Search Results for author:
Found 41 papers, 14 papers with code
Dependency-aware Maximum Likelihood Estimation for Active Learning
In this sequential process, each sample acquisition influences subsequent selections, causing dependencies among samples in the labeled set.
Learning Set Functions with Implicit Differentiation
Ou et al. (2022) introduce the problem of learning set functions from data generated by a so-called optimal subset oracle.
Exploring Token Pruning in Vision State Space Models
Inspired by the observations that the final prediction in vision transformers (ViTs) is only based on a subset of most informative tokens, we take the novel step of enhancing the efficiency of SSM-based vision models through token-based pruning.
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms.
Harm Mitigation in Recommender Systems under User Preference Dynamics
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content.
Fair Concurrent Training of Multiple Models in Federated Learning
We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
It consists of a parameter server and a possibly large collection of clients (e. g., in cross-device federated learning) that may operate in congested and changing environments.
T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge
Spectrum sharing allows different protocols of the same standard (e. g., 802. 11 family) or different standards (e. g., LTE and DVB) to coexist in overlapping frequency bands.
Online Submodular Maximization via Online Convex Optimization
We study monotone submodular maximization under general matroid constraints in the online setting.
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Specifically, in each round $t$, the link between the PS and client $i$ is active with probability $p_i^t$, which is $\textit{unknown}$ to both the PS and the clients.
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity.
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
Rehearsal-based approaches are a mainstay of continual learning (CL).
Explanations of Black-Box Models based on Directional Feature Interactions
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent.
Stochastic Submodular Maximization via Polynomial Estimators
In this paper, we study stochastic submodular maximization problems with general matroid constraints, that naturally arise in online learning, team formation, facility location, influence maximization, active learning and sensing objective functions.
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Adversarial pruning compresses models while preserving robustness.
SparCL: Sparse Continual Learning on the Edge
SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.
Differentially Private Regression with Unbounded Covariates
We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates.
AirNN: Neural Networks with Over-the-Air Convolution via Reconfigurable Intelligent Surfaces
In this paper, we design and implement the first-of-its-kind over-the-air convolution and demonstrate it for inference tasks in a convolutional neural network (CNN).
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.
Robust Regression via Model Based Methods
The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few.
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.
On the Sample Complexity of Rank Regression from Pairwise Comparisons
We consider a rank regression setting, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.
Machine Learning on Camera Images for Fast mmWave Beamforming
Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands.
Submodular Maximization via Taylor Series Approximation
We study submodular maximization problems with matroid constraints, in particular, problems where the objective can be expressed via compositions of analytic and multilinear functions.
Rate Allocation and Content Placement in Cache Networks
We introduce the problem of optimal congestion control in cache networks, whereby \emph{both} rate allocations and content placements are optimized \emph{jointly}.
Networking and Internet Architecture
Learn-Prune-Share for Lifelong Learning
In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.
Open-World Class Discovery with Kernel Networks
We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.
Bandits Under The Influence (Extended Version)
Our bandit algorithms are tailored precisely to recommendation scenarios where user interests evolve under social influence.
Iterative Spectral Method for Alternative Clustering
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition.
Deep Kernel Learning for Clustering
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data.
Accelerated Experimental Design for Pairwise Comparisons
Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size.
ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer.
Deep feature transfer between localization and segmentation tasks
In this paper, we propose a new pre-training scheme for U-net based image segmentation.
Learning Combinations of Sigmoids Through Gradient Estimation
In a nutshell, we estimate the gradient of the regression function at a set of random points, and cluster the estimated gradients.
Truthful Linear Regression
We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy.
Guess Who Rated This Movie: Identifying Users Through Subspace Clustering
It is often the case that, within an online recommender system, multiple users share a common account.
Privacy Tradeoffs in Predictive Analytics
Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data.
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization
Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads.
Learning Mixtures of Linear Classifiers
We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers.
Linear Regression from Strategic Data Sources
In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst.
From Small-World Networks to Comparison-Based Search
In short, a user searching for a target object navigates through a database in the following manner: the user is asked to select the object most similar to her target from a small list of objects.
