Publications

Thesis
Journal article
Conference article
Workshop article
Demonstration

By date
By type

2024

M. Kallel, D. Basu, R. Akrour, and C. D'Eramo, Augmented Bayesian Policy Search. In Proc. ICLR, 2024. (PDF)
E. Carlsson, D. Basu, F. D. Johansson, and D. Dubhashi, Pure Exploration in Bandits with Linear Constraints. In Proc. AISTATS, 2024. Primary version: Accepted in EWRL, 2023. (PDF)
H. Eriksson, T. Tram, D. Basu, M. Alibeigi, and C. Dimitrakakis, Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer. In Proc. AAMAS, 2024. (PDF)
A. Azize, and D. Basu, Interactive and Concentrated Differential Privacy for Bandits. In Proc. IEEE SaTML, 2024. Primary version: Accepted in EWRL, 2023. (PDF)
S. Agarwal, T. Mathieu, D. Basu, and O.-A. Maillard, CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. In Proc. ALT, 2024. (PDF)
T. Mathieu, D. Basu, and O.-A. Maillard, Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. In Transactions of Machine Learning Research (TMLR), 2024. Preprint: arXiv:2203.03186 (PDF)

2023

A. Azize, M. Jourdan, A. Al Marjani, and D. Basu, On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence. In Proc. NeurIPS, 2022. Primary version: Accepted in EWRL, 2023. (PDF)
E. Cyffers, A. Bellet, and D. Basu, From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. In Proc. ICML, 2023. Preprint: arXiv:2302.12559. (PDF)
B. Ghosh, D. Basu, and K. S. Meel, “How Biased are Your Features?”: Computing Fairness Influence Functions with Global Sensitivity Analysis. In Proc. ACM FAccT, 2023. Preprint: arXiv:2206.00667. (PDF | Code)
P. Karmakar, and D. Basu, Marich: A Query-efficient Distributionally-Equivalent Model Extraction Attack using Public Data. In Proc. NeurIPS, 2023. Primary version: Accepted in PPAI@AAAI, 2023. (PDF | Code)
A. Azize, and D. Basu, Renyi Diffrentially Private Bandits.In PPAI@AAAI, 2023. (PDF)
O.-A. Maillard, T. Mathieu, and D. Basu, Farm-gym: A modular reinforcement learning platform for stochastic agronomic games.In AIAFS Workshop@AAAI, 2023. (PDF)
R. Ouhamma, D. Basu, and O.-A. Maillard, Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning. In Proc. AAAI, 2023. Primary version: Accepted in EWRL, 2022. (PDF)

2022

A. Azize, and D. Basu, When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits. In Proc. NeurIPS, 2022. Primary version: Accepted in EWRL, 2022. (PDF)
R. Della Vecchia, and D. Basu, Online Instrumental Variable Regression: Regret Analysis and Bandit Feedback. In HAL archives, hal-03831210, 2022. (PDF)
T. K. Buening, M. Segal, D. Basu, and C. Dimitrakakis, On Meritocracy in Optimal Set Selection. In Proc. ACM EAAMO, 2022. (PDF)
E. Jorge, H. Eriksson, C. Dimitrakakis, D. Basu, and D. Grover, On Bayesian Value Function Distributions. In EWRL, 2022. (PDF)
H. Eriksson, D. Basu, M. Alibeigi, and C. Dimitrakakis, SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning. In Proc. UAI, 2022. (PDF)
Y. Flet-Berliac, and D. Basu, SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics. In Proc. RLDM, 2022. Extended version: arXiv preprint, arXiv:2204.09424, 2022. (PDF)
H. Eriksson, D. Basu, M. Alibeigi, and C. Dimitrakakis, Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty. In OptLearnMAS Workshop@AAMAS, 2022. Extended version: arXiv preprint, arXiv:2203.10045, 2022. (PDF)
J. Wang, D. Basu, and I. Trummer, Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-fidelity Feedback. In Proc. AAAI, 2022. (PDF)
B. Ghosh, D. Basu, and K. S. Meel, Algorithmic Fairness Verification with Graphical Models. In Proc. AAAI, 2022. (PDF)

