181 citations · 505 across the 54 of their papers we have counts for
17 papers · 1 filter
Obliviator Reveals the Cost of Nonlinear Guardedness in Concept Erasure
Ramin Akbari, Milad Afshari, Vishnu Naresh Boddeti
Concept erasure aims to remove unwanted attributes, such as social or demographic factors, from learned representations, while preserving their task-relevant utility. While the goa…
PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors
Sepehr Dehdashtian, Mashrur M. Morshed, Jacob H. Seidman +2
Synthetic image detectors (SIDs) are a key defense against the risks posed by the growing realism of images from text-to-image (T2I) models. Red teaming improves SID's effectivenes…
Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
Xuyang Li, Mahdi Masmoudi, Rami Gharbi +2
Parameterized partial differential equations (PDEs) underpin the mathematical modeling of complex systems in diverse domains, including engineering, healthcare, and physics. A cent…
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows
Mashrur M. Morshed, Vishnu Boddeti
Many real-world applications of flow-based generative models desire a diverse set of samples that cover multiple modes of the target distribution. However, the predominant approach…
CoInD: Enabling Logical Compositions in Diffusion Models
Sachit Gaudi, Gautam Sreekumar, Vishnu Boddeti
How can we learn generative models to sample data with arbitrary logical compositions of statistically independent attributes? The prevailing solution is to sample from distributio…
Compositional World Knowledge leads to High Utility Synthetic data
Sachit Gaudi, Gautam Sreekumar, Vishnu Boddeti
Machine learning systems struggle with robustness, under subpopulation shifts. This problem becomes especially pronounced in scenarios where only a subset of attribute combinations…
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Xuyang Li, Hamed Bolandi, Mahdi Masmoudi +3
Structural health monitoring (SHM) ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure…
AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE
Wei Ao, Vishnu Naresh Boddeti
Secure inference of deep convolutional neural networks (CNNs) under RNS-CKKS involves polynomial approximation of unsupported non-linear activation functions. However, existing app…
Spurious Correlations and Where to Find Them
Gautam Sreekumar, Vishnu Naresh Boddeti
Spurious correlations occur when a model learns unreliable features from the data and are a well-known drawback of data-driven learning. Although there are several algorithms propo…
Physics Informed Neural Network for Dynamic Stress Prediction
Hamed Bolandi, Gautam Sreekumar, Xuyang Li +2
Structural failures are often caused by catastrophic events such as earthquakes and winds. As a result, it is crucial to predict dynamic stress distributions during highly disrupti…
NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems
Xuyang Li, Hamed Bolandi, Talal Salem +2
Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters. Additionally, real-world structura…
Adversarial Representation Learning With Closed-Form Solvers
Bashir Sadeghi, Lan Wang, Vishnu Naresh Boddeti
Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time. Existing methods learn mode…
On Characterizing the Trade-off in Invariant Representation Learning
Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti
Many applications of representation learning, such as privacy preservation, algorithmic fairness, and domain adaptation, desire explicit control over semantic information being dis…
On the Global Optima of Kernelized Adversarial Representation Learning
Bashir Sadeghi, Runyi Yu, Vishnu Naresh Boddeti
Adversarial representation learning is a promising paradigm for obtaining data representations that are invariant to certain sensitive attributes while retaining the information ne…
Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach
Proteek Chandan Roy, Vishnu Naresh Boddeti
Image recognition systems have demonstrated tremendous progress over the past few decades thanks, in part, to our ability of learning compact and robust representations of images.…
Emergence of Selective Invariance in Hierarchical Feed Forward Networks
Dipan K. Pal, Vishnu Boddeti, Marios Savvides
Many theories have emerged which investigate how in- variance is generated in hierarchical networks through sim- ple schemes such as max and mean pooling. The restriction to max/me…
Local Binary Convolutional Neural Networks
Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides
We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motiva…