1 citations · 1 across the 10 of their papers we have counts for
12 papers
Auditing Fairness under Model Updates: Fundamental Complexity and Property-Preserving Updates
Ayoub Ajarra, Debabrota Basu
As machine learning models become increasingly embedded in societal infrastructure, auditing them for bias is of growing importance. However, in real-world deployments, auditing is…
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…
Test-time Verification via Optimal Transport: Coverage, ROC, & Sub-optimality
Arpan Mukherjee, Marcello Bullo, Debabrota Basu +1
While test-time scaling with verification has shown promise in improving the performance of large language models (LLMs), the role of the verifier and its imperfections remain unde…
Dimension Agnostic Testing of Survey Data Credibility through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Assessing whether a sample survey credibly represents the population is a critical question for ensuring the validity of downstream research. Generally, this problem reduces to est…
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…