2021

J. Wang, I. Trummer, and D. Basu, UDO: Universal Database Optimization using Reinforcement Learning. In Proc. VLDB, vol. 14, September, 2021. (PDF)
S. Tavara, A. Schilep, and D. Basu, Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-based SVM. In Proc. of workshop at ECML/PKDD, September, 2021. Presented at PharML'21. (PDF)
J. Wang, I. Trummer, and D. Basu, Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning. In Proc. SIGMOD, June, 2021. (PDF)
I. Ryazanov, A. T. Nylund, D. Basu, I.-M. Hassellöv, and A. Schliep, Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences. In Journal of Marine Science and Engineering, February, 2021. (PDF)
B. Ghosh, D. Basu, and K. S. Meel, Justicia: A Stochastic SAT Approach to Formally Verify Fairness. In Proc. AAAI, February, 2021. Extended version: arXiv preprint, arXiv:2009.06516, 2020. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differential Privacy at Risk: Bridging Randomness and Privacy Budget. In Proc. on Privacy Enhancing Technologies, January, 2021. (PDF)
*Extended version: arXiv preprint, arXiv:2003.00973, 2020. *Older version: In Proc. AAAI Workshop on Privacy Preserving AI (PPAI), Februray, 2020.

2020

E. Jorge, H. Eriksson, C. Dimitrakakis, D. Basu, and D. Grover, Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning. In PMLR, Vol. 137, "I Can't Believe It's Not Better!", NeurIPS, Dec., 2020. (PDF) *Older version: Inferential Induction: Joint Bayesian Estimation of MDPs and Value Functions. In arXiv preprint arXiv:2002.03098, 2020. (PDF)
A. Dandekar, D. Basu, P. Senellart, and S. Bressan, Confidentialité différentielle à risque: Relier les sources d’aléa et un budget de confidentialité. In Proc. on BDA, October, 2020. (PDF)
N. A. Arafat, D. Basu, and S. Bressan, ε-net Induced Lazy Witness Complex on Graphs. In arXiv preprint, arXiv:2009.13071, 2020. (PDF)
D. Grover, D. Basu, and C. Dimitrakakis, Bayesian Reinforcement Learning via Deep, Sparse Sampling. In Proc. AISTATS, Virtual, June, 2020. (PDF)
N. A. Arafat, D. Basu, L. Decreusefond, and S. Bressan, Construction and Random Generation of Hypergraphs with Prescribed Degree and Dimension Sequences. In Proc. DEXA, Virtual, 2020 (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differential Privacy at Risk: Bridging Randomness and Privacy Budget. In Proc. AAAI Workshop on Privacy Preserving AI (PPAI), Februray, 2020. (PDF)

2019

D. Basu, P. Senellart, and S. Bressan, BelMan: An Information Geometric Approach to Stochastic Bandits. In Proc. ECML-PKDD, September,2019. (PDF | Code)
N. A. Arafat, D. Basu, and S. Bressan, ε-net Induced Lazy Witness Complex on Graphs. In Proc. ATDA at ECML-PKDD, September, 2019. (PDF)
D. Basu, C. Dimitrakakis, and A. C. Y. Toussou, Differential Privacy For Multi-Armed Bandits: What Is It And What Is Its Cost?. In CORR, abs/1905.12298, 2019. (PDF)
A. C. Y. Toussou, D. Basu, and C. Dimitrakakis, Near Optimal Reinforcement Learning Using Bayesian Quantiles. In CORR, abs/1906.09114, 2019. (PDF)
A. C. Y. Toussou, D. Basu, and C. Dimitrakakis, Near Optimal Reinforcement Learning using Empirical Bernstein Inequalities. In CORR, abs/1906.09114, 2019. (PDF)
N. A. Arafat, D. Basu, and S. Bressan, Topological Data Analysis with ε-Net Induced Lazy Witness Complex. In Proc. DEXA, Linz, Austria, August, 2019. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differentially Private Non-parametric Machine Learning as a Service. In Proc. DEXA, Linz, Austria, August, 2019. (PDF)
A. Dandekar, D. Basu, T. Kister, G. S. Poh, J. Xu, and S. Bressan, Privacy as a Service: Publishing Data and Models. In Proc. DASFAA, Thailand, April, 2019. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Evaluation of Differentially Private Non-parametric Machine Learning as a Service. In DSpace@NUS, 2019. (PDF)
D. Basu, X. Wang, Y. Hong, H. Chen, and S. Bressan, Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines. In IEEE Transactions on Parallel and Distributed Systems, January, 2019. (PDF)

