlations between labels, and between labels and the text are modeled indirectly through these latent-label  encodings and their correlations. Such latent and distributed correlation modeling can impose less a priori limits and provide more flexibility. The method is conceptually simple but quite effective. It improves the state-of-the-art results on two widely used benchmark datasets by a large margin. Further experiments demonstrate that its effectiveness lies in label-correlation utilization rather than document representation. Feature study reveals the importance of using latent label embeddings. It also reveals that contrary to the other token embeddings, the embeddings of these latent labels are sensitive to tasks; sometimes pretraining them can lead to significant performance loss rather than promotion. This result suggests that they are more related to task information (i.e., the actual labels) than the other tokens.
Fairness Guarantees under Demographic Shift
Authors: Stephen Giguere, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Scott Niekum, Bruno Castro da Silva
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Fairness

Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To address this challenge, recent machine learning algorithms have been designed to limit the likelihood such unfair behaviors will occur. However, these approaches typically assume the data used for training is representative of what will be encountered once the model is deployed, thus limiting their usefulness. In particular, if certain subgroups of the population become more or less probable after the model is deployed (a phenomenon we call demographic shift), the fair-ness assurances provided by prior algorithms are often invalid. We consider the impact of demographic shift and present a class of algorithms, called Shifty algorithms, that provide high-confidence behavioral guarantees that hold under demographic shift. Shifty is the first technique of its kind and demonstrates an effective strategy for designing algorithms to overcome the challenges demographic shift poses. We evaluate Shifty-ttest, an implementation of Shifty based on Student’s 𝑡-test, and, using a real-world data set of university entrance exams and subsequent student success, show that the models output by our algorithm avoid unfair bias under demo-graphic shift, unlike existing methods. Our experiments demonstrate that our algorithm’s high-confidence fairness guarantees are valid in practice and that our algorithm is an effective tool for training models that are fair when demographic shift occurs.
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data
Authors: Dongsun Kim, Sinwoong Yun, Jemin Lee, Eunbyung Park
Date: 2021-09-29 00:00:00
Tasks: Imputation, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, multi-agent reinforcement learning (MARL) adopts the centralized training with decentralized execution (CTDE) framework that trains agents using the data from all agents at a centralized server while each agent takes an action from its observation. In the real world, however, the training data from some agents can be unavailable at the centralized server due to practical reasons including communication failures and security attacks (e.g., data modification), which can slow down training and harm performance. Therefore, we consider the missing training data problem in MARL, and then propose the imputation assisted multiagent reinforcement learning (IA-MARL). IA-MARL consists of two steps: 1) the imputation of missing training data, which uses generative adversarial imputation networks (GAIN), and 2) the mask-based update of the networks, which trains each agent using the training data of corresponding agent, not missed over consecutive times. In the experimental results, we explore the effects of the data missing probability, the number of agents, and the number of pre-training episodes for GAIN on the performance of IA-MARL. We show IA-MARL outperforms a decentralized approach and even can achieve the performance of MARL without missing training data when sufficient imputation accuracy is supported. Our ablation study also shows that both the mask-based update and the imputation accuracy play important roles in achieving the high performance in IA-MARL.
Few-Shot Multi-task Learning via Implicit regularization
Authors: Dongsung Huh
Date: 2021-09-29 00:00:00
Tasks: Few-Shot Learning, Multi-Task Learning

Modern machine learning is highly data-intensive. Few-shot learning (FSL) aims to resolve this sample efficiency problem by learning from multiple tasks and quickly adapt to new tasks containing only a few samples. However,  FSL problems proves to be significantly more challenging and require more compute expensive process to optimize. In this work, we consider multi-task linear regression (MTLR) as a canonical problem for few-shot learning, and investigate the source of challenge of FSL. We find that the MTLR exhibits local minimum problems that are not present in single-task problem, and thus making the learning much more challenging. We also show that the problem can be resolved by  overparameterizing the  model by increasing both the width and depth of the linear network and initializing the weights with small values, exploiting the implicit regularization bias of gradient descent-based learning.
CareGraph: A Graph-based Recommender System for Diabetes Self-Care
Authors: Sirinart Tangruamsub, Karthik Kappaganthu, John O'Donovan, Anmol Madan
Date: 2021-09-29 00:00:00
Tasks: Click-Through Rate Prediction, Knowledge Graph Embeddings, Management, Recommendation Systems, text similarity

