Optimal Stochastic Convex Optimization Through The Lens Of Active Learning - Aaditya Ramdas; Aarti Singh (CoRR, 2012)

Algorithmic Connections between Active Learning and Stochastic Convex Optimization. - Aaditya Ramdas; Aarti Singh (ALT, 2013)

Exploring the intersection of active learning and stochastic convex optimization. - Aaditya Ramdas; Aarti Singh (GlobalSIP, 2013)

Optimal rates for stochastic convex optimization under Tsybakov noise condition. - Aaditya Ramdas; Aarti Singh (ICML (1), 2013)

An Analysis of Active Learning with Uniform Feature Noise. - Aaditya Ramdas; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (AISTATS, 2014)

Margins, Kernels and Non-linear Smoothed Perceptrons. - Aaditya Ramdas; Javier Peña 001 (ICML, 2014)

Rows vs Columns for Linear Systems of Equations - Randomized Kaczmarz or Coordinate Descent? - Aaditya Ramdas (CoRR, 2014)

On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. - Aaditya Ramdas; Sashank J. Reddi; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (CoRR, 2014)

Fast and Flexible ADMM Algorithms for Trend Filtering. - Aaditya Ramdas; Ryan J. Tibshirani (CoRR, 2014)

Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. - Sashank J. Reddi; Aaditya Ramdas; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (CoRR, 2014)

On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. - Aaditya Ramdas; Sashank Jakkam Reddi; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (AAAI, 2015)

On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. - Sashank J. Reddi; Aaditya Ramdas; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (AISTATS, 2015)

Advances in Nonparametric Hypothesis Testing. - Aaditya Ramdas (IJCAI, 2015)

Nonparametric Independence Testing for Small Sample Sizes. - Aaditya Ramdas; Leila Wehbe (IJCAI, 2015)

Fast Two-Sample Testing with Analytic Representations of Probability Measures. - Kacper Chwialkowski; Aaditya Ramdas; Dino Sejdinovic; Arthur Gretton (NIPS, 2015)

Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. - Aaditya Ramdas; Sashank J. Reddi; Barnabás Póczos; Aarti Singh; Larry A. Wasserman (CoRR, 2015)

Convergence Properties of the Randomized Extended Gauss-Seidel and Kaczmarz Methods. - Anna Ma; Deanna Needell; Aaditya Ramdas (SIAM J. Matrix Anal. Appl., 2015)

Computational and Statistical Advances in Testing and Learning. - Aaditya Ramdas (2015)

Minimax lower bounds for linear independence testing. - Aaditya Ramdas; David Isenberg; Aarti Singh; Larry A. Wasserman (ISIT, 2016)

Sequential Nonparametric Testing with the Law of the Iterated Logarithm. - Akshay Balsubramani; Aaditya Ramdas (UAI, 2016)

Asymptotic behavior of ℓp-based Laplacian regularization in semi-supervised learning. - Ahmed El Alaoui; Xiang Cheng 006; Aaditya Ramdas; Martin J. Wainwright; Michael I. Jordan (CoRR, 2016)

Universality of Mallows' and degeneracy of Kendall's kernels for rankings. - Horia Mania; Aaditya Ramdas; Martin J. Wainwright; Michael I. Jordan; Benjamin Recht (CoRR, 2016)

Function-Specific Mixing Times and Concentration Away from Equilibrium. - Maxim Rabinovich; Aaditya Ramdas; Michael I. Jordan; Martin J. Wainwright (CoRR, 2016)

Classification Accuracy as a Proxy for Two Sample Testing. - Aaditya Ramdas; Aarti Singh; Larry A. Wasserman (CoRR, 2016)

Towards a deeper geometric, analytic and algorithmic understanding of margins. - Aaditya Ramdas; Javier Peña 001 (Optim. Methods Softw., 2016)

QuTE: Decentralized multiple testing on sensor networks with false discovery rate control. - Aaditya Ramdas; Jianbo Chen; Martin J. Wainwright; Michael I. Jordan (CDC, 2017)

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. - Danica J. Sutherland; Hsiao-Yu Tung; Heiko Strathmann; Soumyajit De; Aaditya Ramdas; Alexander J. Smola; Arthur Gretton (ICLR (Poster), 2017)

