A Novel Strategy Adaptive Genetic Algorithm with Greedy Local Search for the Permutation Flowshop Scheduling Problem. - Srinjoy Ganguly; Swahum Mukherjee; Debabrota Basu; Swagatam Das (SEMCCO, 2012)

A Novel Improved Discrete ABC Algorithm for Manpower Scheduling Problem in Remanufacturing. - Debabrota Basu; Shantanab Debchoudhury; Kaizhou Gao; Ponnuthurai Nagaratnam Suganthan (SEMCCO (1), 2013)

Load Information Based Priority Dependant Heuristic for Manpower Scheduling Problem in Remanufacturing. - Shantanab Debchoudhury; Debabrota Basu; Kaizhou Gao; Ponnuthurai Nagaratnam Suganthan (SEMCCO (1), 2013)

Multipopulation-Based Differential Evolution with Speciation-Based Response to Dynamic Environments. - Souvik Kundu 001; Debabrota Basu; Sheli Sinha Chaudhuri (SEMCCO (1), 2013)

A differential evolution based adaptive neural Type-2 Fuzzy inference system for classification of motor imagery EEG signals. - Debabrota Basu; Saugat Bhattacharyya; Dwaipayan Sardar; Amit Konar; D. N. Tibarewala; Atulya K. Nagar (FUZZ-IEEE, 2014)

A Spatially Informative Optic Flow Model of Bee Colony With Saccadic Flight Strategy for Global Optimization. - Swagatam Das; Subhodip Biswas; Bijaya K. Panigrahi; Souvik Kundu 001; Debabrota Basu (IEEE Trans. Cybern., 2014)

Cost-Model Oblivious Database Tuning with Reinforcement Learning. - Debabrota Basu; Qian Lin 002; Weidong Chen 004; Hoang Tam Vo; Zihong Yuan; Pierre Senellart; Stéphane Bressan (DEXA (1), 2015)

Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm. - Saugat Bhattacharyya; Debabrota Basu; Amit Konar; D. N. Tibarewala (Robotics Auton. Syst., 2015)

Top-k Queries Over Uncertain Scores. - Qing Liu 020; Debabrota Basu; Talel Abdessalem; Stéphane Bressan (OTM Conferences, 2016)

Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning. - Debabrota Basu; Qian Lin 002; Weidong Chen 004; Hoang Tam Vo; Zihong Yuan; Pierre Senellart; Stéphane Bressan (Trans. Large Scale Data Knowl. Centered Syst., 2016)

How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough. - Qing Liu 020; Debabrota Basu; Shruti Goel; Talel Abdessalem; Stéphane Bressan (DEXA (2), 2017)

Differential Privacy for Regularised Linear Regression. - Ashish Dandekar; Debabrota Basu; Stéphane Bressan (DEXA (2), 2018)

BelMan: Bayesian Bandits on the Belief-Reward Manifold. - Debabrota Basu; Pierre Senellart; Stéphane Bressan (CoRR, 2018)

Privacy as a Service: Publishing Data and Models. - Ashish Dandekar; Debabrota Basu; Thomas Kister; Geong Sen Poh; Jia Xu 006; Stéphane Bressan (DASFAA (3), 2019)

Topological Data Analysis with epsilon ϵ -net Induced Lazy Witness Complex. - Naheed Anjum Arafat; Debabrota Basu; Stéphane Bressan (DEXA (2), 2019)

Differentially Private Non-parametric Machine Learning as a Service. - Ashish Dandekar; Debabrota Basu; Stéphane Bressan (DEXA (1), 2019)

BelMan: An Information-Geometric Approach to Stochastic Bandits. - Debabrota Basu; Pierre Senellart; Stéphane Bressan (ECML/PKDD (3), 2019)

Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost? - Debabrota Basu; Christos Dimitrakakis; Aristide C. Y. Tossou (CoRR, 2019)

Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities. - Aristide C. Y. Tossou; Debabrota Basu; Christos Dimitrakakis (CoRR, 2019)

Topological Data Analysis with ε-net Induced Lazy Witness Complex. - Naheed Anjum Arafat; Debabrota Basu; Stéphane Bressan (CoRR, 2019)

Near-optimal Reinforcement Learning using Bayesian Quantiles. - Aristide C. Y. Tossou; Debabrota Basu; Christos Dimitrakakis (CoRR, 2019)

Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines. - Debabrota Basu; Xiayang Wang; Yang Hong 007; Haibo Chen 001; Stéphane Bressan (IEEE Trans. Parallel Distributed Syst., 2019)

Bayesian Reinforcement Learning via Deep, Sparse Sampling. - Divya Grover; Debabrota Basu; Christos Dimitrakakis (AISTATS, 2020)

Construction and Random Generation of Hypergraphs with Prescribed Degree and Dimension Sequences. - Naheed Anjum Arafat; Debabrota Basu; Laurent Decreusefond; Stéphane Bressan (DEXA (2), 2020)

Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning. - Emilio Jorge; Hannes Eriksson; Christos Dimitrakakis; Debabrota Basu; Divya Grover (ICBINB@NeurIPS, 2020)

Inferential Induction: Joint Bayesian Estimation of MDPs and Value Functions. - Christos Dimitrakakis; Hannes Eriksson; Emilio Jorge; Divya Grover; Debabrota Basu (CoRR, 2020)

ε-net Induced Lazy Witness Complexes on Graphs. - Naheed Anjum Arafat; Debabrota Basu; Stéphane Bressan (CoRR, 2020)

Justicia: A Stochastic SAT Approach to Formally Verify Fairness. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (AAAI, 2021)

Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM. - Shirin Tavara; Alexander Schliep; Debabrota Basu (PKDD/ECML Workshops (2), 2021)

Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning. - Junxiong Wang; Immanuel Trummer; Debabrota Basu (SIGMOD Conference, 2021)

Fair Set Selection: Meritocracy and Social Welfare. - Thomas Kleine Buening; Meirav Segal; Debabrota Basu; Christos Dimitrakakis (CoRR, 2021)

Differential Privacy at Risk: Bridging Randomness and Privacy Budget. - Ashish Dandekar; Debabrota Basu; Stéphane Bressan (Proc. Priv. Enhancing Technol., 2021)

UDO: Universal Database Optimization using Reinforcement Learning. - Junxiong Wang; Immanuel Trummer; Debabrota Basu (Proc. VLDB Endow., 2021)

Algorithmic Fairness Verification with Graphical Models. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (AAAI, 2022)

Procrastinated Tree Search: Black-Box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback. - Junxiong Wang; Debabrota Basu; Immanuel Trummer (AAAI, 2022)

On Meritocracy in Optimal Set Selection. - Thomas Kleine Buening; Meirav Segal; Debabrota Basu; Anne-Marie George; Christos Dimitrakakis (EAAMO, 2022)

When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits. - Achraf Azize; Debabrota Basu (NeurIPS, 2022)

SENTINEL: taming uncertainty with ensemble based distributional reinforcement learning. - Hannes Eriksson; Debabrota Basu; Mina Alibeigi; Christos Dimitrakakis (UAI, 2022)

Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm. - Debabrota Basu; Odalric-Ambrym Maillard; Timothée Mathieu (CoRR, 2022)

Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty. - Hannes Eriksson; Debabrota Basu; Mina Alibeigi; Christos Dimitrakakis (CoRR, 2022)

SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics. - Yannis Flet-Berliac; Debabrota Basu (CoRR, 2022)

How Biased is Your Feature?: Computing Fairness Influence Functions with Global Sensitivity Analysis. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (CoRR, 2022)

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning. - Reda Ouhamma; Debabrota Basu; Odalric-Ambrym Maillard (CoRR, 2022)

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning. - Reda Ouhamma; Debabrota Basu; Odalric Maillard (AAAI, 2023)

"How Biased are Your Features?": Computing Fairness Influence Functions with Global Sensitivity Analysis. - Bishwamittra Ghosh; Debabrota Basu; Kuldeep S. Meel (FAccT, 2023)

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. - Edwige Cyffers; Aurélien Bellet; Debabrota Basu (ICML, 2023)

On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence. - Achraf Azize; Marc Jourdan; Aymen Al Marjani; Debabrota Basu (NeurIPS, 2023)

Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack. - Pratik Karmakar; Debabrota Basu (NeurIPS, 2023)

Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data. - Pratik Karmakar; Debabrota Basu (CoRR, 2023)

Online Instrumental Variable Regression: Regret Analysis and Bandit Feedback. - Riccardo Della Vecchia; Debabrota Basu (CoRR, 2023)

Interactive and Concentrated Differential Privacy for Bandits. - Achraf Azize; Debabrota Basu (CoRR, 2023)

