Aaditya Ramdas

195 papers A* 41A 15B 5Journal 119Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A complete characterization of testable hypotheses. CoRR Martin Larsson, Johannes Ruf, Aaditya Ramdas
2026	J	`jnl`	Huber-Robust Likelihood Ratio Tests for Composite Nulls and Alternatives. IEEE Trans. Inf. Theory Aytijhya Saha, Aaditya Ramdas
2026	J	`jnl`	Improving Wald's (Approximate) Sequential Probability Ratio Test by Avoiding Overshoot. IEEE Trans. Inf. Theory Lasse Fischer, Aaditya Ramdas
2026	J	`jnl`	On admissibility in post-hoc hypothesis testing. Int. J. Approx. Reason. Ben Chugg, Tyron Lardy, Aaditya Ramdas, Peter Grünwald
2026	J	`jnl`	Online monotone density estimation and log-optimal calibration. CoRR Rohan Hore, Ruodu Wang, Aaditya Ramdas
2026	J	`jnl`	Operationalizing Stein's Method for Online Linear Optimization: CLT-Based Optimal Tradeoffs. CoRR Zhiyu Zhang, Aaditya Ramdas
2026	J	`jnl`	Tighter Confidence Intervals under Without Replacement Sampling via Empirical Rate Functions. CoRR Shubhanshu Shekhar, Aaditya Ramdas
2026	J	`jnl`	Universal Sequential Changepoint Detection of Quantum Observables via Classical Shadows. CoRR Matteo Zecchin, Osvaldo Simeone, Aaditya Ramdas
2025	A*	`conf`	Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback. ICLR Michelle D. Zhao, Henny Admoni, Reid G. Simmons, Aaditya Ramdas, Andrea Bajcsy
2025	J	`jnl`	Corrections to "Nonparametric Two-Sample Testing by Betting". IEEE Trans. Inf. Theory Shubhanshu Shekhar, Aaditya Ramdas
2025	J	`jnl`	Eventually LIL Regret: Almost Sure ln ln T Regret for a sub-Gaussian Mixture on Unbounded Data. CoRR Shubhada Agrawal, Aaditya Ramdas
2025	J	`jnl`	Gradient flow for deep equilibrium single-index models. CoRR Sanjit Dandapanthula, Aaditya Ramdas
2025	A*	`conf`	Improving the Statistical Efficiency of Cross-Conformal Prediction. ICML Matteo Gasparin, Aaditya Ramdas
2025	J	`jnl`	Improving the statistical efficiency of cross-conformal prediction. CoRR Matteo Gasparin, Aaditya Ramdas
2025	A	`conf`	Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect. AISTATS Ojash Neopane, Aaditya Ramdas, Aarti Singh
2025	J	`jnl`	On Stopping Times of Power-one Sequential Tests: Tight Lower and Upper Bounds. CoRR Shubhada Agrawal, Aaditya Ramdas
2025	J	`jnl`	Online Selective Conformal Inference: Errors and Solutions. Trans. Mach. Learn. Res. Yusuf Sale, Aaditya Ramdas
2025	J	`jnl`	Online Selective Conformal Prediction: Errors and Solutions. CoRR Yusuf Sale, Aaditya Ramdas
2025	J	`jnl`	Optimal Transportation and Alignment Between Gaussian Measures. CoRR Sanjit Dandapanthula, Aleksandr Podkopaev, Shiva Prasad Kasiviswanathan, Aaditya Ramdas, Ziv Goldfeld
2025	A*	`conf`	Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect. ICML Ojash Neopane, Aaditya Ramdas, Aarti Singh
2025	J	`jnl`	Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect. CoRR Ojash Neopane, Aaditya Ramdas, Aarti Singh
2025	J	`jnl`	Post-detection inference for sequential changepoint localization. CoRR Aytijhya Saha, Aaditya Ramdas
2025	J	`jnl`	Private Evolution Converges. CoRR Tomás González, Giulia Fanti, Aaditya Ramdas
2025	A*	`conf`	QA-Calibration of Language Model Confidence Scores. ICLR Putra Manggala, Atalanti-Anastasia Mastakouri, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2025	J	`jnl`	Quantum Sequential Universal Hypothesis Testing. CoRR Matteo Zecchin, Osvaldo Simeone, Aaditya Ramdas
2025	—	`conf`	Scalable Causal Structure Learning via Amortized Conditional Independence Testing. CLeaR James Leiner, Brian Manzo, Aaditya Ramdas, Wesley Tansey
2025	A	`conf`	Sequential Kernelized Stein Discrepancy. AISTATS Diego Martinez-Taboada, Aaditya Ramdas