Online control of the false discovery rate with decaying memory. - Aaditya Ramdas; Fanny Yang; Martin J. Wainwright; Michael I. Jordan (NIPS, 2017)

A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control. - Fanny Yang; Aaditya Ramdas; Kevin G. Jamieson; Martin J. Wainwright (NIPS, 2017)

DAGGER: A sequential algorithm for FDR control on DAGs. - Aaditya Ramdas; Jianbo Chen; Martin J. Wainwright; Michael I. Jordan (CoRR, 2017)

On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests. - Aaditya Ramdas; Nicolás García Trillos; Marco Cuturi (Entropy, 2017)

Rows versus Columns: Randomized Kaczmarz or Gauss-Seidel for Ridge Regression. - Ahmed Hefny; Deanna Needell; Aaditya Ramdas (SIAM J. Sci. Comput., 2017)

SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate. - Aaditya Ramdas; Tijana Zrnic; Martin J. Wainwright; Michael I. Jordan (ICML, 2018)

Iterative Methods for Solving Factorized Linear Systems. - Anna Ma; Deanna Needell; Aaditya Ramdas (SIAM J. Matrix Anal. Appl., 2018)

A Higher-Order Kolmogorov-Smirnov Test. - Veeranjaneyulu Sadhanala; Yu-Xiang Wang 003; Aaditya Ramdas; Ryan J. Tibshirani (AISTATS, 2019)

Foundations of Large-Scale Sequential Experimentation. - Aaditya Ramdas (KDD, 2019)

Are sample means in multi-armed bandits positively or negatively biased? - Jaehyeok Shin; Aaditya Ramdas; Alessandro Rinaldo (NeurIPS, 2019)

ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. - Jinjin Tian; Aaditya Ramdas (NeurIPS, 2019)

Conformal Prediction Under Covariate Shift. - Ryan J. Tibshirani; Rina Foygel Barber; Emmanuel J. Candès; Aaditya Ramdas (NeurIPS, 2019)

Decoding from Pooled Data: Sharp Information-Theoretic Bounds. - Ahmed El Alaoui; Aaditya Ramdas; Florent Krzakala; Lenka Zdeborová; Michael I. Jordan (SIAM J. Math. Data Sci., 2019)

Decoding From Pooled Data: Phase Transitions of Message Passing. - Ahmed El Alaoui; Aaditya Ramdas; Florent Krzakala; Lenka Zdeborová; Michael I. Jordan (IEEE Trans. Inf. Theory, 2019)

The Power of Batching in Multiple Hypothesis Testing. - Tijana Zrnic; Daniel L. Jiang; Aaditya Ramdas; Michael I. Jordan (AISTATS, 2020)

Analyzing Student Strategies In Blended Courses Using Clickstream Data. - Nil-Jana Akpinar; Aaditya Ramdas; Umut A. Acar (EDM, 2020)

Familywise Error Rate Control by Interactive Unmasking. - Boyan Duan; Aaditya Ramdas; Larry A. Wasserman (ICML, 2020)

On Conditional Versus Marginal Bias in Multi-Armed Bandits. - Jaehyeok Shin; Aaditya Ramdas; Alessandro Rinaldo (ICML, 2020)

Online Control of the False Coverage Rate and False Sign Rate. - Asaf Weinstein; Aaditya Ramdas (ICML, 2020)

Distribution-free binary classification: prediction sets, confidence intervals and calibration. - Chirag Gupta; Aleksandr Podkopaev; Aaditya Ramdas (NeurIPS, 2020)

Confidence sequences for sampling without replacement. - Ian Waudby-Smith; Aaditya Ramdas (NeurIPS, 2020)

Best Arm Identification under Additive Transfer Bandits. - Ojash Neopane; Aaditya Ramdas; Aarti Singh (ACSCC, 2021)

Uncertainty quantification using martingales for misspecified Gaussian processes. - Willie Neiswanger; Aaditya Ramdas (ALT, 2021)

RiLACS: Risk Limiting Audits via Confidence Sequences. - Ian Waudby-Smith; Philip B. Stark; Aaditya Ramdas (E-VOTE-ID, 2021)

Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting. - Chirag Gupta; Aaditya Ramdas (ICML, 2021)

