Search Results for author:
Found 12 papers, 6 papers with code
HITSnDIFFs: From Truth Discovery to Ability Discovery by Recovering Matrices with the Consecutive Ones Property
We call this problem "ability discovery" to emphasize the connection to and duality with the more well-studied problem of "truth discovery".
Towards Unbiased Exploration in Partial Label Learning
We consider learning a probabilistic classifier from partially-labelled supervision (inputs denoted with multiple possibilities) using standard neural architectures with a softmax as the final layer.
Beyond Equi-joins: Ranking, Enumeration and Factorization
Our approach achieves strong time and space complexity properties: with $n$ denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for every value of $k$, the $k$ top-ranked answers are returned in $\mathcal{O}(n \operatorname{polylog} n + k \log k)$ time.
Databases Data Structures and Algorithms
Tractable Orders for Direct Access to Ranked Answers of Conjunctive Queries
For each of the two problems, we give a decidable characterization (under conventional complexity assumptions) of the class of tractable lexicographic orders for every CQ without self-joins.
Databases Data Structures and Algorithms
Factorized Graph Representations for Semi-Supervised Learning from Sparse Data
We answer this question affirmatively and suggest a method called distant compatibility estimation that works even on extremely sparsely labeled graphs (e. g., 1 in 10, 000 nodes is labeled) in a fraction of the time it later takes to label the remaining nodes.
Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs
We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, re- turn as many of the top-ranked results as possible.
Social and Information Networks Databases Data Structures and Algorithms
The Linearization of Belief Propagation on Pairwise Markov Networks
Belief Propagation (BP) is a widely used approximation for exact probabilistic inference in graphical models, such as Markov Random Fields (MRFs).
Semi-Supervised Learning with Heterophily
We derive a family of linear inference algorithms that generalize existing graph-based label propagation algorithms by allowing them to propagate generalized assumptions about "attraction" or "compatibility" between classes of neighboring nodes (in particular those that involve heterophily between nodes where "opposites attract").
Approximate Lifted Inference with Probabilistic Databases
This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases.
Oblivious Bounds on the Probability of Boolean Functions
By performing several dissociations, one can transform a Boolean formula whose probability is difficult to compute, into one whose probability is easy to compute, and which is guaranteed to provide an upper or lower bound on the probability of the original formula by choosing appropriate probabilities for the dissociated variables.
Linearized and Single-Pass Belief Propagation
Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily ("birds of a feather flock together") or heterophily ("opposites attract").
Dissociation and Propagation for Approximate Lifted Inference with Standard Relational Database Management Systems
We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers.