2025	J	`jnl`	Sequentially Auditing Differential Privacy. CoRR Tomás González, Mateo Dulce-Rubio, Aaditya Ramdas, Mónica Ribero
2025	J	`jnl`	Time-Uniform Confidence Spheres for Means of Random Vectors. Trans. Mach. Learn. Res. Ben Chugg, Hongjian Wang, Aaditya Ramdas
2025	A*	`conf`	Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract). COLT Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
2025	J	`jnl`	Universal Inference Meets Random Projections: A Scalable Test for Log-Concavity. J. Comput. Graph. Stat. Robin Dunn, Aditya Gangrade, Larry A. Wasserman, Aaditya Ramdas
2025	J	`jnl`	Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity. CoRR Diego Martinez-Taboada, Tomás González, Aaditya Ramdas
2024	J	`jnl`	Bias Detection Via Signaling. CoRR Yiling Chen, Tao Lin, Ariel D. Procaccia, Aaditya Ramdas, Itai Shapira
2024	A*	`conf`	Bias Detection via Signaling. NeurIPS Yiling Chen, Tao Lin, Ariel D. Procaccia, Aaditya Ramdas, Itai Shapira
2024	J	`jnl`	Combining Evidence Across Filtrations. CoRR Yo Joong Choe, Aaditya Ramdas
2024	J	`jnl`	Comparing Sequential Forecasters. Oper. Res. Yo Joong Choe, Aaditya Ramdas
2024	J	`jnl`	Conformal online model aggregation. CoRR Matteo Gasparin, Aaditya Ramdas
2024	J	`jnl`	Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback. CoRR Michelle Zhao, Reid G. Simmons, Henny Admoni, Aaditya Ramdas, Andrea Bajcsy
2024	A	`conf`	Deep anytime-valid hypothesis testing. AISTATS Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
2024	A	`conf`	Differentially Private Conditional Independence Testing. AISTATS Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2024	A	`conf`	Graph fission and cross-validation. AISTATS James Leiner, Aaditya Ramdas
2024	J	`jnl`	Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect. CoRR Ojash Neopane, Aaditya Ramdas, Aarti Singh
2024	J	`jnl`	Nonparametric Two-Sample Testing by Betting. IEEE Trans. Inf. Theory Shubhanshu Shekhar, Aaditya Ramdas
2024	A	`conf`	Online multiple testing with e-values. AISTATS Ziyu Xu, Aaditya Ramdas
2024	A*	`conf`	Reducing sequential change detection to sequential estimation. ICML Shubhanshu Shekhar, Aaditya Ramdas
2024	—	`conf`	Semiparametric Efficient Inference in Adaptive Experiments. CLeaR Thomas Cook, Alan Mishler, Aaditya Ramdas
2024	J	`jnl`	Sequential Kernelized Stein Discrepancy. CoRR Diego Martinez-Taboada, Aaditya Ramdas
2024	A	`conf`	Testing exchangeability by pairwise betting. AISTATS Aytijhya Saha, Aaditya Ramdas
2024	A*	`conf`	Total Variation Floodgate for Variable Importance Inference in Classification. ICML Wenshuo Wang, Lucas Janson, Lihua Lei, Aaditya Ramdas
2024	J	`jnl`	β-calibration of Language Model Confidence Scores for Generative QA. CoRR Putra Manggala, Atalanti-Anastasia Mastakouri, Elke Kirschbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2023	J	`jnl`	A Permutation-Free Kernel Independence Test. J. Mach. Learn. Res. Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas
2023	J	`jnl`	A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds. J. Mach. Learn. Res. Ben Chugg, Hongjian Wang, Aaditya Ramdas
2023	J	`jnl`	A unified recipe for deriving (time-uniform) PAC-Bayes bounds. CoRR Ben Chugg, Hongjian Wang, Aaditya Ramdas
2023	A*	`conf`	Adaptive Privacy Composition for Accuracy-first Mechanisms. NeurIPS Ryan M. Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas
2023	J	`jnl`	Adaptive Privacy Composition for Accuracy-first Mechanisms. CoRR Ryan Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas
2023	A*	`conf`	An Efficient Doubly-Robust Test for the Kernel Treatment Effect. NeurIPS Diego Martinez-Taboada, Aaditya Ramdas, Edward Kennedy
