Aleksander Madry

According to our database, Aleksander Madry authored at least 100 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Attribute-to-Delete: Machine Unlearning via Datamodel Matching.

Kristian Georgiev, Roy Rinberg, Sung Min Park, Shivam Garg, Andrew Ilyas, Aleksander Madry, Seth Neel

CoRR, 2024

GPT-4o System Card.

Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, Aleksander Madry, Alex Baker-Whitcomb, Alex Beutel, Alex Borzunov, Alex Carney, Alex Chow, Alex Kirillov, Alex Nichol, Alex Paino, Alex Renzin, Alex Tachard Passos, Alexander Kirillov, Alexi Christakis, Alexis Conneau, Ali Kamali, Allan Jabri, Allison Moyer, Allison Tam, Amadou Crookes, Amin Tootoonchian, Ananya Kumar, Andrea Vallone, Andrej Karpathy, Andrew Braunstein, Andrew Cann, Andrew Codispoti, Andrew Galu, Andrew Kondrich, Andrew Tulloch, Andrey Mishchenko, Angela Baek, Angela Jiang, Antoine Pelisse, Antonia Woodford, Anuj Gosalia, Arka Dhar, Ashley Pantuliano, Avi Nayak, Avital Oliver, Barret Zoph, Behrooz Ghorbani, Ben Leimberger, Ben Rossen, Ben Sokolowsky, Ben Wang, Benjamin Zweig, Beth Hoover, Blake Samic, Bob McGrew, Bobby Spero, Bogo Giertler, Bowen Cheng, Brad Lightcap, Brandon Walkin, Brendan Quinn, Brian Guarraci, Brian Hsu, Bright Kellogg, Brydon Eastman, Camillo Lugaresi, Carroll L. Wainwright, Cary Bassin, Cary Hudson, Casey Chu, Chad Nelson, Chak Li, Chan Jun Shern, Channing Conger, Charlotte Barette, Chelsea Voss, Chen Ding, Cheng Lu, Chong Zhang, Chris Beaumont, Chris Hallacy, Chris Koch, Christian Gibson, Christina Kim, Christine Choi, Christine McLeavey, Christopher Hesse, Claudia Fischer, Clemens Winter, Coley Czarnecki, Colin Jarvis, Colin Wei, Constantin Koumouzelis, Dane Sherburn

CoRR, 2024

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering.

Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, Aleksander Madry

CoRR, 2024

ContextCite: Attributing Model Generation to Context.

Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry

CoRR, 2024

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection.

Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry

CoRR, 2024

Ask Your Distribution Shift if Pre-Training is Right for You.

Benjamin Cohen-Wang, Joshua Vendrow, Aleksander Madry

CoRR, 2024

User Strategization and Trustworthy Algorithms.

Sarah H. Cen, Andrew Ilyas, Aleksander Madry

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content.

Sarah H. Cen, Andrew Ilyas, Jennifer Allen, Hannah Li, Aleksander Madry

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

Decomposing and Editing Predictions by Modeling Model Computation.

Harshay Shah, Andrew Ilyas, Aleksander Madry

Proceedings of the Forty-first International Conference on Machine Learning, 2024

DsDm: Model-Aware Dataset Selection with Datamodels.

Logan Engstrom, Axel Feldmann, Aleksander Madry

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses.

Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein

IEEE Trans. Pattern Anal. Mach. Intell., 2023

The Journey, Not the Destination: How Data Guides Diffusion Models.

Kristian Georgiev, Joshua Vendrow, Hadi Salman, Sung Min Park, Aleksander Madry

CoRR, 2023

A User-Driven Framework for Regulating and Auditing Social Media.

Sarah H. Cen, Aleksander Madry, Devavrat Shah

CoRR, 2023

Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation.

Joshua Vendrow, Saachi Jain, Logan Engstrom, Aleksander Madry

CoRR, 2023

ModelDiff: A Framework for Comparing Learning Algorithms.

Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

Raising the Cost of Malicious AI-Powered Image Editing.

Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

TRAK: Attributing Model Behavior at Scale.

Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

Rethinking Backdoor Attacks.

Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2023

Distilling Model Failures as Directions in Latent Space.

Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

FFCV: Accelerating Training by Removing Data Bottlenecks.

Guillaume Leclerc, Andrew Ilyas, Logan Engstrom, Sung Min Park, Hadi Salman, Aleksander Madry

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

A Data-Based Perspective on Transfer Learning.

Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

When does Bias Transfer in Transfer Learning?

Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry

CoRR, 2022

Adversarially trained neural representations may already be as robust as corresponding biological neural representations.

Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo

CoRR, 2022

Datamodels: Predicting Predictions from Training Data.

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry

CoRR, 2022

3DB: A Framework for Debugging Computer Vision Models.

Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Yuanqing Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Combining Diverse Feature Priors.

Saachi Jain, Dimitris Tsipras, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2022

Datamodels: Understanding Predictions with Data and Data with Predictions.

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry

Proceedings of the International Conference on Machine Learning, 2022

Adversarially trained neural representations are already as robust as biological neural representations.

Chong Guo, Michael J. Lee, Guillaume Leclerc, Joel Dapello, Yug Rao, Aleksander Madry, James J. DiCarlo

Proceedings of the International Conference on Machine Learning, 2022

Missingness Bias in Model Debugging.

Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry

Proceedings of the Tenth International Conference on Learning Representations, 2022

Certified Patch Robustness via Smoothed Vision Transformers.

Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

On Distinctive Properties of Universal Perturbations.

Sung Min Park, Kuo-An Wei, Kai Yuanqing Xiao, Jerry Li, Aleksander Madry

CoRR, 2021

Editing a classifier by rewriting its prediction rules.

Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unadversarial Examples: Designing Objects for Robust Vision.

Hadi Salman, Andrew Ilyas, Logan Engstrom, Sai Vemprala, Aleksander Madry, Ashish Kapoor

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Leveraging Sparse Linear Layers for Debuggable Deep Networks.

Eric Wong, Shibani Santurkar, Aleksander Madry

Proceedings of the 38th International Conference on Machine Learning, 2021

Noise or Signal: The Role of Image Backgrounds in Object Recognition.

Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry

Proceedings of the 9th International Conference on Learning Representations, 2021

BREEDS: Benchmarks for Subpopulation Shift.

Shibani Santurkar, Dimitris Tsipras, Aleksander Madry

Proceedings of the 9th International Conference on Learning Representations, 2021

Faster Sparse Minimum Cost Flow by Electrical Flow Localization.

Kyriakos Axiotis, Aleksander Madry, Adrian Vladu

Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

2020

Round Compression for Parallel Matching Algorithms.

Artur Czumaj, Jakub Lacki, Aleksander Madry, Slobodan Mitrovic, Krzysztof Onak, Piotr Sankowski

SIAM J. Comput., 2020

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

CoRR, 2020

The Two Regimes of Deep Network Training.

Guillaume Leclerc, Aleksander Madry

CoRR, 2020

On Adaptive Attacks to Adversarial Example Defenses.

Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Do Adversarially Robust ImageNet Models Transfer Better?

Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks.

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, Aleksander Madry

Proceedings of the 37th International Conference on Machine Learning, 2020

Identifying Statistical Bias in Dataset Replication.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry

Proceedings of the 37th International Conference on Machine Learning, 2020

A Closer Look at Deep Policy Gradients.

Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

Proceedings of the 8th International Conference on Learning Representations, 2020

Implementation Matters in Deep RL: A Case Study on PPO and TRPO.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

Proceedings of the 8th International Conference on Learning Representations, 2020

Circulation Control for Faster Minimum Cost Flow in Unit-Capacity Graphs.

Kyriakos Axiotis, Aleksander Madry, Adrian Vladu

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

2019

Label-Consistent Backdoor Attacks.

