1 citations · 1 across the 8 of their papers we have counts for
9 papers
Witness Set in Weak Visibility Polygons is Polynomial-Time Solvable
Udvas Das, Shouvik Mondal, Sasanka Roy
In the classical Art Gallery Problem (AGP), guards are placed in a polygon so that together they see every point. The Witness Set Problem (WSP), introduced by Amit, Mitchell, and P…
Perfectly Guarding Straits: Exact Algorithms for Weak Visibility Polygons
Shouvik Mondal, Udvas Das, Sasanka Roy
The Art Gallery Problem (AGP) asks for the fewest guards that see all of a simple polygon. It is -complete, hence NP-hard. We show that for a particular class of…
Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics
Vedant Palit, Udvas Das, Brahim Driss +1
As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn significant attention.…
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
Udvas Das, Waris Radji, Debabrota Basu +1
We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized pref…
Performative Policy Gradient: Optimality in Performative Reinforcement Learning
Debabrota Basu, Udvas Das, Brahim Driss +1
Post-deployment machine learning algorithms often influence the environments they act in, and thus shift the underlying dynamics that the standard reinforcement learning (RL) metho…
Witness Set in Monotone Polygons: Exact and Approximate
Udvas Das, Binayak Dutta, Satyabrata Jana +2
Given a simple polygon , two points and within are {\em visible} to each other if the line segment between and is contained in $\mathscr{…
FraPPE: Fast and Efficient Preference-based Pure Exploration
Udvas Das, Apurv Shukla, Debabrota Basu
Preference-based Pure Exploration (PrePEx) aims to identify with a given confidence level the set of Pareto optimal arms in a vector-valued (aka multi-objective) bandit, where the…
The Fair Game: Auditing & Debiasing AI Algorithms Over Time
Debabrota Basu, Udvas Das
An emerging field of AI, namely Fair Machine Learning (ML), aims to quantify different types of bias (also known as unfairness) exhibited in the predictions of ML algorithms, and t…
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints
Udvas Das, Debabrota Basu
Pure exploration in bandits formalises multiple real-world problems, such as tuning hyper-parameters or conducting user studies to test a set of items, where different safety, reso…