52 citations · 314 across the 32 of their papers we have counts for
14 papers · 1 filter
Neural Basis Models for Interpretability
Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically b…
Scalable Interpretability via Polynomials
Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan
Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they…
Adaptive Methods for Aggregated Domain Generalization
Xavier Thomas, Dhruv Mahajan, Alex Pentland +1
Domain generalization involves learning a classifier from a heterogeneous collection of training sources such that it generalizes to data drawn from similar unknown target domains,…
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation
Abhimanyu Dubey, Alex Pentland
Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic tr…
Differentially-Private Federated Linear Bandits
Abhimanyu Dubey, Alex Pentland
The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextu…
Cooperative Multi-Agent Bandits with Heavy Tails
Abhimanyu Dubey, Alex Pentland
We study the heavy-tailed stochastic bandit problem in the cooperative multi-agent setting, where a group of agents interact with a common bandit problem, while communicating on a…
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Abhimanyu Dubey, Alex Pentland
Cooperative multi-agent decision making involves a group of agents cooperatively solving learning problems while communicating over a network with delays. In this paper, we conside…
Smaller Models, Better Generalization
Mayank Sharma, Suraj Tripathi, Abhimanyu Dubey +3
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology. Convolutional Neural Networks that perform various vision tasks wi…
Thompson Sampling on Symmetric -Stable Bandits
Abhimanyu Dubey, Alex Pentland
Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance. In this pap…
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning
Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey +4
A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A…
Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions
Shayne O'Brien, Matt Groh, Abhimanyu Dubey
The true distribution parameterizations of commonly used image datasets are inaccessible. Rather than designing metrics for feature spaces with unknown characteristics, we propose…
No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar +2
We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still…
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey +3
In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a…
Learning Neural Network Classifiers with Low Model Complexity
Jayadeva, Himanshu Pant, Mayank Sharma +5
Modern neural network architectures for large-scale learning tasks have substantially higher model complexities, which makes understanding, visualizing and training these architect…