Off-Policy Confidence Sequences. - Nikos Karampatziakis; Paul Mineiro; Aaditya Ramdas (ICML, 2021)

Dynamic Algorithms for Online Multiple Testing. - Ziyu Xu 001; Aaditya Ramdas (MSML, 2021)

A unified framework for bandit multiple testing. - Ziyu Xu 001; Ruodu Wang; Aaditya Ramdas (NeurIPS, 2021)

Distribution-free uncertainty quantification for classification under label shift. - Aleksandr Podkopaev; Aaditya Ramdas (UAI, 2021)

How can one test if a binary sequence is exchangeable? Fork-convex hulls, supermartingales, and Snell envelopes. - Aaditya Ramdas; Johannes Ruf; Martin Larsson; Wouter M. Koolen (CoRR, 2021)

Sequential Estimation of Convex Divergences using Reverse Submartingales and Exchangeable Filtrations. - Tudor Manole; Aaditya Ramdas (CoRR, 2021)

Top-label calibration. - Chirag Gupta; Aaditya K. Ramdas (CoRR, 2021)

Universal Inference Meets Random Projections: A Scalable Test for Log-concavity. - Robin Dunn; Larry A. Wasserman; Aaditya Ramdas (CoRR, 2021)

Game-theoretic Formulations of Sequential Nonparametric One- and Two-Sample Tests. - Shubhanshu Shekhar; Aaditya Ramdas (CoRR, 2021)

Path Length Bounds for Gradient Descent and Flow. - Chirag Gupta; Sivaraman Balakrishnan; Aaditya Ramdas (J. Mach. Learn. Res., 2021)

Asynchronous Online Testing of Multiple Hypotheses. - Tijana Zrnic; Aaditya Ramdas; Michael I. Jordan (J. Mach. Learn. Res., 2021)

Nonparametric Iterated-Logarithm Extensions of the Sequential Generalized Likelihood Ratio Test. - Jaehyeok Shin; Aaditya Ramdas; Alessandro Rinaldo (IEEE J. Sel. Areas Inf. Theory, 2021)

On the Bias, Risk, and Consistency of Sample Means in Multi-armed Bandits. - Jaehyeok Shin; Aaditya Ramdas; Alessandro Rinaldo (SIAM J. Math. Data Sci., 2021)

Interactive rank testing by betting. - Boyan Duan; Aaditya Ramdas; Larry A. Wasserman (CLeaR, 2022)

Faster online calibration without randomization: interval forecasts and the power of two choices. - Chirag Gupta; Aaditya Ramdas (COLT, 2022)

Top-label calibration and multiclass-to-binary reductions. - Chirag Gupta; Aaditya Ramdas (ICLR, 2022)

Tracking the risk of a deployed model and detecting harmful distribution shifts. - Aleksandr Podkopaev; Aaditya Ramdas (ICLR, 2022)

A permutation-free kernel two-sample test. - Shubhanshu Shekhar; Ilmun Kim; Aaditya Ramdas (NeurIPS, 2022)

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. - Justin Whitehouse; Aaditya Ramdas; Zhiwei Steven Wu; Ryan M. Rogers (NeurIPS, 2022)

Locally private nonparametric confidence intervals and sequences. - Ian Waudby-Smith; Zhiwei Steven Wu; Aaditya Ramdas (CoRR, 2022)

A composite generalization of Ville's martingale theorem. - Johannes Ruf; Martin Larsson; Wouter M. Koolen; Aaditya Ramdas (CoRR, 2022)

Game-theoretic statistics and safe anytime-valid inference. - Aaditya Ramdas; Peter Grünwald; Vladimir Vovk; Glenn Shafer (CoRR, 2022)

Anytime-valid off-policy inference for contextual bandits. - Ian Waudby-Smith; Lili Wu; Aaditya Ramdas; Nikos Karampatziakis; Paul Mineiro (CoRR, 2022)

Testing exchangeability: Fork-convexity, supermartingales and e-processes. - Aaditya Ramdas; Johannes Ruf; Martin Larsson; Wouter M. Koolen (Int. J. Approx. Reason., 2022)