2018

D. Basu, Learning to Make Decisions with Incomplete Information: Reinforcement Learning, Information Geometry, and Real-Life Applications. In ScholarBank@NUS, October 2018. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differential Privacy for Regularised Linear Regression. In Proc. DEXA, Regensburg, Germany September 2018. (PDF)
D. Basu, P. Senellart, and S. Bressan, BelMan: Bayesian Bandits on the Belief–Reward Manifold. arXiv preprint arXiv:1805.01627 2018. (PDF | Code)

2017

Q. Liu, D. Basu, S. Goel, T. Abdessalem, and S. Bressan, How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough?. In Proc. DEXA, Lyon, France, August 2017. (PDF)
Q. Liu, D. Basu, S. Goel, T. Abdessalem, and S. Bressan, How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough? . In Technical report, School of Computing, National University of Singapore, June 2017. *Detailed version of DEXA 2017 paper. (PDF)
D. Basu, X. Wang, Y. Hong, H. Chen, and S. Bressan, Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines. In ICDCS, Atlanta, USA, June 2017. (PDF)
D. Basu, G. Pedrielli, W. Chen, S. H. Ng, L. H. Lee, and S. Bressan, Sequential Vessel Speed Optimization under Dynamic Weather Conditions. In MTEC, Singapore, April 2017. (PDF)

2016

Q. Liu, D. Basu, T. Abdessalem, and S. Bressan, Top-k Queries over Uncertain Scores. In Proc. CoopIS, Rhodes, Greece, October 2016. (PDF)
D. Basu, Q. Lin, Z. Yuan, P. Senellart, and S. Bressan, Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Volume 28, Special Edition, LNCS 9940, 2016. (PDF | Code)

2015

D. Basu, Q. Lin, Z. Yuan, P. Senellart, and S. Bressan, Apprentissage par renforcement pour optimiser les bases de donnéees indépendamment du modèle de coût. In Proc. BDA, Porquerolles, France, September 2015. Conference without formal proceedings. (PDF | Code)
D. Basu, Q. Lin, W. Chen, H. T. Vo, Z. Yuan, P. Senellart, and S. Bressan, Cost-Model Oblivious Database Tuning with Reinforcement Learning. In Proc. DEXA, Valencia, Spain, September 2015. (PDF | Code)
S. Bhattacharyya, D. Basu, A. Konar, D. N. Tibarewala, Interval Type-2 Fuzzy Logic Based Multiclass ANFIS Algorithm for Real-time EEG Based Movement Control of a Robot Arm. Robotics and Autonomous Systems, 68, pp. 104 - 115, June 2015. (PDF)

2014

D. Basu, S. Bhattacharyya, A. Konar, D. N. Tibarewala, A Type-2 Adaptive Neuro-Fuzzy Inference System using Differential Evolution for EEG classification. In Proc. Fuzz-IEEE, Beijing, China,July 2014. (PDF)
D. Basu, S. Bhattacharyya, A. Konar, and D. N. Tibarewala, Feature Extraction using Scale-Free Graphs for Motor Imagery EEG Signals. July 2014. Preprint. Accepted in IEEE-SSCI, 2014 but is not in proceedings.
D. Sardar, D. Basu, S. Bhattacharyya, A. Konar, A. Khasnobish, D. N. Tibarewala, and R. Janarthanan, Embedded Realisation of Amplitude-Phase Adaptive Filter for Bio-Potential Signals. In Proc. CIEC, Kolkata, India, February 2014. (PDF)
S. Das, S. Biswas, B. K. Panigrahi, S. Kundu and D. Basu, A Spatially Informative Optic Flow Model of Bee Colony with Saccadic Flight Strategy for Global Optimization. IEEE Transactions on Cybernetics, vol. 44(10), pp. 593-597, January 2014. (PDF)

2013

S. Debchoudhury, D. Basu, K. Z. Gao, and P. N. Sugnathan, Load Information Based Priority Dependent Heuristic for Manpower Scheduling Problem in Remanufacturing. In Proc. SEMCCO, Chennai, India, December 2013. (PDF)
D. Basu, S. Debchoudhury, K. Z. Gao, and P. N. Sugnathan, A Novel Improved Discrete ABC Algorithm for Manpower Scheduling Problem in Remanufacturing. In Proc. SEMCCO, Chennai, India, December 2013. (PDF)

Thesis

D. Basu, Learning to Make Decisions with Incomplete Information: Reinforcement Learning, Information Geometry, and Real-Life Applications. In ScholarBank, NUS, October 2018. (PDF)