Alexander Turner, Dimitris Tsipras, Aleksander Madry

CoRR, 2019

Computer Vision with a Single (Robust) Classifier.

Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Madry

CoRR, 2019

Learning Perceptually-Aligned Representations via Adversarial Robustness.

Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry

CoRR, 2019

On Evaluating Adversarial Robustness.

Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin

CoRR, 2019

Image Synthesis with a Single (Robust) Classifier.

Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Examples Are Not Bugs, They Are Features.

Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exploring the Landscape of Spatial Robustness.

Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

Proceedings of the 36th International Conference on Machine Learning, 2019

Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability.

Kai Yuanqing Xiao, Vincent Tjeng, Nur Muhammad (Mahi) Shafiullah, Aleksander Madry

Proceedings of the 7th International Conference on Learning Representations, 2019

Robustness May Be at Odds with Accuracy.

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry

Proceedings of the 7th International Conference on Learning Representations, 2019

Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors.

Andrew Ilyas, Logan Engstrom, Aleksander Madry

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?

Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

CoRR, 2018

There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits).

Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry

CoRR, 2018

How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift).

Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry

CoRR, 2018

k-server via multiscale entropic regularization.

Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, James R. Lee, Aleksander Madry

Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

Spectral Signatures in Backdoor Attacks.

Brandon Tran, Jerry Li, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Adversarially Robust Generalization Requires More Data.

Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

How Does Batch Normalization Help Optimization?

Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Classification-Based Study of Covariate Shift in GAN Distributions.

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

Proceedings of the 35th International Conference on Machine Learning, 2018

On the Limitations of First-Order Approximation in GAN Dynamics.

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

Proceedings of the 35th International Conference on Machine Learning, 2018

Towards Deep Learning Models Resistant to Adversarial Attacks.

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu

Proceedings of the 6th International Conference on Learning Representations, 2018

A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians.

Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

On the Resiliency of Static Forwarding Tables.

Marco Chiesa, Ilya Nikolaevskiy, Slobodan Mitrovic, Andrei V. Gurtov, Aleksander Madry, Michael Schapira, Scott Shenker

IEEE/ACM Trans. Netw., 2017

An <i>O</i>(log <i>n</i>/log log <i>n</i>)-Approximation Algorithm for the Asymmetric Traveling Salesman Problem.

Arash Asadpour, Michel X. Goemans, Aleksander Madry, Shayan Oveis Gharan, Amin Saberi

Oper. Res., 2017

A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations.

Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

CoRR, 2017

A Classification-Based Perspective on GAN Distributions.

Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

CoRR, 2017

Towards Understanding the Dynamics of Generative Adversarial Networks.

Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

CoRR, 2017

Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in Õ (<i>m</i>^10/7 log <i>W</i>) Time (Extended Abstract).

Michael B. Cohen, Aleksander Madry, Piotr Sankowski, Adrian Vladu

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods.

Michael B. Cohen, Aleksander Madry, Dimitris Tsipras, Adrian Vladu

Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

2016

Negative-Weight Shortest Paths and Unit Capacity Minimum Cost Flow in Õ(m^10/7 log W) Time.

Michael B. Cohen, Aleksander Madry, Piotr Sankowski, Adrian Vladu

CoRR, 2016

The quest for resilient (static) forwarding tables.

Marco Chiesa, Ilya Nikolaevskiy, Slobodan Mitrovic, Aurojit Panda, Andrei V. Gurtov, Aleksander Madry, Michael Schapira, Scott Shenker

Proceedings of the 35th Annual IEEE International Conference on Computer Communications, 2016

On the Resiliency of Randomized Routing Against Multiple Edge Failures.

Marco Chiesa, Andrei V. Gurtov, Aleksander Madry, Slobodan Mitrovic, Ilya Nikolaevskiy, Michael Schapira, Scott Shenker

Proceedings of the 43rd International Colloquium on Automata, Languages, and Programming, 2016

Continuous Optimization: The "Right" Language for Graph Algorithms? (Invited Talk).

