Aaditya Ramdas — aramdas [@] cs [dot] cmu [.] edu
8223 Gates (CMU), Pittsburgh

5th Year Joint PhD Student, Machine Learning and Statistics
School of Computer Science, Carnegie Mellon University

My advisors are Larry Wasserman (Stats) and Aarti Singh (ML). My other thesis committee members are Ryan Tibshirani (CMU), Michael Jordan (UC Berkeley) and Arthur Gretton (UC London). I am looking for post-docs starting Fall 2015.


News (last 12 months)


Peer-Reviewed Papers (low acceptance rates)
  1. Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses (PLOS ONE)
    Leila Wehbe, Brian Murphy, Partha Talukdar, Alona Fyshe, A.R., Tom Mitchell [in press]
  2. Margins, Kernels and Non-linear Smoothed Perceptrons (ICML '14)
    31st International Conference on Machine Learning, Beijing, 2014
    A.R., Javier Pena [ICML] [pdf] [supp] [20-min oral]
  3. An Analysis of Active Learning with Uniform Feature Noise (AISTATS '14)
    17th International Conference on Artificial Intelligence and Statistics, Reykjavik, 2014
    A.R., Aarti Singh, Larry Wasserman, Barnabas Poczos [AISTATS] [pdf] [supp] [25-min oral]
  4. Algorithmic Connections Between Active Learning and Stochastic Convex Optimization (ALT '13)
    24th International Conference on Algorithmic Learning Theory, Singapore, 2013
    A.R., Aarti Singh [ALT] [pdf] [25-min oral]

Some Relevant Preprints
  1. On The Decreasing Power of Kernel and Distance based Hypothesis Tests in High Dimensions
    A.R., **Sashank Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman [arxiv]
  2. Fast ADMM Algorithms for Trend Filtering
    A.R., Ryan Tibshirani [arxiv][R package][talk]
  3. Towards A Deeper Geometric, Analytic and Algorithmic Understanding of Margins
    A.R., Javier Pena [arxiv]
  4. On The Increasing Power of Kernel Independence Testing due to Stein Shrinkage
    A.R., **Leila Wehbe [arxiv]
  5. Rows vs Columns for Linear Systems of Equations: Randomized Kaczmarz vs Coordinate Descent
    A.R. [arxiv]

Non-Conference Talks and Posters
  1. Margins: Geometry, Analysis and Algorithms, UCLA IPAM Stochastic Gradient Methods
  2. Increasing Power of Kernel Independence Testing by Stein Shrinkage, UCL-Duke Workshop on High-Dimensional Inference
  3. Decreasing Power of Kernel Hypothesis Testing in High Dimensions, UCL-Duke Workshop on High-Dimensional Inference
  4. A Case for Trend Filtering over Splines, Microsoft Research Cambridge
  5. A Case for Trend Filtering over Splines, Gatsby Neuroscience Unit (UCL)
  6. A Case for Trend Filtering over Splines, ML Lunch Seminar (CMU)
  7. Connecting Convex Optimization and Active Learning, IIT Madras
  8. Connecting Convex Optimization and Active Learning, IBM Research Bangalore
  9. Connecting Convex Optimization and Active Learning, Chennai Math. Institute

Reviewing
  1. Journal of Machine Learning Research
  2. Biometrika
  3. Computational Optimization and Applications
  4. Neural Information Processing Systems
  5. International Conference on Machine Learning

Miscellaneous
  1. Co-organizing NIPS OPT 2014 - Workshop on Optimization in ML.
  2. Made pre-requisite review videos for CMU's graduate courses in ML.