Pure Exploration in Bandits with Linear Constraints. - Emil Carlsson; Debabrota Basu; Fredrik D. Johansson; Devdatt P. Dubhashi (AISTATS, 2024)

CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. - Shubhada Agrawal; Timothée Mathieu; Debabrota Basu; Odalric-Ambrym Maillard (ALT, 2024)

Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer. - Hannes Eriksson; Tommy Tram; Debabrota Basu; Mina Alibeigi; Christos Dimitrakakis (AAMAS, 2024)

Open Problem: What is the Complexity of Joint Differential Privacy in Linear Contextual Bandits? - Achraf Azize; Debabrota Basu (COLT, 2024)

Augmented Bayesian Policy Search. - Mahdi Kallel; Debabrota Basu; Riad Akrour; Carlo D'Eramo (ICLR, 2024)

Preference-based Pure Exploration. - Apurv Shukla 001; Debabrota Basu (NeurIPS, 2024)

Concentrated Differential Privacy for Bandits. - Achraf Azize; Debabrota Basu (SaTML, 2024)

Measuring Exploration in Reinforcement Learning via Optimal Transport in Policy Space. - Reabetswe M. Nkhumise; Debabrota Basu; Tony J. Prescott; Aditya Gilra (CoRR, 2024)

How Much Does Each Datapoint Leak Your Privacy? Quantifying the Per-datum Membership Leakage. - Achraf Azize; Debabrota Basu (CoRR, 2024)

Don't Forget What I did?: Assessing Client Contributions in Federated Learning. - Bishwamittra Ghosh; Debabrota Basu; Huazhu Fu; Yuan Wang 008; Renuga Kanagavelu; Jiang Jin Peng; Yong Liu 026; Rick Siow Mong Goh; Qingsong Wei (CoRR, 2024)

FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits. - Sunrit Chakraborty; Saptarshi Roy; Debabrota Basu (CoRR, 2024)

Differentially Private Best-Arm Identification. - Achraf Azize; Marc Jourdan; Aymen Al Marjani; Debabrota Basu (CoRR, 2024)

Testing Credibility of Public and Private Surveys through the Lens of Regression. - Debabrota Basu; Sourav Chakraborty 001; Debarshi Chanda; Buddha Dev Das; Arijit Ghosh; Arnab Ray (CoRR, 2024)

Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints. - Udvas Das; Debabrota Basu (CoRR, 2024)

Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs. - Chao Han; Debabrota Basu; Michael Mangan; Eleni Vasilaki; Aditya Gilra (CoRR, 2024)

Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret. - Emilio Jorge; Christos Dimitrakakis; Debabrota Basu (CoRR, 2024)

Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. - Timothée Mathieu; Debabrota Basu; Odalric-Ambrym Maillard (Trans. Mach. Learn. Res., 2024)

Active Fourier Auditor for Estimating Distributional Properties of ML Models. - Ayoub Ajarra; Bishwamittra Ghosh; Debabrota Basu (AAAI, 2025)

When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning. - Naheed Anjum Arafat; Debabrota Basu; Yulia R. Gel; Yuzhou Chen (AAAI, 2025)

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback. - Riccardo Della Vecchia; Debabrota Basu (AAAI, 2025)

Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage. - Achraf Azize; Debabrota Basu (AISTATS, 2025)

FLIPHAT: Joint Differential Privacy for High Dimensional Linear Bandits. - Saptarshi Roy; Sunrit Chakraborty; Debabrota Basu (AISTATS, 2025)

Studying Exploration in RL: An Optimal Transport Analysis of Occupancy Measure Trajectories. - Reabetswe M. Nkhumise; Debabrota Basu; Tony J. Prescott; Aditya Gilra (Trans. Mach. Learn. Res., 2025)

Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing. - Debabrota Basu; Debarshi Chanda (CoRR, 2025)

Optimal Regret of Bernoulli Bandits under Global Differential Privacy. - Achraf Azize; Yulian Wu; Junya Honda; Francesco Orabona; Shinji Ito; Debabrota Basu (CoRR, 2025)

Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning. - Guillaume Pourcel; Debabrota Basu; Maxence Ernoult; Aditya Gilra (CoRR, 2025)

The Fair Game: Auditing & Debiasing AI Algorithms Over Time. - Debabrota Basu; Udvas Das (CoRR, 2025)