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.
MISA detects Trojan triggers in neural networks at inference time.
Evolutionary algorithm improves DNN watermarking with fewer false positives.
Backdoor attacks make models predict a specific class near triggers, smoothing their decision function.
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender…
New dynamic backdoor attacks bypass current defenses.
Paper defends LSTM-based text classification models from backdoor attacks.
Adversarial weight perturbations can inject backdoors into trained neural models.
Paper improves voice trigger detection for privacy-centric smart assistants.
New method defends against neural backdoors using generative modeling.
Study identifies two borrowing patterns in UK payday loan users.
Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are requir…
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly. The effectiveness in reducing communication thus heavily depends on the quality of the dynamics models used …
A data-driven approach predicts morphological development under structural instability.
New method shows fully-connected networks can learn convolutional structures from data.
Online financial markets can be represented as complex systems where trading dynamics can be captured and characterized at different resolutions and time scales. In this work, we develop a methodology based on non-negative tensor factorization (NTF) aimed at extracting and revealing the multi-timescale trading dynamics…
PHAZE framework uses zkML and hashing for fast, verifiable LHC trigger decisions.
Improved voice trigger detection in noisy environments.
PPPD framework extracts physical characterizations from stochastic mechanical systems.
New algorithm for contextual combinatorial bandits with probabilistic arm triggering.
This review assesses deep-learning methods for complex sequential data.
We demystify attention patterns in multi-head softmax models for linear data.
Paper improves CMAB regret bounds by reducing batch-size dependency.
SPARQ-SGD optimizes communication in decentralized SGD with event-triggered and compressed updates.
Study detects anomalies in robot vision data to predict hazards.
Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements,…
We consider the problem of building a state representation model for control, in a continual learning setting. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy …
BadGD identifies gradient descent vulnerabilities through strategic backdoor attacks.
Corporate defaults may be triggered by some major market news or events such as financial crises or collapses of major banks or financial institutions. With a view to develop a more realistic model for credit risk analysis, we introduce a new type of reduced-form intensity-based model that can incorporate the impacts o…
Graph-Triggered Bandits unify rested and restless bandits with graph-defined arm interactions.
Improved speech recognition for voice assistants by analyzing speech data.
Researchers quantify the relationship between feature depth and performance in deep neural networks.
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields. Among various point process models, Hawkes process and its variants attract many researchers in statistics and computer science these years because they capt…
We study combinatorial multi-armed bandit with probabilistically triggered arms (CMAB-T) and semi-bandit feedback. We resolve a serious issue in the prior CMAB-T studies where the regret bounds contain a possibly exponentially large factor of , where is the minimum positive probability that an arm is trigg…
This paper uses advanced math to price special insurance bonds.
Bayesian nonparametric Hawkes process model with EM-variational inference.
The problem of resource allocation of nonlinear networked control systems is investigated, where, unlike the well discussed case of triggering for stability, the objective is optimal triggering. An approximate dynamic programming approach is developed for solving problems with fixed final times initially and then it is…
In this paper we discuss the issue of computation of the bilateral credit valuation adjustment (CVA) under rating triggers, and in presence of ratings-linked margin agreements. Specifically, we consider collateralized OTC contracts, that are subject to rating triggers, between two parties -- an investor and a counterpa…
Analyzes gaming in federated learning systems and provides design principles.
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with va…
ET-GP-UCB optimizes time-varying functions without knowing change rates.
Deep learning models price convertible bonds with complex reset and call features.
Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger caus…
In financial markets, liquidity is not constant over time but exhibits strong seasonal patterns. In this article we consider a limit order book model that allows for time-dependent, deterministic depth and resilience of the book and determine optimal portfolio liquidation strategies. In a first model variant, we propos…
We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To uni…
We analyze the regret of combinatorial Thompson sampling (CTS) for the combinatorial multi-armed bandit with probabilistically triggered arms under the semi-bandit feedback setting. We assume that the learner has access to an exact optimization oracle but does not know the expected base arm outcomes beforehand. When th…
Efficiently models event-based data with general parametric kernels.
Study combines speaker verification and voice trigger detection in a single network.
In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history and the number of event types (i.e. dimensions). The proposed Low-Rank Hawkes Process (LRHP) framework introduces a low-rank approximation of the kernel m…