Journal articles

T. Mathieu, D. Basu, and O.-A. Maillard, Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. In Transactions of Machine Learning Research (TMLR), 2024. Preprint: arXiv:2203.03186 (PDF)
I. Ryazanov, A. T. Nylund, D. Basu, I.-M. Hassellöv, and A. Schliep, Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences. In Journal of Marine Science and Engineering, February, 2021. (PDF)
D. Basu, X. Wang, Y. Hong, H. Chen, and S. Bressan, Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines. In IEEE Transactions on Parallel and Distributed Systems, January, 2019. (PDF)
D. Basu, Q. Lin, Z. Yuan, P. Senellart, and S. Bressan, Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Volume 28, Special Edition, LNCS 9940, 2016. (PDF)
S. Bhattacharyya, D. Basu, A. Konar, D. N. Tibarewala, Interval Type-2 Fuzzy Logic Based Multiclass ANFIS Algorithm for Real-time EEG Based Movement Control of a Robot Arm. Robotics and Autonomous Systems, 68, pp. 104 - 115, June 2015. (PDF)
S. Das, S. Biswas, B. K. Panigrahi, S. Kundu and D. Basu, A Spatially Informative Optic Flow Model of Bee Colony with Saccadic Flight Strategy for Global Optimization. IEEE Transactions on Cybernetics, vol. 44(10), pp. 593-597, January 2014. (PDF)

