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Found 18 papers, 6 papers with code
RLSbench: Domain Adaptation Under Relaxed Label Shift
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
To tackle this problem, we first propose a two-layer bandit structure for auto tuning the exploration parameter and further generalize it to the Syndicated Bandits framework which can learn multiple hyper-parameters dynamically in contextual bandit environment.
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
We provide theoretical guarantees for our proposed algorithm and show by experiments that our proposed algorithm improves the robustness against various kinds of popular attacks.
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling
A natural way to resolve this problem is to apply online stochastic gradient descent (SGD) so that the per-step time and memory complexity can be reduced to constant with respect to $t$, but a contextual bandit policy based on online SGD updates that balances exploration and exploitation has remained elusive.
Multiscale Non-stationary Stochastic Bandits
Classic contextual bandit algorithms for linear models, such as LinUCB, assume that the reward distribution for an arm is modeled by a stationary linear regression.
Unsupervised Object Segmentation with Explicit Localization Module
In this paper, we propose a novel architecture that iteratively discovers and segments out the objects of a scene based on the image reconstruction quality.
SSE-PT: Sequential Recommendation Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with.
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Temporal Collaborative Ranking Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed us to make better use of the temporal ordering of items that each user has engaged with.
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we propose using Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, for exploiting deeper neighborhood information.
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.
On $L_2$-consistency of nearest neighbor matching
Biased sampling and missing data complicates statistical problems ranging from causal inference to reinforcement learning.
Distributed Cartesian Power Graph Segmentation for Graphon Estimation
We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models.
SQL-Rank: A Listwise Approach to Collaborative Ranking
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion.
Estimating Graphlet Statistics via Lifting
Exploratory analysis over network data is often limited by the ability to efficiently calculate graph statistics, which can provide a model-free understanding of the macroscopic properties of a network.
Trend Filtering on Graphs
We introduce a family of adaptive estimators on graphs, based on penalizing the $\ell_1$ norm of discrete graph differences.
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks.
Recovering Graph-Structured Activations using Adaptive Compressive Measurements
We study the localization of a cluster of activated vertices in a graph, from adaptively designed compressive measurements.
Identifying graph-structured activation patterns in networks
We consider the problem of identifying an activation pattern in a complex, large-scale network that is embedded in very noisy measurements.