2023	—	`conf`	Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform. WWW (Companion Volume) Akash Maharaj, Ritwik Sinha, David Arbour, Ian Waudby-Smith, Simon Z. Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg, Viswanathan Swaminathan
2023	J	`jnl`	Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance. CoRR Hongjian Wang, Aaditya Ramdas
2023	A*	`conf`	Auditing Fairness by Betting. NeurIPS Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder, Aaditya Ramdas
2023	J	`jnl`	Auditing Fairness by Betting. CoRR Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder, Aaditya Ramdas
2023	A*	`conf`	Counterfactually Comparing Abstaining Classifiers. NeurIPS Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas
2023	J	`jnl`	Counterfactually Comparing Abstaining Classifiers. CoRR Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas
2023	J	`jnl`	Deep anytime-valid hypothesis testing. CoRR Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
2023	J	`jnl`	Differentially Private Conditional Independence Testing. CoRR Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2023	A*	`conf`	Fully-Adaptive Composition in Differential Privacy. ICML Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu
2023	J	`jnl`	Huber-Robust Confidence Sequences. CoRR Hongjian Wang, Aaditya Ramdas
2023	A	`conf`	Huber-robust confidence sequences. AISTATS Hongjian Wang, Aaditya Ramdas
2023	J	`jnl`	Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB. CoRR Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
2023	J	`jnl`	Martingale Methods for Sequential Estimation of Convex Functionals and Divergences. IEEE Trans. Inf. Theory Tudor Manole, Aaditya Ramdas
2023	A*	`conf`	Nonparametric Extensions of Randomized Response for Private Confidence Sets. ICML Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
2023	A*	`conf`	On the Sublinear Regret of GP-UCB. NeurIPS Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu
2023	J	`jnl`	On the near-optimality of betting confidence sets for bounded means. CoRR Shubhanshu Shekhar, Aaditya Ramdas
2023	A*	`conf`	Online Platt Scaling with Calibeating. ICML Chirag Gupta, Aaditya Ramdas
2023	J	`jnl`	Online Platt Scaling with Calibeating. CoRR Chirag Gupta, Aaditya Ramdas
2023	J	`jnl`	Randomized and Exchangeable Improvements of Markov's, Chebyshev's and Chernoff's Inequalities. CoRR Aaditya Ramdas, Tudor Manole
2023	J	`jnl`	Reducing sequential change detection to sequential estimation. CoRR Shubhanshu Shekhar, Aaditya Ramdas
2023	J	`jnl`	Risk-limiting Financial Audits via Weighted Sampling without Replacement. CoRR Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas
2023	A	`conf`	Risk-limiting financial audits via weighted sampling without replacement. UAI Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas
2023	J	`jnl`	Semiparametric Efficient Inference in Adaptive Experiments. CoRR Thomas Cook, Alan Mishler, Aaditya Ramdas
2023	A*	`conf`	Sequential Changepoint Detection via Backward Confidence Sequences. ICML Shubhanshu Shekhar, Aaditya Ramdas
2023	A*	`conf`	Sequential Kernelized Independence Testing. ICML Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2023	A*	`conf`	Sequential Predictive Two-Sample and Independence Testing. NeurIPS Aleksandr Podkopaev, Aaditya Ramdas
2023	J	`jnl`	Sequential Predictive Two-Sample and Independence Testing. CoRR Aleksandr Podkopaev, Aaditya Ramdas
2023	J	`jnl`	Sequential change detection via backward confidence sequences. CoRR Shubhanshu Shekhar, Aaditya Ramdas
2023	J	`jnl`	Time-Uniform Confidence Spheres for Means of Random Vectors. CoRR Ben Chugg, Hongjian Wang, Aaditya Ramdas
