Bhagyashree Puranik - Academia.edu (original) (raw)

Uploads

Papers by Bhagyashree Puranik

IEEE journal on selected areas in information theory, 2024

arXiv (Cornell University), Nov 1, 2023

Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

With AI-based decisions playing an increasingly consequential role in our society, for example, i... more With AI-based decisions playing an increasingly consequential role in our society, for example, in our financial and criminal justice systems, there is a great deal of interest in designing algorithms conforming to application-specific notions of fairness. In this work, we ask a complementary question: can AI-based decisions be designed to dynamically influence the evolution of fairness in our society over the long term? To explore this question, we propose a framework for sequential decision-making aimed at dynamically influencing long-term societal fairness, illustrated via the problem of selecting applicants from a pool consisting of two groups, one of which is under-represented. We consider a dynamic model for the composition of the applicant pool, in which admission of more applicants from a group in a given selection round positively reinforces more candidates from the group to participate in future selection rounds. Under such a model, we show the efficacy of the proposed Fair-Greedy selection policy which systematically trades the sum of the scores of the selected applicants ("greedy") against the deviation of the proportion of selected applicants belonging to a given group from a target proportion ("fair"). In addition to experimenting on synthetic data, we adapt static real-world datasets on law school candidates and credit lending to simulate the dynamics of the composition of the applicant pool. We prove that the applicant pool composition converges to a target proportion set by the decision-maker when score distributions across the groups are identical. CCS CONCEPTS • Computing methodologies → Markov decision processes; Machine learning approaches.

2022 IEEE Wireless Communications and Networking Conference (WCNC)

A key goal of next generation networks is to scale hardware design and signal processing algorith... more A key goal of next generation networks is to scale hardware design and signal processing algorithms to mmWave and THz arrays with a large number of elements. Imperfect manufacturing and limitations of circuit design introduce variations in the gain and relative phase offset of transmit and receive array elements that must be compensated prior to beam formation for either communication or sensing. We propose a novel method for calibrating large arrays in the field by exploiting the sparsity of the spatial channel. While conventional calibration methods are susceptible to multipath components in the wireless channel, our approach is shown to be robust to multipath interference if the number of measurement locations is sufficiently large.

ArXiv, 2021

Machine learning models are known to be susceptible to adversarial attacks which can cause miscla... more Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composite hypothesis testing problem, jointly estimating the class of interest and the adversarial perturbation. While the GLRT approach is applicable to general multi-class hypothesis testing, we first evaluate it for binary hypothesis testing in white Gaussian noise under `∞ norm-bounded adversarial perturbations, for which a known minimax defense optimizing for the worst-case attack provides a benchmark. We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance...

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

Machine learning models are vulnerable to adversarial attacks that can often cause misclassificat... more Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under ℓ ∞ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, and observe that it yields a better robustness-accuracy trade-off under weaker attacks, depending on the values of signal components relative to the attack budget. We also observe that the GLRT defense generalizes naturally to more complex models for which optimal minimax classifiers are not known.

2017 IEEE International Symposium on Information Theory (ISIT), 2017

In this paper, we study the impact of locality on the decoding of binary cyclic codes under two a... more In this paper, we study the impact of locality on the decoding of binary cyclic codes under two approaches, namely ordered statistics decoding (OSD) and trellis decoding. Given a binary cyclic code having locality or availability, we suitably modify the OSD to obtain gains in terms of the Signal-To-Noise ratio, for a given reliability and essentially the same level of decoder complexity. With regard to trellis decoding, we show that careful introduction of locality results in the creation of cyclic subcodes having lower maximum state complexity. We also present a simple upper-bounding technique on the state complexity profile, based on the zeros of the code. Finally, it is shown how the decoding speed can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making use of a quick-look decoder that often returns the ML codeword.

2017 IEEE International Symposium on Information Theory (ISIT), 2017

With increase in scale, the number of node failures in a data center increases sharply. To ensure... more With increase in scale, the number of node failures in a data center increases sharply. To ensure availability of data, failure-tolerance schemes such as Reed-Solomon (RS) or more generally, Maximum Distance Separable (MDS) erasure codes are used. However, while MDS codes offer minimum storage overhead for a given amount of failure tolerance, they do not meet other practical needs of today's data centers. Although modern codes such as Minimum Storage Regenerating (MSR) codes are designed to meet these practical needs, they are available only in highly-constrained theoretical constructions, that are not sufficiently mature enough for practical implementation. We present Clay codes that extract the best from both worlds. Clay (short for Coupled-Layer) codes are MSR codes that offer a simplified construction for decoding/repair by using pairwise coupling across multiple stacked layers of any single MDS code. In addition, Clay codes provide the first practical implementation of an M...

