I am an Assistant Professor at the
Machine Learning Department in Carnegie Mellon University.
I received my PhD in the Computer Science Department at Princeton University under the advisement of Sanjeev Arora.
My research interests lie in machine learning and statistics, spanning topics like representation learning, generative models, word embeddings, variational inference and MCMC and non-convex optimization. The broad goal of my research is principled and mathematical understanding of statistical and algorithmic phenomena and problems arising in modern machine learning.
The easiest way to reach me is email. My address is aristesk
(In alphabetical order, following the tradition in theoretical computer science)
Understanding representations in supervised and unsupervised learning
Parametric Complexity Bounds for Approximating PDEs with Neural Networks. With Tanya Marwah and Zachary C. Lipton. Manuscript 2021.
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization. With Elan Rosenfeld and Pradeep Ravikumar. Manuscript 2021.
The Risks of Invariant Risk Minimization. With Elan Rosenfeld and Pradeep Ravikumar. ICLR 2021.
Representational aspects of depth and conditioning in normalizing flows. With Frederic Koehler and Viraj Mehta. Manuscript 2020.
On Learning Language-Invariant Representations for Universal Machine Translation. With Han Zhao, Junjie Hu. ICML 2020.
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models. With Rares Buhai, Yoni Halpern and David Sontag. ICML 2020.
Approximability of Discriminators Implies Diversity in GANs. With Yu Bai and Tengyu Ma. ICLR 2019.
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. With Frederic Koehler. ICLR 2019.
Do GANs learn the distribution? Some theory and empirics. With Sanjeev Arora and Yi Zhang. ICLR 2018
Linear algebraic structure of word senses, with applications to polysemy. With Sanjeev Arora, Yuanzhi Li, Yingyu Liang and Tengyu Ma. Transactions of the Association for Computat
ional Linguistics (TACL), 2018
Automated WordNet Construction Using Word Embeddings. With Mikhail Khodak, Christiane Fellbaum, Sanjeev Arora. EACL Workshop on Sense, Concept and Entity Representation
s and their Applications, 2017
On the ability of neural nets to express distributions. With Holden Lee, Rong Ge, Tengyu Ma, Sanjeev Arora. COLT 2017
RAND-WALK: a latent variable model approach to word embeddings. With Sanjeev Arora, Yuanzhi Li, Yingyu Liang and Tengyu Ma. Transactions of the Association for Computational Linguistics (TACL), 2016
Provable algorithms for learning and inference in probabilistic and generative models
Computational issues around online and improper learning
Diffusing along manifolds of local optima via Langevin dynamics
Microsoft Research New England, 03/19
MIFODS Workshop on Learning with Complex Structure, MIT, 01/20
Mean-field approximation and variational methods via convex relaxations
Harvard Physics and Computation Seminar, 10/18
MIT Seminar on Stochastic Processes, 11/18
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
MIT Algorithms and Complexity Seminar, 11/01/17
Provable algorithms for learning noisy-OR networks
Theoretical aspects of representation learning
Simons Institute for the Theory of Computing, 03/27/17
New techniques for learning and inference in probabilistic graphical models
MIT Stochastics and Statistics Seminar, 09/08/17
Microsoft Research Redmond, 02/08/17
How to calculate partition functions using convex programming hierarchies: provable bounds for variational methods
Stanford Theory Seminar, 02/02/17
Los Alamos National Laboratory, 11/07/16
Rutgers University, 10/19/16
COLT (New York City, 2016)
On some provably correct cases of variational inference for topic models
Random walks on context spaces: towards an explanation of the mysteries of semantic word embeddings
China Theory Week (Jiao Tong University, Shanghai, 2015)
Label optimal regret bounds for online local learning