2023	J	`jnl`	When do exact and powerful p-values and e-values exist? CoRR Zhenyuan Zhang, Aaditya Ramdas, Ruodu Wang
2022	J	`jnl`	A Permutation-Free Kernel Independence Test. CoRR Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas
2022	J	`jnl`	A Permutation-free Kernel Two-Sample Test. CoRR Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas
2022	J	`jnl`	A composite generalization of Ville's martingale theorem. CoRR Johannes Ruf, Martin Larsson, Wouter M. Koolen, Aaditya Ramdas
2022	A*	`conf`	A permutation-free kernel two-sample test. NeurIPS Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas
2022	J	`jnl`	Anytime-valid off-policy inference for contextual bandits. CoRR Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, Paul Mineiro
2022	A*	`conf`	Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. NeurIPS Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers
2022	J	`jnl`	Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. CoRR Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas, Ryan Rogers
2022	A*	`conf`	Faster online calibration without randomization: interval forecasts and the power of two choices. COLT Chirag Gupta, Aaditya Ramdas
2022	J	`jnl`	Faster online calibration without randomization: interval forecasts and the power of two choices. CoRR Chirag Gupta, Aaditya Ramdas
2022	J	`jnl`	Fully Adaptive Composition in Differential Privacy. CoRR Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Zhiwei Steven Wu
2022	J	`jnl`	Game-theoretic statistics and safe anytime-valid inference. CoRR Aaditya Ramdas, Peter Grünwald, Vladimir Vovk, Glenn Shafer
2022	—	`conf`	Interactive rank testing by betting. CLeaR Boyan Duan, Aaditya Ramdas, Larry A. Wasserman
2022	J	`jnl`	Locally private nonparametric confidence intervals and sequences. CoRR Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
2022	J	`jnl`	Nested conformal prediction and quantile out-of-bag ensemble methods. Pattern Recognit. Chirag Gupta, Arun K. Kuchibhotla, Aaditya Ramdas
2022	J	`jnl`	QuTE: decentralized multiple testing on sensor networks with false discovery rate control. CoRR Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan
2022	J	`jnl`	Sequential Kernelized Independence Testing. CoRR Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2022	J	`jnl`	Testing exchangeability: Fork-convexity, supermartingales and e-processes. Int. J. Approx. Reason. Aaditya Ramdas, Johannes Ruf, Martin Larsson, Wouter M. Koolen
2022	A*	`conf`	Top-label calibration and multiclass-to-binary reductions. ICLR Chirag Gupta, Aaditya Ramdas
2022	A*	`conf`	Tracking the risk of a deployed model and detecting harmful distribution shifts. ICLR Aleksandr Podkopaev, Aaditya Ramdas
2021	A*	`conf`	A unified framework for bandit multiple testing. NeurIPS Ziyu Xu, Ruodu Wang, Aaditya Ramdas
2021	J	`jnl`	A unified framework for bandit multiple testing. CoRR Ziyu Xu, Ruodu Wang, Aaditya Ramdas
2021	J	`jnl`	Asynchronous Online Testing of Multiple Hypotheses. J. Mach. Learn. Res. Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan
2021	—	`conf`	Best Arm Identification under Additive Transfer Bandits. ACSCC Ojash Neopane, Aaditya Ramdas, Aarti Singh
2021	J	`jnl`	Best Arm Identification under Additive Transfer Bandits. CoRR Ojash Neopane, Aaditya Ramdas, Aarti Singh
2021	J	`jnl`	Comparing Sequential Forecasters. CoRR Yo Joong Choe, Aaditya Ramdas
2021	A*	`conf`	Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting. ICML Chirag Gupta, Aaditya Ramdas
2021	A	`conf`	Distribution-free uncertainty quantification for classification under label shift. UAI Aleksandr Podkopaev, Aaditya Ramdas
