research
∙
07/07/2022
Interactive Combinatorial Bandits: Balancing Competitivity and Complementarity
We study non-modular function maximization in the online interactive ban...
share
research
∙
01/31/2022
Submodularity In Machine Learning and Artificial Intelligence
In this manuscript, we offer a gentle review of submodularity and superm...
research
∙
08/06/2021
Independence Properties of Generalized Submodular Information Measures
Recently a class of generalized information measures was defined on sets...
research
∙
05/15/2021
An Effective Baseline for Robustness to Distributional Shift
Refraining from confidently predicting when faced with categories of inp...
research
∙
04/30/2021
Submodular Mutual Information for Targeted Data Subset Selection
With the rapid growth of data, it is becoming increasingly difficult to ...
research
∙
02/27/2021
PRISM: A Unified Framework of Parameterized Submodular Information Measures for Targeted Data Subset Selection and Summarization
With increasing data, techniques for finding smaller, yet effective subs...
research
∙
10/12/2020
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures
We study submodular information measures as a rich framework for generic...
research
∙
06/27/2020
Submodular Combinatorial Information Measures with Applications in Machine Learning
Information-theoretic quantities like entropy and mutual information hav...
research
∙
06/27/2020
Concave Aspects of Submodular Functions
Submodular Functions are a special class of set functions, which general...
research
∙
06/05/2019
Data Sketching for Faster Training of Machine Learning Models
Many machine learning problems reduce to the problem of minimizing an ex...
research
∙
05/27/2019
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Mixup zhang2017mixup is a recently proposed method for training deep neu...
research
∙
05/27/2019
Combating Label Noise in Deep Learning Using Abstention
We introduce a novel method to combat label noise when training deep neu...
research
∙
02/26/2019
A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems
We are motivated by large scale submodular optimization problems, where ...
research
∙
02/26/2019
Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs
In this paper, we investigate a class of submodular problems which in ge...
research
∙
06/01/2016
Scaling Submodular Maximization via Pruned Submodularity Graphs
We propose a new random pruning method (called "submodular sparsificatio...
research
∙
06/01/2016
Stream Clipper: Scalable Submodular Maximization on Stream
Applying submodular maximization in the streaming setting is nontrivial ...
research
∙
11/06/2015
Submodular Hamming Metrics
We show that there is a largely unexplored class of functions (positive ...
research
∙
06/22/2014
Divide-and-Conquer Learning by Anchoring a Conical Hull
We reduce a broad class of machine learning problems, usually addressed ...
research
∙
02/02/2014
Graph Cuts with Interacting Edge Costs - Examples, Approximations, and Algorithms
We study an extension of the classical graph cut problem, wherein we rep...
research
∙
06/13/2012