Conference articles

M. Kallel, D. Basu, R. Akrour, and C. D'Eramo, Augmented Bayesian Policy Search. In Proc. ICLR, 2024. (PDF)
E. Carlsson, D. Basu, F. D. Johansson, and D. Dubhashi, Pure Exploration in Bandits with Linear Constraints. In Proc. AISTATS, 2024. Primary version: Accepted in EWRL, 2023. (PDF)
H. Eriksson, T. Tram, D. Basu, M. Alibeigi, and C. Dimitrakakis, Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer. In Proc. AAMAS, 2024. (PDF)
A. Azize, and D. Basu, Interactive and Concentrated Differential Privacy for Bandits. In Proc. IEEE SaTML, 2024. Primary version: Accepted in EWRL, 2023. (PDF)
S. Agarwal, T. Mathieu, D. Basu, and O.-A. Maillard, CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. In Proc. ALT, 2024. (PDF)
A. Azize, M. Jourdan, A. Al Marjani, and D. Basu, On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence. In Proc. NeurIPS, 2022. Primary version: Accepted in EWRL, 2023. (PDF)
P. Karmakar, and D. Basu, Marich: A Query-efficient Distributionally-Equivalent Model Extraction Attack using Public Data. In Proc. NeurIPS, 2023. Primary version: Accepted in PPAI@AAAI, 2023. (PDF | Code)
E. Cyffers, A. Bellet, and D. Basu, From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. In Proc. ICML, 2023. Preprint: arXiv:2302.12559. (PDF)
B. Ghosh, D. Basu, and K. S. Meel, “How Biased are Your Features?”: Computing Fairness Influence Functions with Global Sensitivity Analysis. In Proc. ACM FAccT, 2023. Preprint: arXiv:2206.00667. (PDF | Code)
R. Ouhamma, D. Basu, and O.-A. Maillard, Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning. In Proc. AAAI, 2023. Primary version: Accepted in EWRL, 2022. (PDF)
A. Azize, and D. Basu, When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits. In Proc. NeurIPS, 2022. Primary version: Accepted in EWRL, 2022. (PDF)
T. K. Buening, M. Segal, D. Basu, and C. Dimitrakakis, On Meritocracy in Optimal Set Selection. In Proc. ACM EAAMO, 2022. (PDF)
H. Eriksson, D. Basu, M. Alibeigi, and C. Dimitrakakis, SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning. In Proc. UAI, 2022. (PDF)
Y. Flet-Berliac, and D. Basu, SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics. In Proc. RLDM, 2022. Extended version: arXiv preprint, arXiv:2204.09424, 2022. (PDF)
J. Wang, D. Basu, and I. Trummer, Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-fidelity Feedback. In Proc. AAAI, 2022. (PDF)
B. Ghosh, D. Basu, and K. S. Meel, Algorithmic Fairness Verification with Graphical Models. In Proc. AAAI, 2022. (PDF)
J. Wang, I. Trummer, and D. Basu, UDO: Universal Database Optimization using Reinforcement Learning. In Proc. VLDB, vol. 14, September, 2021. (PDF)
S. Tavara, A. Schilep, and D. Basu, Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-based SVM. In Proc. of workshop at ECML/PKDD, September, 2021. Presented at PharML'21. (PDF)
B. Ghosh, D. Basu, and K. S. Meel, Justicia: A Stochastic SAT Approach to Formally Verify Fairness. In Proc. AAAI, February, 2021. Extended version: arXiv preprint, arXiv:2009.06516, 2020. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differential Privacy at Risk: Bridging Randomness and Privacy Budget. In Proc. on Privacy Enhancing Technologies, January, 2021. (PDF)
*Extended version: arXiv preprint, arXiv:2003.00973, 2020. *Older version: In Proc. AAAI Workshop on Privacy Preserving AI (PPAI), Februray, 2020.
E. Jorge, H. Eriksson, C. Dimitrakakis, D. Basu, and D. Grover, Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning. In PMLR, Vol. 137, "I Can't Believe It's Not Better!", NeurIPS, Dec., 2020. (PDF)
A. Dandekar, D. Basu, P. Senellart, and S. Bressan, Confidentialité différentielle à risque: Relier les sources d’aléa et un budget de confidentialité. In Proc. on BDA, October, 2020. (PDF)
D. Grover, D. Basu, and C. Dimitrakakis, Bayesian Reinforcement Learning via Deep, Sparse Sampling. In Proc. AISTATS, Palermo, Italy, June, 2020. (PDF)
D. Basu, P. Senellart, and S. Bressan, BelMan: An Information Geometric Approach to Stochastic Bandits. In Proc. ECML-PKDD, September,2019. (PDF | Code)
N. A. Arafat, D. Basu, and S. Bressan, Topological Data Analysis with ε-Net Induced Lazy Witness Complex. In Proc. DEXA, Linz, Austria, August, 2019. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differentially Private Non-parametric Machine Learning as a Service. In Proc. DEXA, Linz, Austria, August, 2019. (PDF)
A. Dandekar, D. Basu, T. Kister, G. S. Poh, J. Xu, and S. Bressan, Privacy as a Service: Publishing Data and Models. In Proc. DASFAA, Thailand, April, 2019. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differential Privacy for Regularised Linear Regression.. In Proc. DEXA, Regensburg, Germany September 2018. (PDF)
Q. Liu, D. Basu, S. Goel, T. Abdessalem, and S. Bressan, How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough?. In Proc. DEXA, Lyon, France, August 2017. (PDF)
D. Basu, X. Wang, Y. Hong, H. Chen, and S. Bressan, Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines. In ICDCS, Atlanta, USA, June 2017. (PDF)
D. Basu, G. Pedrielli, W. Chen, S. H. Ng, L. H. Lee, and S. Bressan, Sequential Vessel Speed Optimization under Dynamic Weather Conditions. In MTEC, Singapore, April 2017. (PDF)
Q. Liu, D. Basu, T. Abdessalem, and S. Bressan, Top-k Queries over Uncertain Scores. In Proc. CoopIS, Rhodes, Greece, October 2016. (PDF)
D. Basu, Q. Lin, Z. Yuan, P. Senellart, and S. Bressan, Apprentissage par renforcement pour optimiser les bases de donnéees indépendamment du modèle de coût. In Proc. BDA, Porquerolles, France, September 2015. Conference without formal proceedings. (PDF)
D. Basu, Q. Lin, W. Chen, H. T. Vo, Z. Yuan, P. Senellart, and S. Bressan, Cost-Model Oblivious Database Tuning with Reinforcement Learning. In Proc. DEXA, Valencia, Spain, September 2015. (PDF)
D. Basu, S. Bhattacharyya, A. Konar, D. N. Tibarewala, A Type-2 Adaptive Neuro-Fuzzy Inference System using Differential Evolution for EEG classification. In Proc. Fuzz-IEEE, Beijing, China,July 2014. (PDF)
D. Sardar, D. Basu, S. Bhattacharyya, A. Konar, A. Khasnobish, D. N. Tibarewala, and R. Janarthanan, Embedded Realisation of Amplitude-Phase Adaptive Filter for Bio-Potential Signals. In Proc. CIEC, Kolkata, India, February 2014. (PDF)
D. Basu, S. Debchoudhury, K. Z. Gao, and P. N. Sugnathan, A Novel Improved Discrete ABC Algorithm for Manpower Scheduling Problem in Remanufacturing. In Proc. SEMCCO, Chennai, India, December 2013. (PDF)
S. Debchoudhury, D. Basu, K. Z. Gao, and P. N. Sugnathan, Load Information Based Priority Dependent Heuristic for Manpower Scheduling Problem in Remanufacturing. In Proc. SEMCCO, Chennai, India, December 2013. (PDF)

