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Found 17 papers, 2 papers with code
Closing the Gap in the Trade-off between Fair Representations and Accuracy
The rapid developments of various machine learning models and their deployments in several applications has led to discussions around the importance of looking beyond the accuracies of these models.
Byzantine Spectral Ranking
We first show that the popular spectral ranking based Rank-Centrality algorithm, though optimal for the BTL model, does not perform well even when a small constant fraction of the voters are Byzantine.
Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality
Using the dual view, in this paper, we rethink the conventional interpretations of DNNs thereby explicitsing the implicit interpretability of DNNs.
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons
Exploiting our structural characterization, we propose \texttt{PairwiseBlockRank} - a pairwise ranking algorithm for this class.
A Theory of Tournament Representations
For any given tournament, we show a novel upper bound on the smallest representation dimension that depends on the least size of the number of unique nodes in any feedback arc set of the flip class associated with a tournament.
Sequential Ski Rental Problem
The learner has access to two sets of experts, one set who advise on the true cost of buying the ski and another set who advise on the length of the ski season.
Censored Semi-Bandits for Resource Allocation
The loss depends on two hidden parameters, one specific to the arm but independent of the resource allocation, and the other depends on the allocated resource.
SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems
The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey.
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
We study this novel setting by establishing its `equivalence' to Multiple-Play Multi-Armed Bandits(MP-MAB) and Combinatorial Semi-Bandits.
Learning Transferable Feature Representations Using Neural Networks
Generally it requires labeled data from the source and only unlabeled data from the target to learn such representations.
Ranking with Features: Algorithm and A Graph Theoretic Analysis
We present a new least squares based algorithm called fBTL-LS which we show requires much lesser than $O(n\log(n))$ pairs to obtain a good ranking -- precisely our new sample complexity bound is of $O(\alpha\log \alpha)$, where $\alpha$ denotes the number of `independent items' of the set, in general $\alpha << n$.
Lovasz Convolutional Networks
We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.
Provable Inductive Robust PCA via Iterative Hard Thresholding
The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse components, is a well-studied problem in machine learning.
Learning to Partition using Score Based Compatibilities
For one of the four objectives, we show $NP$ hardness under the score structure and give a $\frac{1}{2}$ approximation algorithm for which no constant approximation was known thus far.
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier
We study the problem of ranking a set of items from nonactively chosen pairwise preferences where each item has feature information with it.
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
Recent work on deriving $O(\log T)$ anytime regret bounds for stochastic dueling bandit problems has considered mostly Condorcet winners, which do not always exist, and more recently, winners defined by the Copeland set, which do always exist.
Online Decision-Making in General Combinatorial Spaces
Here we study a general setting where costs may be linear in any suitable low-dimensional vector representation of elements of the decision space.
