arXiv subjects

Kiran Karra

Publications and source records attributed to Kiran Karra.

At least 19 recordsLinked to original sources

arXiv2025

Backdoors in DRL: Four Environments Focusing on In-distribution Triggers

Backdoor attacks, or trojans, pose a security risk by concealing undesirable behavior in deep neural network models. Open-source neural networks are downloaded from the internet daily, possibly containing backdoors, and third-party model developers are common. To advance research on backdoor attack mitigation, we develop several trojans for deep reinforcement learning (DRL) agents. We focus on in-distribution triggers, which occur within the agent's natural data distribution, since they pose a more significant security threat than out-of-distribution triggers due to their ease of activation by the attacker during model deployment. We implement backdoor attacks in four reinforcement learning (RL) environments: LavaWorld, Randomized LavaWorld, Colorful Memory, and Modified Safety Gymnasium. We train various models, both clean and backdoored, to characterize these attacks. We find that in-distribution triggers can require additional effort to implement and be more challenging for models to learn, but are nevertheless viable threats in DRL even using basic data poisoning attacks.

cs.LG↗

arXiv2025

Investigating the Treacherous Turn in Deep Reinforcement Learning

The Treacherous Turn refers to the scenario where an artificial intelligence (AI) agent subtly, and perhaps covertly, learns to perform a behavior that benefits itself but is deemed undesirable and potentially harmful to a human supervisor. During training, the agent learns to behave as expected by the human supervisor, but when deployed to perform its task, it performs an alternate behavior without the supervisor there to prevent it. Initial experiments applying DRL to an implementation of the A Link to the Past example do not produce the treacherous turn effect naturally, despite various modifications to the environment intended to produce it. However, in this work, we find the treacherous behavior to be reproducible in a DRL agent when using other trojan injection strategies. This approach deviates from the prototypical treacherous turn behavior since the behavior is explicitly trained into the agent, rather than occurring as an emergent consequence of environmental complexity or poor objective specification. Nonetheless, these experiments provide new insights into the challenges of producing agents capable of true treacherous turn behavior.

cs.LG↗

arXiv2024

Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics

The dynamics of a power system with a significant presence of renewable energy resources are growing increasingly nonlinear. This nonlinearity is a result of the intermittent nature of these resources and the switching behavior of their power electronic devices. Therefore, it is crucial to address these nonlinearity in the blind source separation methods. In this paper, we propose a blind source separation of a linear mixture of dependent sources based on copula statistics that measure the non-linear dependence between source component signals structured as copula density functions. The source signals are assumed to be stationary. The method minimizes the Kullback-Leibler divergence between the copula density functions of the estimated sources and of the dependency structure. The proposed method is applied to data obtained from the time-domain analysis of the classical 11-Bus 4-Machine system. Extensive simulation results demonstrate that the proposed method based on copula statistics converges faster and outperforms the state-of-the-art blind source separation method for dependent sources in terms of interference-to-signal ratio.

eess.SP↗

arXiv2023

Smoothening block rewards: How much should miners pay for mining pools?

The rewards a blockchain miner earns vary with time. Most of the time is spent mining without receiving any rewards, and only occasionally the miner wins a block and earns a reward. Mining pools smoothen the stochastic flow of rewards, and in the ideal case, provide a steady flow of rewards over time. Smooth block rewards allow miners to choose an optimal mining power growth strategy that will result in a higher reward yield for a given investment. We quantify the economic advantage for a given miner of having smooth rewards, and use this to define a maximum percentage of rewards that a miner should be willing to pay for the mining pool services.

cs.CR↗

arXiv2023

An Agent-Based Model Framework for Utility-Based Cryptoeconomies

In this paper, we outline a framework for modeling utility-based blockchain-enabled economic systems using Agent Based Modeling (ABM). Our approach is to model the supply dynamics based on metrics of the cryptoeconomy. We then build autonomous agents that make decisions based on those metrics. Those decisions, in turn, impact the metrics in the next time-step, creating a closed loop that models the evolution of cryptoeconomies over time. We apply this framework as a case-study to Filecoin, a decentralized blockchain-based storage network. We perform several experiments that explore the effect of different strategies, capitalization, and external factors to agent rewards, that highlight the efficacy of our approach to modeling blockchain based cryptoeconomies.