Workshop Articles (Peer-reviewed)

A. Azize, and D. Basu, Interactive and Concentrated Differential Privacy for Bandits. Accepted in EWRL, 2023. (PDF)
A. Azize, M. Jourdan, A. Al Marjani, and D. Basu, On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence. Accepted in EWRL, 2023. (PDF)
E. Carlsson, D. Basu, F. D. Johansson, and D. Dubhashi, Pure Exploration in Bandits with Linear Constraints. Preprint: arXiv:2306.12774, 2023. Accepted in EWRL, 2023. (PDF)
P. Karmakar, and D. Basu, Marich: A Query-efficient Distributionally-Equivalent Model Extraction Attack using Public Data.In PPAI@AAAI, 2023. Extended version: arXiv preprint, arXiv:2203.10045. (PDF | Code)
A. Azize, and D. Basu, Renyi Diffrentially Private Bandits.In PPAI@AAAI, 2023. (PDF)
O.-A. Maillard, T. Mathieu, and D. Basu, Farm-gym: A modular reinforcement learning platform for stochastic agronomic games.In AIAFS Workshop@AAAI, 2023. (PDF)
E. Jorge, H. Eriksson, C. Dimitrakakis, D. Basu, and D. Grover, On Bayesian Value Function Distributions. In EWRL, 2022. (PDF)
H. Eriksson, D. Basu, M. Alibeigi, and C. Dimitrakakis, Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty. In OptLearnMAS Workshop@AAMAS, 2022. Extended version: arXiv preprint, arXiv:2203.10045, 2022. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Differential Privacy at Risk: Bridging Randomness and Privacy Budget. In Proc. AAAI Workshop on Privacy Preserving AI (PPAI), Februray, 2020. (PDF)
N. A. Arafat, D. Basu, and S. Bressan, ε-net Induced Lazy Witness Complex on Graphs. In Proc. ATDA at ECML-PKDD, September, 2019. (PDF)

Preprint (Archived Articles)

R. Della Vecchia, and D. Basu, Online Instrumental Variable Regression: Regret Analysis and Bandit Feedback. In HAL archives, hal-03831210, 2022. (PDF)
N. A. Arafat, D. Basu, and S. Bressan, ε-net Induced Lazy Witness Complex on Graphs. In arXiv preprint, arXiv:2009.13071, 2020. (PDF)
D. Basu, C. Dimitrakakis, and A. C. Y. Toussou, Differential Privacy For Multi-Armed Bandits: What Is It And What Is Its Cost?. In arXiv preprint, arXiv:1905.12298, 2019. (PDF)
A. C. Y. Toussou, D. Basu, and C. Dimitrakakis, Near Optimal Reinforcement Learning Using Bayesian Quantiles. In arXiv preprint, arXiv:1906.09114, 2019. (PDF)
A. C. Y. Toussou, D. Basu, and C. Dimitrakakis, Near Optimal Reinforcement Learning using Empirical Bernstein Inequalities. In arXiv preprint, arXiv:1905.12425, 2019. (PDF)
A. Dandekar, D. Basu, and S. Bressan, Evaluation of Differentially Private Non-parametric Machine Learning as a Service. In DSpace@NUS, 2019. (PDF)
D. Basu, P. Senellart, and S. Bressan, BelMan: Bayesian Bandits on the Belief–Reward Manifold. In arXiv preprint, arXiv:1805.01627 2018. (PDF)
Q. Liu, D. Basu, S. Goel, T. Abdessalem, and S. Bressan, How to Find the Best Rated Items on a Likert Scale and How Many Ratings Are Enough? . In Technical report, School of Computing, National University of Singapore, June 2017. *Detailed version of DEXA 2017 paper. (PDF)
D. Basu, S. Bhattacharyya, A. Konar, and D. N. Tibarewala, Feature Extraction using Scale-Free Graphs for Motor Imagery EEG Signals. July 2014. Preprint. Accepted in IEEE-SSCI, 2014 but is not in proceedings.

Demonstration

J. Wang, I. Trummer, and D. Basu, Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning. In Proc. SIGMOD, June, 2021. (PDF)
A. Dandekar, D. Basu, T. Kister, G. S. Poh, J. Xu, and S. Bressan, Privacy as a Service: Publishing Data and Models. In Proc. DASFAA, Thailand, April, 2019. (PDF)