With increase in scale, the number of node failures in a data center increases sharply. To ensure... more With increase in scale, the number of node failures in a data center increases sharply. To ensure availability of data, failure-tolerance schemes such as Reed-Solomon (RS) or more generally, Maximum Distance Separable (MDS) erasure codes are used. However, while MDS codes offer minimum storage overhead for a given amount of failure tolerance, they do not meet other practical needs of today's data centers. Although modern codes such as Minimum Storage Regenerating (MSR) codes are designed to meet these practical needs, they are available only in highly-constrained theoretical constructions, that are not sufficiently mature enough for practical implementation. We present Clay codes that extract the best from both worlds. Clay (short for Coupled-Layer) codes are MSR codes that offer a simplified construction for decoding/repair by using pairwise coupling across multiple stacked layers of any single MDS code. In addition, Clay codes provide the first practical implementation of an M...

IEEE Transactions on Communications, 2018

In this paper, we show how the presence of locality within a binary cyclic code can be exploited ... more In this paper, we show how the presence of locality within a binary cyclic code can be exploited to improve decoding performance and to reduce decoding complexity. We pursue two approaches. Under the first approach, we show how the Ordered Statistics Decoding (OSD) method can be modified by inserting a simple single round belief-propagation step at the start, that involves only the local codes. The resultant locality-aware OSD algorithm yields an appreciable signal-to-noise ratio (SNR) gain for a given level of reliability and essentially the same level of decoder complexity. Under the second, trellis decoding approach, we show that careful introduction of locality results in the creation of a cyclic subcode that possesses lower maximum state complexity. Additionally, we present a simple means of deriving an upper bound to the state complexity profile of any cyclic code, that is based only on the zeros of the code. Furthermore, we show how the decoding speed of either locality-aware OSD or trellis decoding can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making use of a quick-look decoder that often returns the maximum likelihood codeword.

arXiv: Machine Learning, Nov 16, 2020

Machine learning models are vulnerable to adversarial attacks that can often cause misclassificat... more Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under ℓ ∞ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, and observe that it yields a better robustness-accuracy trade-off under weaker attacks, depending on the values of signal components relative to the attack budget. We also observe that the GLRT defense generalizes naturally to more complex models for which optimal minimax classifiers are not known.

IEEE journal on selected areas in information theory, 2024

arXiv (Cornell University), Nov 1, 2023

Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

With AI-based decisions playing an increasingly consequential role in our society, for example, i... more With AI-based decisions playing an increasingly consequential role in our society, for example, in our financial and criminal justice systems, there is a great deal of interest in designing algorithms conforming to application-specific notions of fairness. In this work, we ask a complementary question: can AI-based decisions be designed to dynamically influence the evolution of fairness in our society over the long term? To explore this question, we propose a framework for sequential decision-making aimed at dynamically influencing long-term societal fairness, illustrated via the problem of selecting applicants from a pool consisting of two groups, one of which is under-represented. We consider a dynamic model for the composition of the applicant pool, in which admission of more applicants from a group in a given selection round positively reinforces more candidates from the group to participate in future selection rounds. Under such a model, we show the efficacy of the proposed Fair-Greedy selection policy which systematically trades the sum of the scores of the selected applicants ("greedy") against the deviation of the proportion of selected applicants belonging to a given group from a target proportion ("fair"). In addition to experimenting on synthetic data, we adapt static real-world datasets on law school candidates and credit lending to simulate the dynamics of the composition of the applicant pool. We prove that the applicant pool composition converges to a target proportion set by the decision-maker when score distributions across the groups are identical. CCS CONCEPTS • Computing methodologies → Markov decision processes; Machine learning approaches.

2022 IEEE Wireless Communications and Networking Conference (WCNC)

A key goal of next generation networks is to scale hardware design and signal processing algorith... more A key goal of next generation networks is to scale hardware design and signal processing algorithms to mmWave and THz arrays with a large number of elements. Imperfect manufacturing and limitations of circuit design introduce variations in the gain and relative phase offset of transmit and receive array elements that must be compensated prior to beam formation for either communication or sensing. We propose a novel method for calibrating large arrays in the field by exploiting the sparsity of the spatial channel. While conventional calibration methods are susceptible to multipath components in the wireless channel, our approach is shown to be robust to multipath interference if the number of measurement locations is sufficiently large.