2021	J	`jnl`	Distribution-free uncertainty quantification for classification under label shift. CoRR Aleksandr Podkopaev, Aaditya Ramdas
2021	—	`conf`	Dynamic Algorithms for Online Multiple Testing. MSML Ziyu Xu, Aaditya Ramdas
2021	J	`jnl`	Game-theoretic Formulations of Sequential Nonparametric One- and Two-Sample Tests. CoRR Shubhanshu Shekhar, Aaditya Ramdas
2021	J	`jnl`	How can one test if a binary sequence is exchangeable? Fork-convex hulls, supermartingales, and Snell envelopes. CoRR Aaditya Ramdas, Johannes Ruf, Martin Larsson, Wouter M. Koolen
2021	J	`jnl`	Nonparametric Iterated-Logarithm Extensions of the Sequential Generalized Likelihood Ratio Test. IEEE J. Sel. Areas Inf. Theory Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
2021	A*	`conf`	Off-Policy Confidence Sequences. ICML Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas
2021	J	`jnl`	Off-policy Confidence Sequences. CoRR Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas
2021	J	`jnl`	On the Bias, Risk, and Consistency of Sample Means in Multi-armed Bandits. SIAM J. Math. Data Sci. Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
2021	J	`jnl`	Path Length Bounds for Gradient Descent and Flow. J. Mach. Learn. Res. Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
2021	—	`conf`	RiLACS: Risk Limiting Audits via Confidence Sequences. E-VOTE-ID Ian Waudby-Smith, Philip B. Stark, Aaditya Ramdas
2021	J	`jnl`	Sequential Estimation of Convex Divergences using Reverse Submartingales and Exchangeable Filtrations. CoRR Tudor Manole, Aaditya Ramdas
2021	J	`jnl`	Tracking the risk of a deployed model and detecting harmful distribution shifts. CoRR Aleksandr Podkopaev, Aaditya Ramdas
2021	B	`conf`	Uncertainty quantification using martingales for misspecified Gaussian processes. ALT Willie Neiswanger, Aaditya Ramdas
2021	J	`jnl`	Universal Inference Meets Random Projections: A Scalable Test for Log-concavity. CoRR Robin Dunn, Larry A. Wasserman, Aaditya Ramdas
2020	B	`conf`	Analyzing Student Strategies In Blended Courses Using Clickstream Data. EDM Nil-Jana Akpinar, Aaditya Ramdas, Umut A. Acar
2020	J	`jnl`	Analyzing Student Strategies In Blended Courses Using Clickstream Data. CoRR Nil-Jana Akpinar, Aaditya Ramdas, Umut A. Acar
2020	A*	`conf`	Confidence sequences for sampling without replacement. NeurIPS Ian Waudby-Smith, Aaditya Ramdas
2020	A*	`conf`	Distribution-free binary classification: prediction sets, confidence intervals and calibration. NeurIPS Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas
2020	J	`jnl`	Distribution-free binary classification: prediction sets, confidence intervals and calibration. CoRR Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas
2020	A*	`conf`	Familywise Error Rate Control by Interactive Unmasking. ICML Boyan Duan, Aaditya Ramdas, Larry A. Wasserman
2020	A*	`conf`	On Conditional Versus Marginal Bias in Multi-Armed Bandits. ICML Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
2020	A*	`conf`	Online Control of the False Coverage Rate and False Sign Rate. ICML Asaf Weinstein, Aaditya Ramdas
2020	A	`conf`	The Power of Batching in Multiple Hypothesis Testing. AISTATS Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas, Michael I. Jordan
2020	J	`jnl`	Uncertainty quantification using martingales for misspecified Gaussian processes. CoRR Willie Neiswanger, Aaditya Ramdas
2019	A	`conf`	A Higher-Order Kolmogorov-Smirnov Test. AISTATS Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
2019	J	`jnl`	A Higher-Order Kolmogorov-Smirnov Test. CoRR Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