cs.CR↗

arXiv2022

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity

The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e.g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions). However, these one-size-fits-all models are typically optimized for average case performance, encouraging them to achieve high performance in nominal conditions but exposing them to unexpected behavior in challenging or rare contexts. To address this concern, we develop a new method for training context-dependent models. We extend Bridge-Mode Connectivity (BMC) (Garipov et al., 2018) to train an infinite ensemble of models over a continuous measure of context such that we can sample model parameters specifically tuned to the corresponding evaluation context. We explore the definition of context in image classification tasks through multiple lenses including changes in the risk profile, long-tail image statistics/appearance, and context-dependent distribution shift. We develop novel extensions of the BMC optimization for each of these cases and our experiments demonstrate that model performance can be successfully tuned to context in each scenario.

cs.CV↗

arXiv2022

SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks

Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure. In this work, we harness unsupervised data augmentation (UDA), an SSL technique, to mitigate backdoor or Trojan attacks on deep neural networks. We show that UDA is more effective at removing trojans than current state-of-the-art methods for both feature space and point triggers, over a range of model architectures, trojans, and data quantities provided for trojan removal. These results demonstrate that UDA is both an effective and practical approach to mitigating the effects of backdoors on neural networks.

cs.LG↗

arXiv2021

Machine Learning aided Crop Yield Optimization

We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We empirically show that DRL algorithms may be useful in discovering new policies and approaches to help optimize crop yield, while simultaneously minimizing constraining factors such as water and fertilizer usage. We propose that this hybrid plant modeling and data-driven approach for discovering new strategies to optimize crop yield may help address upcoming global food demands due to population expansion and climate change.

cs.LG↗

arXiv2021

Speaker Diarization using Two-pass Leave-One-Out Gaussian PLDA Clustering of DNN Embeddings

Many modern systems for speaker diarization, such as the recently-developed VBx approach, rely on clustering of DNN speaker embeddings followed by resegmentation. Two problems with this approach are that the DNN is not directly optimized for this task, and the parameters need significant retuning for different applications. We have recently presented progress in this direction with a Leave-One-Out Gaussian PLDA (LGP) clustering algorithm and an approach to training the DNN such that embeddings directly optimize performance of this scoring method. This paper presents a new two-pass version of this system, where the second pass uses finer time resolution to significantly improve overall performance. For the Callhome corpus, we achieve the first published error rate below 4% without any task-dependent parameter tuning. We also show significant progress towards a robust single solution for multiple diarization tasks.

eess.AS↗

arXiv2021

Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers

In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions the model will be trained on and deployed in. We outline a simple procedure for embedding these, and other, triggers in deep reinforcement learning agents following a multi-task learning paradigm, and demonstrate in three common reinforcement learning environments. We believe that this work has important implications for the security of deep learning models.

cs.LG↗

arXiv2021

On the Effect of Suboptimal Estimation of Mutual Information in Feature Selection and Classification

This paper introduces a new property of estimators of the strength of statistical association, which helps characterize how well an estimator will perform in scenarios where dependencies between continuous and discrete random variables need to be rank ordered. The new property, termed the estimator response curve, is easily computable and provides a marginal distribution agnostic way to assess an estimator's performance. It overcomes notable drawbacks of current metrics of assessment, including statistical power, bias, and consistency. We utilize the estimator response curve to test various measures of the strength of association that satisfy the data processing inequality (DPI), and show that the CIM estimator's performance compares favorably to kNN, vME, AP, and H_{MI} estimators of mutual information. The estimators which were identified to be suboptimal, according to the estimator response curve, perform worse than the more optimal estimators when tested with real-world data from four different areas of science, all with varying dimensionalities and sizes.

stat.ML↗

arXiv2020

Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation

The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantify the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method.

