22 citations · 36 across the 13 of their papers we have counts for
13 papers
Verifiable Random Sampling
Yeoh Wei Zhu, Soorya Rethinasamy, Anthony Alexiades Armenakas +3
Verifiable random functions (VRF) underpin a wide range of applications that require publicly verifiable evaluations of a pseudorandom function on a given input. However, once the…
A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
Georgios Papadopoulos, Shaltiel Eloul, Yash Satsangi +4
The loss landscape of Variational Quantum Neural Networks (VQNNs) is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more chall…
Applications of Certified Randomness
Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty +8
Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the ran…
Private, Auditable, and Distributed Ledger for Financial Institutes
Shaltiel Eloul, Yash Satsangi, Yeoh Wei Zhu +3
Distributed ledger technology offers several advantages for banking and finance industry, including efficient transaction processing and cross-party transaction reconciliation. The…
Estimating class separability of text embeddings with persistent homology
Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne +3
This paper introduces an unsupervised method to estimate the class separability of text datasets from a topological point of view. Using persistent homology, we demonstrate how tra…
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning
Yash Satsangi, Paniz Behboudian
A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning. The desired goals of an algorithm that can shape the learning of…
Topical: Learning Repository Embeddings from Source Code using Attention
Agathe Lherondelle, Varun Babbar, Yash Satsangi +3
This paper presents Topical, a novel deep neural network for repository level embeddings. Existing methods, reliant on natural language documentation or naive aggregation technique…
Learning to Be Cautious
Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai +2
A key challenge in the field of reinforcement learning is to develop agents that behave cautiously in novel situations. It is generally impossible to anticipate all situations that…
Useful Policy Invariant Shaping from Arbitrary Advice
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor +2
Reinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in com…
Real-Time Resource Allocation for Tracking Systems
Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek +1
Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting peop…
Exploiting Submodular Value Functions For Scaling Up Active Perception
Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek +1
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. While partially observable Markov decision proce…
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Yash Satsangi, Sungsu Lim, Shimon Whiteson +2
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty. For exa…
Probably Approximately Correct Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection
Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek
Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is c…