2019	A*	`conf`	ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. NeurIPS Jinjin Tian, Aaditya Ramdas
2019	A*	`conf`	Are sample means in multi-armed bandits positively or negatively biased? NeurIPS Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
2019	A*	`conf`	Conformal Prediction Under Covariate Shift. NeurIPS Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas
2019	J	`jnl`	Decoding From Pooled Data: Phase Transitions of Message Passing. IEEE Trans. Inf. Theory Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2019	J	`jnl`	Decoding from Pooled Data: Sharp Information-Theoretic Bounds. SIAM J. Math. Data Sci. Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2019	A*	`conf`	Foundations of Large-Scale Sequential Experimentation. KDD Aaditya Ramdas
2019	J	`jnl`	On the bias, risk and consistency of sample means in multi-armed bandits. CoRR Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
2019	J	`jnl`	Path Length Bounds for Gradient Descent and Flow. CoRR Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
2018	J	`jnl`	Asynchronous Online Testing of Multiple Hypotheses. CoRR Tijana Zrnic, Aaditya Ramdas, Michael I. Jordan
2018	J	`jnl`	Iterative Methods for Solving Factorized Linear Systems. SIAM J. Matrix Anal. Appl. Anna Ma, Deanna Needell, Aaditya Ramdas
2018	A*	`conf`	SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. ICML Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan
2018	J	`jnl`	SAFFRON: an adaptive algorithm for online control of the false discovery rate. CoRR Aaditya Ramdas, Tijana Zrnic, Martin J. Wainwright, Michael I. Jordan
2017	—	`conf`	A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control. NIPS Fanny Yang, Aaditya Ramdas, Kevin Jamieson, Martin J. Wainwright
2017	J	`jnl`	A framework for Multi-A(rmed)/B(andit) testing with online FDR control. CoRR Fanny Yang, Aaditya Ramdas, Kevin Jamieson, Martin J. Wainwright
2017	J	`jnl`	DAGGER: A sequential algorithm for FDR control on DAGs. CoRR Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Decoding from Pooled Data: Phase Transitions of Message Passing. CoRR Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2017	B	`conf`	Decoding from pooled data: Phase transitions of message passing. ISIT Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2017	—	`conf`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2017	J	`jnl`	On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests. Entropy Aaditya Ramdas, Nicolás García Trillos, Marco Cuturi
2017	—	`conf`	Online control of the false discovery rate with decaying memory. NIPS Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Online control of the false discovery rate with decaying memory. CoRR Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan
2017	—	`conf`	QuTE: Decentralized multiple testing on sensor networks with false discovery rate control. CDC Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan
2017	J	`jnl`	Rows versus Columns: Randomized Kaczmarz or Gauss-Seidel for Ridge Regression. SIAM J. Sci. Comput. Ahmed Hefny, Deanna Needell, Aaditya Ramdas
2016	J	`jnl`	Asymptotic behavior of ℓ CoRR Ahmed El Alaoui, Xiang Cheng, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan
2016	J	`jnl`	Classification Accuracy as a Proxy for Two Sample Testing. CoRR Aaditya Ramdas, Aarti Singh, Larry A. Wasserman
2016	J	`jnl`	Decoding from Pooled Data: Sharp Information-Theoretic Bounds. CoRR Ahmed El Alaoui, Aaditya Ramdas, Florent Krzakala, Lenka Zdeborová, Michael I. Jordan
2016	J	`jnl`	Function-Specific Mixing Times and Concentration Away from Equilibrium. CoRR Maxim Rabinovich, Aaditya Ramdas, Michael I. Jordan, Martin J. Wainwright