Nested conformal prediction and quantile out-of-bag ensemble methods. - Chirag Gupta; Arun K. Kuchibhotla; Aaditya Ramdas (Pattern Recognit., 2022)

Huber-robust confidence sequences. - Hongjian Wang; Aaditya Ramdas (AISTATS, 2023)

Online Platt Scaling with Calibeating. - Chirag Gupta; Aaditya Ramdas (ICML, 2023)

Sequential Kernelized Independence Testing. - Aleksandr Podkopaev; Patrick Blöbaum; Shiva Prasad Kasiviswanathan; Aaditya Ramdas (ICML, 2023)

Sequential Changepoint Detection via Backward Confidence Sequences. - Shubhanshu Shekhar; Aaditya Ramdas (ICML, 2023)

Nonparametric Extensions of Randomized Response for Private Confidence Sets. - Ian Waudby-Smith; Zhiwei Steven Wu; Aaditya Ramdas (ICML, 2023)

Fully-Adaptive Composition in Differential Privacy. - Justin Whitehouse; Aaditya Ramdas; Ryan Rogers 002; Steven Wu 001 (ICML, 2023)

Counterfactually Comparing Abstaining Classifiers. - Yo Joong Choe; Aditya Gangrade; Aaditya Ramdas (NeurIPS, 2023)

Auditing Fairness by Betting. - Ben Chugg; Santiago Cortes-Gomez; Bryan Wilder; Aaditya Ramdas (NeurIPS, 2023)

An Efficient Doubly-Robust Test for the Kernel Treatment Effect. - Diego Martinez-Taboada; Aaditya Ramdas; Edward Kennedy (NeurIPS, 2023)

Sequential Predictive Two-Sample and Independence Testing. - Aleksandr Podkopaev; Aaditya Ramdas (NeurIPS, 2023)

Adaptive Privacy Composition for Accuracy-first Mechanisms. - Ryan M. Rogers; Gennady Samorodnitsky; Zhiwei Steven Wu; Aaditya Ramdas (NeurIPS, 2023)

On the Sublinear Regret of GP-UCB. - Justin Whitehouse; Aaditya Ramdas; Zhiwei Steven Wu (NeurIPS, 2023)

Risk-limiting financial audits via weighted sampling without replacement. - Shubhanshu Shekhar; Ziyu Xu 001; Zachary C. Lipton; Pierre J. Liang; Aaditya Ramdas (UAI, 2023)

Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform. - Akash Maharaj; Ritwik Sinha; David Arbour; Ian Waudby-Smith; Simon Z. Liu; Moumita Sinha; Raghavendra Addanki; Aaditya Ramdas; Manas Garg; Viswanathan Swaminathan 001 (WWW (Companion Volume), 2023)

Sequential change detection via backward confidence sequences. - Shubhanshu Shekhar; Aaditya Ramdas (CoRR, 2023)

Randomized and Exchangeable Improvements of Markov's, Chebyshev's and Chernoff's Inequalities. - Aaditya Ramdas; Tudor Manole (CoRR, 2023)

When do exact and powerful p-values and e-values exist? - Zhenyuan Zhang; Aaditya Ramdas; Ruodu Wang (CoRR, 2023)

Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB. - Justin Whitehouse; Zhiwei Steven Wu; Aaditya Ramdas (CoRR, 2023)

On the near-optimality of betting confidence sets for bounded means. - Shubhanshu Shekhar; Aaditya Ramdas (CoRR, 2023)

Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance. - Hongjian Wang; Aaditya Ramdas (CoRR, 2023)

A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds. - Ben Chugg; Hongjian Wang; Aaditya Ramdas (J. Mach. Learn. Res., 2023)

A Permutation-Free Kernel Independence Test. - Shubhanshu Shekhar; Ilmun Kim; Aaditya Ramdas (J. Mach. Learn. Res., 2023)

Martingale Methods for Sequential Estimation of Convex Functionals and Divergences. - Tudor Manole; Aaditya Ramdas (IEEE Trans. Inf. Theory, 2023)

Differentially Private Conditional Independence Testing. - Iden Kalemaj; Shiva Prasad Kasiviswanathan; Aaditya Ramdas (AISTATS, 2024)

Graph fission and cross-validation. - James Leiner; Aaditya Ramdas (AISTATS, 2024)

