
Protecting Individual Interests across Clusters: Spectral Clustering with Guarantees
Studies related to fairness in machine learning have recently gained tra...
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Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
While deep learning is a powerful tool for natural language processing (...
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Stay Alive with Many Options: A Reinforcement Learning Approach for Autonomous Navigation
Hierarchical reinforcement learning approaches learn policies based on h...
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A Regret bound for Nonstationary MultiArmed Bandits with Fairness Constraints
The multiarmed bandits' framework is the most common platform to study ...
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Adversarial Context Aware Network Embeddings for Textual Networks
Representation learning of textual networks poses a significant challeng...
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Contradistinguisher: Applying Vapnik's Philosophy to Unsupervised Domain Adaptation
A complex combination of simultaneous supervisedunsupervised learning i...
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Networked MultiAgent Reinforcement Learning with Emergent Communication
MultiAgent Reinforcement Learning (MARL) methods find optimal policies ...
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A Statistical Model for Dynamic Networks with Neural Variational Inference
In this paper we propose a statistical model for dynamically evolving ne...
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Restricted Boltzmann Stochastic Block Model: A Generative Model for Networks with Attributes
In most practical contexts network indexed data consists not only of a d...
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Active Learning with Siamese Twins for Sequence Tagging
Deep learning, in general, and natural language processing methods, in p...
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CUDA: Contradistinguisher for Unsupervised Domain Adaptation
In this paper, we propose a simple model referred as Contradistinguisher...
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On Voting Strategies and Emergent Communication
Humans use language to collectively execute complex strategies in additi...
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Deep Discriminative Learning for Unsupervised Domain Adaptation
The primary objective of domain adaptation methods is to transfer knowle...
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Instancebased Inductive Deep Transfer Learning by CrossDataset Querying with Locality Sensitive Hashing
Supervised learning models are typically trained on a single dataset and...
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Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
Machine Learning has been the quintessential solution for many AI proble...
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Evolving Latent Space Model for Dynamic Networks
Networks observed in the real world like social networks, collaboration ...
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Amortized Inference and Learning in Latent Conditional Random Fields for WeaklySupervised Semantic Image Segmentation
Conditional random fields (CRFs) are commonly employed as a postprocess...
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Generative Adversarial Residual Pairwise Networks for One Shot Learning
Deep neural networks achieve unprecedented performance levels over many ...
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Attentive Recurrent Comparators
Rapid learning requires flexible representations to quickly adopt to new...
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Spectral Clustering via Graph Filtering: Consistency on the HighDimensional Stochastic Block Model
Spectral clustering is amongst the most popular methods for community de...
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Image Generation and Editing with Variational Info Generative AdversarialNetworks
Recently there has been an enormous interest in generative models for im...
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Deep Variational Inference Without PixelWise Reconstruction
Variational autoencoders (VAEs), that are built upon deep neural network...
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A Neural Architecture Mimicking Humans EndtoEnd for Natural Language Inference
In this work we use the recent advances in representation learning to pr...
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Variational methods for Conditional Multimodal Deep Learning
In this paper, we address the problem of conditional modality learning, ...
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Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
In a series of recent works, we have generalised the consistency results...
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On Gröbner Bases and Krull Dimension of Residue Class Rings of Polynomial Rings over Integral Domains
Given an ideal a in A[x_1, ..., x_n], where A is a Noetherian integral d...
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Consistency of Spectral Hypergraph Partitioning under Planted Partition Model
Hypergraph partitioning lies at the heart of a number of problems in mac...
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On Powerlaw Kernels, corresponding Reproducing Kernel Hilbert Space and Applications
The role of kernels is central to machine learning. Motivated by the imp...
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Ambedkar Dukkipati
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Associate Professor of Computer Science and Automation at Indian Institute of Science