2016	J	`jnl`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2016	J	`jnl`	Minimax Lower Bounds for Linear Independence Testing. CoRR Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman
2016	B	`conf`	Minimax lower bounds for linear independence testing. ISIT Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman
2016	A	`conf`	Sequential Nonparametric Testing with the Law of the Iterated Logarithm. UAI Akshay Balsubramani, Aaditya Ramdas
2016	J	`jnl`	Towards a deeper geometric, analytic and algorithmic understanding of margins. Optim. Methods Softw. Aaditya Ramdas, Javier Peña
2016	J	`jnl`	Universality of Mallows' and degeneracy of Kendall's kernels for rankings. CoRR Horia Mania, Aaditya Ramdas, Martin J. Wainwright, Michael I. Jordan, Benjamin Recht
2015	J	`jnl`	Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. CoRR Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	A*	`conf`	Advances in Nonparametric Hypothesis Testing. IJCAI Aaditya Ramdas
2015	J	`jnl`	Algorithmic Connections Between Active Learning and Stochastic Convex Optimization. CoRR Aaditya Ramdas, Aarti Singh
2015	J	`jnl`	An Analysis of Active Learning With Uniform Feature Noise. CoRR Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	—	`—`	Computational and Statistical Advances in Testing and Learning. Aaditya Ramdas
2015	J	`jnl`	Convergence Properties of the Randomized Extended Gauss-Seidel and Kaczmarz Methods. SIAM J. Matrix Anal. Appl. Anna Ma, Deanna Needell, Aaditya Ramdas
2015	—	`conf`	Fast Two-Sample Testing with Analytic Representations of Probability Measures. NIPS Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton
2015	J	`jnl`	Margins, Kernels and Non-linear Smoothed Perceptrons. CoRR Aaditya Ramdas, Javier Peña
2015	A*	`conf`	Nonparametric Independence Testing for Small Sample Sizes. IJCAI Aaditya Ramdas, Leila Wehbe
2015	A*	`conf`	On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. AAAI Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	A	`conf`	On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. AISTATS Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2015	J	`jnl`	Sequential Nonparametric Testing with the Law of the Iterated Logarithm. CoRR Aaditya Ramdas, Akshay Balsubramani
2014	A	`conf`	An Analysis of Active Learning with Uniform Feature Noise. AISTATS Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2014	J	`jnl`	Fast and Flexible ADMM Algorithms for Trend Filtering. CoRR Aaditya Ramdas, Ryan J. Tibshirani
2014	J	`jnl`	Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2014	A*	`conf`	Margins, Kernels and Non-linear Smoothed Perceptrons. ICML Aaditya Ramdas, Javier Peña
2014	J	`jnl`	On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. CoRR Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
2014	J	`jnl`	Rows vs Columns for Linear Systems of Equations - Randomized Kaczmarz or Coordinate Descent? CoRR Aaditya Ramdas
2014	J	`jnl`	Towards A Deeper Geometric, Analytic and Algorithmic Understanding of Margins. CoRR Aaditya Ramdas, Javier Peña
2013	B	`conf`	Algorithmic Connections between Active Learning and Stochastic Convex Optimization. ALT Aaditya Ramdas, Aarti Singh
2013	—	`conf`	Exploring the intersection of active learning and stochastic convex optimization. GlobalSIP Aaditya Ramdas, Aarti Singh
2013	—	`conf`	Optimal rates for stochastic convex optimization under Tsybakov noise condition. ICML (1) Aaditya Ramdas, Aarti Singh
2012	J	`jnl`	Optimal Stochastic Convex Optimization Through The Lens Of Active Learning CoRR Aaditya Ramdas, Aarti Singh

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.