Search Results for author:
Found 15 papers, 6 papers with code
Intergroup Bias in Smile Discrimination in Autism
We found both autism and non-autism groups rated genuine smiles more genuine than posed smiles and in-groups more genuine than out-groups.
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging.
Large-Scale Public Data Improves Differentially Private Image Generation Quality
In this work, we look at how to use generic large-scale public data to improve the quality of differentially private image generation in Generative Adversarial Networks (GANs), and provide an improved method that uses public data effectively.
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy.
Differentially Private Multi-Party Data Release for Linear Regression
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects.
Does Label Differential Privacy Prevent Label Inference Attacks?
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels.
Online Adaptation to Label Distribution Shift
Machine learning models often encounter distribution shifts when deployed in the real world.
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech, analyze natural language, infer preferences, rank options, etc.
Making Paper Reviewing Robust to Bid Manipulation Attacks
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states.
On Hiding Neural Networks Inside Neural Networks
Modern neural networks often contain significantly more parameters than the size of their training data.
TrojanNet: Exposing the Danger of Trojan Horse Attack on Neural Networks
The complexity of large-scale neural networks can lead to poor understanding of their internal details.
Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs).
Scalable Influence Maximization with General Marketing Strategies
In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$.
Social and Information Networks Data Structures and Algorithms
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in $\mathcal C$ according to the recursive teaching model.
