1 citations · 1 across the 29 of their papers we have counts for
31 papers
On Partial Optimality in QUBO via Quadratic Posiform Autarkies
Lucas Fabian Naumann, Bjoern Andres
Partially optimal assignments (also called persistencies) allow combinatorial optimization problems to be reduced by fixing a subset of variables to values consistent with at least…
Efficient Active Auditing of Multi-Group Fairness with Bias Probes
Ayoub Ajarra, Debabrota Basu
Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM) while controlling unfairness b…
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…
DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning
Yifan Wang, Debabrota Basu, Pierre Bourhis +2
Database Management Systems (DBMS) are crucial for efficient data management and access control, but their administration remains challenging for Database Administrators (DBAs). Tu…
Asymptotically Optimal Sequential Testing with Markovian Data
Alhad Sethi, Kavali Sofia Sagar, Shubhada Agrawal +2
We study one-sided and -correct sequential hypothesis testing for data generated by an ergodic, finite-state Markov chain. The null hypothesis is that the unknown transition mat…
Sequential Membership Inference Attacks
Thomas Michel, Debabrota Basu, Emilie Kaufmann
Modern AI models are not static. They go through multiple updates in their lifecycles. We propose to design Sequential Membership Inference (SeMI) attacks leading to tighter privac…
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…
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
Thomas Michel, Debabrota Basu, Emilie Kaufmann
We revisit Wald's celebrated Sequential Probability Ratio Test for sequential tests of two simple hypotheses, under privacy constraints. We propose DP-SPRT, a wrapper that can be c…
Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning
Guillaume Pourcel, Debabrota Basu, Maxence Ernoult +1
Equilibrium Propagation (EP) is a learning algorithm for training Energy-based Models (EBMs) on static inputs which leverages the variational description of their fixed points. Ext…
Optimal Regret of Bernoulli Bandits under Global Differential Privacy
Achraf Azize, Yulian Wu, Junya Honda +3
As sequential learning algorithms are increasingly applied to real life, ensuring data privacy while maintaining their utilities emerges as a timely question. In this context, regr…
Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing
Debabrota Basu, Debarshi Chanda
Resource-efficiently computing representations of probability distributions and the distances between them while only having access to the samples is a fundamental and useful probl…
Preference-based Pure Exploration
Apurv Shukla, Debabrota Basu
We study the preference-based pure exploration problem for bandits with vector-valued rewards. The rewards are ordered using a (given) preference cone and our goal is…
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
Emilio Jorge, Christos Dimitrakakis, Debabrota Basu
Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions…
Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs
Chao Han, Debabrota Basu, Michael Mangan +2
Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov D…
Active Fourier Auditor for Estimating Distributional Properties of ML Models
Ayoub Ajarra, Bishwamittra Ghosh, Debabrota Basu
With the pervasive deployment of Machine Learning (ML) models in real-world applications, verifying and auditing properties of ML models have become a central concern. In this work…
Testing Credibility of Public and Private Surveys through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in ge…
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…
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Naheed Anjum Arafat, Debabrota Basu, Yulia Gel +1
Capitalizing on the intuitive premise that shape characteristics are more robust to perturbations, we bridge adversarial graph learning with the emerging tools from computational t…