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Found 31 papers, 9 papers with code
Query Obfuscation by Semantic Decomposition
Our experimental results show that the proposed method can accurately reconstruct the search results for user queries, without compromising the privacy of the search engine users.
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem
Firstly, we show that a mixture distribution that has a greedy-applicable component is also greedy-applicable.
A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings
We propose a Neighbourhood-Aware Differential Privacy (NADP) mechanism considering the neighbourhood of a word in a pretrained static word embedding space to determine the minimal amount of noise required to guarantee a specified privacy level.
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering.
Online Task Assignment Problems with Reusable Resources
The key features of our problem are (1) an agent is reusable, i. e., an agent comes back to the market after completing the assigned task, (2) an agent may reject the assigned task to stay the market, and (3) a task may accommodate multiple agents.
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.
RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding
Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions
However, there is a gap of $\tilde{O}(\max(\sqrt{d}, \sqrt{k}))$ between the current best upper and lower bounds, where $d$ is the dimension of the feature vectors, $k$ is the number of the chosen arms in a round, and $\tilde{O}(\cdot)$ ignores the logarithmic factors.
Delay and Cooperation in Nonstochastic Linear Bandits
This paper offers a nearly optimal algorithm for online linear optimization with delayed bandit feedback.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e. g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features.
Automorphism groups of maps in linear time
The automorphism group of the original map can be reconstructed from the automorphism group of the uniform map in linear time.
Combinatorics Data Structures and Algorithms
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback
Our algorithm for non-stochastic settings has an oracle complexity of $\tilde{O}( T )$ and is the first algorithm that achieves both a regret bound of $\tilde{O}( \sqrt{T} )$ and an oracle complexity of $\tilde{O} ( \mathrm{poly} ( T ) )$, given only linear optimization oracles.
Improved Regret Bounds for Bandit Combinatorial Optimization
\textit{Bandit combinatorial optimization} is a bandit framework in which a player chooses an action within a given finite set $\mathcal{A} \subseteq \{ 0, 1 \}^d$ and incurs a loss that is the inner product of the chosen action and an unobservable loss vector in $\mathbb{R} ^ d$ in each round.
Query Obfuscation Semantic Decomposition
Our experimental results show that the proposed method can accurately reconstruct the search results for user queries, without compromising the privacy of the search engine users.
Are Girls Neko or Sh\=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding
Existing methods for learning KGEs can be seen as a two-stage process where (a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and (b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings.
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Online portfolio selection is a sequential decision-making problem in which a learner repetitively selects a portfolio over a set of assets, aiming to maximize long-term return.
Representation Learning on Graphs with Jumping Knowledge Networks
Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
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on PPI
Causal Bandits with Propagating Inference
In this setting, the arms are identified with interventions on a given causal graph, and the effect of an intervention propagates throughout all over the causal graph.
ClassiNet -- Predicting Missing Features for Short-Text Classification
We propose \emph{ClassiNet} -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem.
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation
Under these assumptions, we present polynomial-time sublinear-regret algorithms for the online sparse linear regression.
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks.
Using $k$-way Co-occurrences for Learning Word Embeddings
Co-occurrences between two words provide useful insights into the semantics of those words.
Joint Word Representation Learning using a Corpus and a Semantic Lexicon
For this purpose, we propose a joint word representation learning method that simultaneously predicts the co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.
Unsupervised Cross-Domain Word Representation Learning
Given a pair of \emph{source}-\emph{target} domains, we propose an unsupervised method for learning domain-specific word representations that accurately capture the domain-specific aspects of word semantics.
Embedding Semantic Relations into Word Representations
We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words.
Learning Word Representations from Relational Graphs
To evaluate the accuracy of the word representations learnt using the proposed method, we use the learnt word representations to solve semantic word analogy problems.
Generating Approximate Solutions to the TTP using a Linear Distance Relaxation
For larger n, we propose a novel "expander construction" that generates an approximate solution to the LD-TTP.
Scheduling Bipartite Tournaments to Minimize Total Travel Distance
This is an example of a bipartite tournament.