Deep anytime-valid hypothesis testing. - Teodora Pandeva; Patrick Forré; Aaditya Ramdas; Shubhanshu Shekhar (AISTATS, 2024)

Testing exchangeability by pairwise betting. - Aytijhya Saha; Aaditya Ramdas (AISTATS, 2024)

Online multiple testing with e-values. - Ziyu Xu 001; Aaditya Ramdas (AISTATS, 2024)

Semiparametric Efficient Inference in Adaptive Experiments. - Thomas Cook; Alan Mishler; Aaditya Ramdas (CLeaR, 2024)

Reducing sequential change detection to sequential estimation. - Shubhanshu Shekhar; Aaditya Ramdas (ICML, 2024)

Total Variation Floodgate for Variable Importance Inference in Classification. - Wenshuo Wang 002; Lucas Janson; Lihua Lei; Aaditya Ramdas (ICML, 2024)

Bias Detection via Signaling. - Yiling Chen 001; Tao Lin 013; Ariel D. Procaccia; Aaditya Ramdas; Itai Shapira (NeurIPS, 2024)

Combining Evidence Across Filtrations. - Yo Joong Choe; Aaditya Ramdas (CoRR, 2024)

Conformal online model aggregation. - Matteo Gasparin; Aaditya Ramdas (CoRR, 2024)

β-calibration of Language Model Confidence Scores for Generative QA. - Putra Manggala; Atalanti-Anastasia Mastakouri; Elke Kirschbaum; Shiva Prasad Kasiviswanathan; Aaditya Ramdas (CoRR, 2024)

Comparing Sequential Forecasters. - Yo Joong Choe; Aaditya Ramdas (Oper. Res., 2024)

Nonparametric Two-Sample Testing by Betting. - Shubhanshu Shekhar; Aaditya Ramdas (IEEE Trans. Inf. Theory, 2024)

Sequential Kernelized Stein Discrepancy. - Diego Martinez-Taboada; Aaditya Ramdas (AISTATS, 2025)

Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect. - Ojash Neopane; Aaditya Ramdas; Aarti Singh (AISTATS, 2025)

Scalable Causal Structure Learning via Amortized Conditional Independence Testing. - James Leiner; Brian Manzo; Aaditya Ramdas; Wesley Tansey (CLeaR, 2025)

Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract). - Justin Whitehouse; Zhiwei Steven Wu; Aaditya Ramdas (COLT, 2025)

QA-Calibration of Language Model Confidence Scores. - Putra Manggala; Atalanti-Anastasia Mastakouri; Elke Kirschbaum; Shiva Prasad Kasiviswanathan; Aaditya Ramdas (ICLR, 2025)

Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback. - Michelle D. Zhao; Henny Admoni; Reid G. Simmons; Aaditya Ramdas; Andrea Bajcsy (ICLR, 2025)

Universal Inference Meets Random Projections: A Scalable Test for Log-Concavity. - Robin Dunn; Aditya Gangrade; Larry A. Wasserman; Aaditya Ramdas (J. Comput. Graph. Stat., 2025)

Time-Uniform Confidence Spheres for Means of Random Vectors. - Ben Chugg; Hongjian Wang; Aaditya Ramdas (Trans. Mach. Learn. Res., 2025)

Online Selective Conformal Inference: Errors and Solutions. - Yusuf Sale; Aaditya Ramdas (Trans. Mach. Learn. Res., 2025)

Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect. - Ojash Neopane; Aaditya Ramdas; Aarti Singh (CoRR, 2025)

Post-detection inference for sequential changepoint localization. - Aytijhya Saha; Aaditya Ramdas (CoRR, 2025)

Improving the statistical efficiency of cross-conformal prediction. - Matteo Gasparin; Aaditya Ramdas (CoRR, 2025)

Online Selective Conformal Prediction: Errors and Solutions. - Yusuf Sale; Aaditya Ramdas (CoRR, 2025)

On Stopping Times of Power-one Sequential Tests: Tight Lower and Upper Bounds. - Shubhada Agrawal; Aaditya Ramdas (CoRR, 2025)

Private Evolution Converges. - Tomás González; Giulia Fanti; Aaditya Ramdas (CoRR, 2025)