In this work, we build a knowledge graph that captures key attributes of content and notifications in a digital health platform for diabetes management.  We propose a Deep Neural Network-based recommender that uses the knowledge graph embeddings to recommend health nudges for maximizing engagement by combating the cold-start and sparsity problems. We use a leave-one-out approach to evaluate the model. We compare the proposed model performance with a text similarity and Deep-and-Cross Network-based approach as the baseline. The overall improvement in Click-Through-Rate prediction AUC for the Knowledge-Graph-based model was 11%. We also observe that our model improved the average AUC by 5% in cold-start situations.
Localized Persistent Homologies for more Effective Deep Learning
Authors: Doruk Oner, Adélie Garin, Mateusz Kozinski, Kathryn Hess, Pascal Fua
Date: 2021-09-29 00:00:00
Tasks: Deep Learning

Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results. However, existing methods are very global and ignore the location of topological features. In this paper, we introduce an approach that relies on a new filtration function to account for location during network training. We demonstrate experimentally on 2D images of roads and 3D image stacks of neural processes that networks trained in this manner are better at recovering the topology of the curvilinear structures they extract.
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations
Authors: Rongjie Huang, Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu, Zhou Zhao, Yi Ren
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Disentanglement, Representation Learning, Speaker Verification, Speech Synthesis, Translation

Learning generalizable speech representations for unseen samples in different domains has been a challenge with ever increasing importance to date. Although contrastive learning has been a prominent class of representation learning approaches, the state-of-the-art (SOTA) contrastive learning methods were found to have limited ability for learning unseen out-of-domain speech representations. This paper presents SynCLR, a synthesis framework for contrastive learning of speech representations that can be generalized over unseen domains. Specifically, instead of using data augmentation approach, SynCLR employs data synthesis for multi-view generation. To ensure a highly-varied conditional speech distribution in view generation, we design a novel diffusion-based speech synthesizer. A new contrastive loss is also proposed to construct multiple embedding spaces, each of which preserves view-sensitive information to reduce domain reliance for a better disentanglement. Our experiments showed that SynCLR outperformed the SOTA contrastive learning methods with a 17.2\% relative reduction of EER in speaker verification tested on an unseen speech corpus, and considerably reduced 50.8\% relative FIDs in a challenging speech-to-image translation task given out-of-domain test speeches.
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL
Authors: Chaoning Zhang, Kang Zhang, Chenshuang Zhang, Trung X. Pham, Chang D. Yoo, In So Kweon
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Self-Supervised Learning

Towards avoiding collapse in self-supervised learning (SSL), contrastive loss is widely used but often requires a large number of negative samples. Without negative samples yet achieving competitive performance one recent work~\cite{chen2021exploring} has attracted significant attention for providing a minimalist simple Siamese (SimSiam) method to avoid collapse. However, the reason for its success remains not fully clear and our investigation starts by revisiting the explanatory claims in the SimSiam. After refuting their claims, we introduce vector decomposition for analyzing the collapse based on the gradient analysis of $l_2$ normalized vector. This yields a unified perspective on how negative samples and SimSiam predictor alleviate collapse and promote dimensional de-correlation. Such a unified perspective comes timely for understanding the recent progress in SSL.
MA-CLIP: Towards Modality-Agnostic Contrastive Language-Image Pre-training
Authors: Haoxuan You, Luowei Zhou, Bin Xiao, Noel C Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, Lu Yuan
Date: 2021-09-29 00:00:00

Large-scale multimodal contrastive pretraining has demonstrated great utility to support high performance in a range of downstream tasks by mapping multiple modalities into a shared embedding space. Typically, this has employed separate encoders for each modality. However, recent work suggest that transformers can support learning across multiple modalities and allow knowledge sharing. Inspired by this, we investigate how to build a modality-shared Contrastive Language-Image Pre-training framework (MS-CLIP). More specifically, we question how many parameters of a transformer model can be shared across modalities during contrastive pre-training, and rigorously study architectural design choices that position the proportion of parameters shared along a spectrum. We observe that a mostly unified encoder for vision and language signals outperforms all other variations that separate more parameters. Additionally, we find that light-weight modality-specific parallel adapter modules further improve performance. Experimental results show that the proposed MS-CLIP outperforms OpenAI CLIP by 13\% relatively in zero-shot ImageNet classification (pre-trained on YFCC100M), while simultaneously supporting a reduction of parameters. In addition, our approach outperforms OpenAI CLIP by 1.6 points on a collection of 19 downstream vision tasks. Furthermore, we discover that sharing parameters leads to semantic concepts from different modalities being encoded more closely in the embedding space, facilitating the learning of common semantic structures (e.g., attention patterns) across modalities.