Aleksander Madry

Proceedings of the 36th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2016

Computing Maximum Flow with Augmenting Electrical Flows.

Aleksander Madry

Proceedings of the IEEE 57th Annual Symposium on Foundations of Computer Science, 2016

2015

On the configuration LP for maximum budgeted allocation.

Christos Kalaitzis, Aleksander Madry, Alantha Newman, Lukás Polácek, Ola Svensson

Math. Program., 2015

A Polylogarithmic-Competitive Algorithm for the <i>k</i>-Server Problem.

Nikhil Bansal, Niv Buchbinder, Aleksander Madry, Joseph Naor

J. ACM, 2015

Fast Generation of Random Spanning Trees and the Effective Resistance Metric.

Aleksander Madry, Damian Straszak, Jakub Tarnawski

Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

2013

Runtime guarantees for regression problems.

Hui Han Chin, Aleksander Madry, Gary L. Miller, Richard Peng

Proceedings of the Innovations in Theoretical Computer Science, 2013

Navigating Central Path with Electrical Flows: From Flows to Matchings, and Back.

Aleksander Madry

Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

2011

New techniques for graph algorithms.

Aleksander Madry

PhD thesis, 2011

Maximum bipartite flow in networks with adaptive channel width.

Yossi Azar, Aleksander Madry, Thomas Moscibroda, Debmalya Panigrahi, Aravind Srinivasan

Theor. Comput. Sci., 2011

Electrical Flow Algorithms for Total Variation Minimization

Aleksander Madry, Gary L. Miller, Richard Peng

CoRR, 2011

Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs.

Paul F. Christiano, Jonathan A. Kelner, Aleksander Madry, Daniel A. Spielman, Shang-Hua Teng

Proceedings of the 43rd ACM Symposium on Theory of Computing, 2011

The Semi-stochastic Ski-rental Problem.

Aleksander Madry, Debmalya Panigrahi

Proceedings of the IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2011

A Polylogarithmic-Competitive Algorithm for the k-Server Problem.

Nikhil Bansal, Niv Buchbinder, Aleksander Madry, Joseph Naor

Proceedings of the IEEE 52nd Annual Symposium on Foundations of Computer Science, 2011

2010

Faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms.

Aleksander Madry

Proceedings of the 42nd ACM Symposium on Theory of Computing, 2010

An O(log n/ log log n)-approximation Algorithm for the Asymmetric Traveling Salesman Problem.

Arash Asadpour, Michel X. Goemans, Aleksander Madry, Shayan Oveis Gharan, Amin Saberi

Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, 2010

Fast Approximation Algorithms for Cut-Based Problems in Undirected Graphs.

Aleksander Madry

Proceedings of the 51th Annual IEEE Symposium on Foundations of Computer Science, 2010

2009

Faster Generation of Random Spanning Trees.

Jonathan A. Kelner, Aleksander Madry

Proceedings of the 50th Annual IEEE Symposium on Foundations of Computer Science, 2009

A 7/9 - Approximation Algorithm for the Maximum Traveling Salesman Problem.

Katarzyna E. Paluch, Marcin Mucha, Aleksander Madry

Proceedings of the Approximation, 2009

2008

Geometric Aspects of Online Packet Buffering: An Optimal Randomized Algorithm for Two Buffers.

Marcin Bienkowski, Aleksander Madry

Proceedings of the LATIN 2008: Theoretical Informatics, 2008

Susceptible Two-Party Quantum Computations.

Andreas Jakoby, Maciej Liskiewicz, Aleksander Madry

Proceedings of the Information Theoretic Security, Third International Conference, 2008

2006

Using Quantum Oblivious Transfer to Cheat Sensitive Quantum Bit Commitment.

Andreas Jakoby, Maciej Liskiewicz, Aleksander Madry

Electron. Colloquium Comput. Complex., 2006

2005

Data exchange: On the complexity of answering queries with inequalities.

Aleksander Madry

Inf. Process. Lett., 2005