ArXiv, 2021

Machine learning models are known to be susceptible to adversarial attacks which can cause miscla... more Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composite hypothesis testing problem, jointly estimating the class of interest and the adversarial perturbation. While the GLRT approach is applicable to general multi-class hypothesis testing, we first evaluate it for binary hypothesis testing in white Gaussian noise under `∞ norm-bounded adversarial perturbations, for which a known minimax defense optimizing for the worst-case attack provides a benchmark. We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance...

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021

Machine learning models are vulnerable to adversarial attacks that can often cause misclassificat... more Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under ℓ ∞ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, and observe that it yields a better robustness-accuracy trade-off under weaker attacks, depending on the values of signal components relative to the attack budget. We also observe that the GLRT defense generalizes naturally to more complex models for which optimal minimax classifiers are not known.

2017 IEEE International Symposium on Information Theory (ISIT), 2017

In this paper, we study the impact of locality on the decoding of binary cyclic codes under two a... more In this paper, we study the impact of locality on the decoding of binary cyclic codes under two approaches, namely ordered statistics decoding (OSD) and trellis decoding. Given a binary cyclic code having locality or availability, we suitably modify the OSD to obtain gains in terms of the Signal-To-Noise ratio, for a given reliability and essentially the same level of decoder complexity. With regard to trellis decoding, we show that careful introduction of locality results in the creation of cyclic subcodes having lower maximum state complexity. We also present a simple upper-bounding technique on the state complexity profile, based on the zeros of the code. Finally, it is shown how the decoding speed can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making use of a quick-look decoder that often returns the ML codeword.

2017 IEEE International Symposium on Information Theory (ISIT), 2017

With increase in scale, the number of node failures in a data center increases sharply. To ensure... more With increase in scale, the number of node failures in a data center increases sharply. To ensure availability of data, failure-tolerance schemes such as Reed-Solomon (RS) or more generally, Maximum Distance Separable (MDS) erasure codes are used. However, while MDS codes offer minimum storage overhead for a given amount of failure tolerance, they do not meet other practical needs of today's data centers. Although modern codes such as Minimum Storage Regenerating (MSR) codes are designed to meet these practical needs, they are available only in highly-constrained theoretical constructions, that are not sufficiently mature enough for practical implementation. We present Clay codes that extract the best from both worlds. Clay (short for Coupled-Layer) codes are MSR codes that offer a simplified construction for decoding/repair by using pairwise coupling across multiple stacked layers of any single MDS code. In addition, Clay codes provide the first practical implementation of an M...

With increase in scale, the number of node failures in a data center increases sharply. To ensure... more With increase in scale, the number of node failures in a data center increases sharply. To ensure availability of data, failure-tolerance schemes such as Reed-Solomon (RS) or more generally, Maximum Distance Separable (MDS) erasure codes are used. However, while MDS codes offer minimum storage overhead for a given amount of failure tolerance, they do not meet other practical needs of today's data centers. Although modern codes such as Minimum Storage Regenerating (MSR) codes are designed to meet these practical needs, they are available only in highly-constrained theoretical constructions, that are not sufficiently mature enough for practical implementation. We present Clay codes that extract the best from both worlds. Clay (short for Coupled-Layer) codes are MSR codes that offer a simplified construction for decoding/repair by using pairwise coupling across multiple stacked layers of any single MDS code. In addition, Clay codes provide the first practical implementation of an M...

IEEE Transactions on Communications, 2018

In this paper, we show how the presence of locality within a binary cyclic code can be exploited ... more In this paper, we show how the presence of locality within a binary cyclic code can be exploited to improve decoding performance and to reduce decoding complexity. We pursue two approaches. Under the first approach, we show how the Ordered Statistics Decoding (OSD) method can be modified by inserting a simple single round belief-propagation step at the start, that involves only the local codes. The resultant locality-aware OSD algorithm yields an appreciable signal-to-noise ratio (SNR) gain for a given level of reliability and essentially the same level of decoder complexity. Under the second, trellis decoding approach, we show that careful introduction of locality results in the creation of a cyclic subcode that possesses lower maximum state complexity. Additionally, we present a simple means of deriving an upper bound to the state complexity profile of any cyclic code, that is based only on the zeros of the code. Furthermore, we show how the decoding speed of either locality-aware OSD or trellis decoding can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making use of a quick-look decoder that often returns the maximum likelihood codeword.

arXiv: Machine Learning, Nov 16, 2020

Machine learning models are vulnerable to adversarial attacks that can often cause misclassificat... more Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under ℓ ∞ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, and observe that it yields a better robustness-accuracy trade-off under weaker attacks, depending on the values of signal components relative to the attack budget. We also observe that the GLRT defense generalizes naturally to more complex models for which optimal minimax classifiers are not known.