Search Results for author:
Found 18 papers, 10 papers with code
Tighter Bounds on the Information Bottleneck with Application to Deep Learning
These advancements strengthen the case for the IB and its variational approximations as a data modeling framework, and provide a simple method to significantly enhance the adversarial robustness of classifier DNNs.
Graph Neural Networks Use Graphs When They Shouldn't
We analyze the implicit bias of gradient-descent learning of GNNs and prove that when the ground truth function does not use the graphs, GNNs are not guaranteed to learn a solution that ignores the graph, even with infinite data.
The Case Against Explainability
Accordingly, this study carries some important policy ramifications, as it calls upon regulators and Machine Learning practitioners to reconsider the widespread pursuit of end-user Explainability and a right to explanation of AI systems.
TREE-G: Decision Trees Contesting Graph Neural Networks
When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability properties.
Ranked #1 on
Graph Classification
on HIV dataset
(Accuracy metric)
Inherent Inconsistencies of Feature Importance
The rapid advancement and widespread adoption of machine learning-driven technologies have underscored the practical and ethical need for creating interpretable artificial intelligence systems.
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Motivated by the fact that humans like some level of unpredictability or novelty, and might therefore get quickly bored when interacting with a stationary policy, we introduce a novel non-stationary bandit problem, where the expected reward of an arm is fully determined by the time elapsed since the arm last took part in a switch of actions.
Trees with Attention for Set Prediction Tasks
However, most machine learning algorithms are not designed to handle set structures and are limited to processing records of fixed size.
Robust Model Compression Using Deep Hypotheses
This leads to our Compact Robust Estimated Median Belief Optimization (CREMBO) algorithm for robust model compression.
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case
While it is common to make the distinction between local scores that focus on individual predictions and global scores that look at the contribution of a feature to the model, another important division distinguishes model scenarios, in which the goal is to understand predictions of a given model from natural scenarios, in which the goal is to understand a phenomenon such as a disease.
Algorithmic Copywriting: Automated Generation of Health-Related Advertisements to Improve their Performance
The marginal contribution of the generator model was, on average, 28\% lower than that of human-authored ads, while the translator model received, on average, 32\% more clicks than human-authored ads.
E-Gotsky: Sequencing Content using the Zone of Proximal Development
Vygotsky's notions of Zone of Proximal Development and Dynamic Assessment emphasize the importance of personalized learning that adapts to the needs and abilities of the learners and enables more efficient learning.
Human-Computer Interaction Computers and Society
Low Latency Privacy Preserving Inference
When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity.
Turning Lemons into Peaches using Secure Computation
However, we argue that in many of these situations, the problem is not the missing information but the computational challenge of obtaining it.
Computer Science and Game Theory Cryptography and Security
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models.
Smooth Sensitivity Based Approach for Differentially Private Principal Component Analysis
The problem of designing simpler and more efficient methods for this task has been raised as an open problem in \cite{kapralov2013differentially}.
DART: Dropouts meet Multiple Additive Regression Trees
Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice.
Crypto-Nets: Neural Networks over Encrypted Data
To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model.
Using Multiple Samples to Learn Mixture Models
In the mixture models problem it is assumed that there are $K$ distributions $\theta_{1},\ldots,\theta_{K}$ and one gets to observe a sample from a mixture of these distributions with unknown coefficients.