eess.SY↗

arXiv2020

The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models

In this paper, we introduce the TrojAI software framework, an open source set of Python tools capable of generating triggered (poisoned) datasets and associated deep learning (DL) models with trojans at scale. We utilize the developed framework to generate a large set of trojaned MNIST classifiers, as well as demonstrate the capability to produce a trojaned reinforcement-learning model using vector observations. Results on MNIST show that the nature of the trigger, training batch size, and dataset poisoning percentage all affect successful embedding of trojans. We test Neural Cleanse against the trojaned MNIST models and successfully detect anomalies in the trained models approximately $18\%$ of the time. Our experiments and workflow indicate that the TrojAI software framework will enable researchers to easily understand the effects of various configurations of the dataset and training hyperparameters on the generated trojaned deep learning model, and can be used to rapidly and comprehensively test new trojan detection methods.

cs.LG↗

arXiv2018

An Empirical Assessment of the Complexity and Realism of Synthetic Social Contact Networks

We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populations from several sources, including the Census, transportation surveys, and geographical data. The resulting networks represent an approximation of daily or weekly human interaction. Our results indicate that the synthetically generated graphs according to our methodology are closer to the real world graphs, as measured across multiple structural measures, than a range of stylized graphs generated using common network models from the literature.

cs.SI↗

arXiv2018

Copula Index for Detecting Dependence and Monotonicity between Stochastic Signals

This paper introduces a nonparametric copula-based index for detecting the strength and monotonicity structure of linear and nonlinear statistical dependence between pairs of random variables or stochastic signals. Our index, termed Copula Index for Detecting Dependence and Monotonicity (CIM), satisfies several desirable properties of measures of association, including Renyi's properties, the data processing inequality (DPI), and consequently self-equitability. Synthetic data simulations reveal that the statistical power of CIM compares favorably to other state-of-the-art measures of association that are proven to satisfy the DPI. Simulation results with real-world data reveal the CIM's unique ability to detect the monotonicity structure among stochastic signals to find interesting dependencies in large datasets. Additionally, simulations show that the CIM shows favorable performance to estimators of mutual information when discovering Markov network structure.

stat.ML↗

arXiv2017

Learning Approximate Neural Estimators for Wireless Channel State Information

Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large computationally efficient artificial neural network models capable of learning non-linear function mappings which provide compact and accurate estimates. For single carrier PSK modulation, we explore the accuracy and computational complexity of such estimators compared with the current gold-standard analytically derived alternatives. We compare performance in various wireless operating conditions and consider the trade offs between the two different classes of systems. Our results show the learned estimators can provide improvements in areas such as short-time estimation and estimation under non-trivial real world channel conditions such as fading or other non-linear hardware or propagation effects.

cs.LG↗

arXiv2016

A Copula Statistic for Measuring Nonlinear Multivariate Dependence

A new index based on empirical copulas, termed the Copula Statistic (CoS), is introduced for assessing the strength of multivariate dependence and for testing statistical independence. New properties of the copulas are proved. They allow us to define the CoS in terms of a relative distance function between the empirical copula, the Fréchet-Hoeffding bounds and the independence copula. Monte Carlo simulations reveal that for large sample sizes, the CoS is approximately normal. This property is utilised to develop a CoS-based statistical test of independence against various noisy functional dependencies. It is shown that this test exhibits higher statistical power than the Total Information Coefficient (TICe), the Distance Correlation (dCor), the Randomized Dependence Coefficient (RDC), and the Copula Correlation (Ccor) for monotonic and circular functional dependencies. Furthermore, the R2-equitability of the CoS is investigated for estimating the strength of a collection of functional dependencies with additive Gaussian noise. Finally, the CoS is applied to a real stock market data set from which we infer that a bivariate analysis is insufficient to unveil multivariate dependencies and to two gene expression data sets of the Yeast and of the E. Coli, which allow us to demonstrate the good performance of the CoS.

math.ST↗

arXiv2016

Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention

We address the problem of learning efficient and adaptive ways to communicate binary information over an impaired channel. We treat the problem as reconstruction optimization through impairment layers in a channel autoencoder and introduce several new domain-specific regularizing layers to emulate common channel impairments. We also apply a radio transformer network based attention model on the input of the decoder to help recover canonical signal representations. We demonstrate some promising initial capacity results from this architecture and address several remaining challenges before such a system could become practical.

cs.LG↗