3 citations · 7 across the 11 of their papers we have counts for
11 papers
Conditioned free-energy density of proteins using unbalanced solutions to constraint satisfaction problems
Pratik Worah, Subhash Khot, Srinivasa Varadhan
We show that computing the log-partition function (free-energy) of conditioned inhomogeneous Curie--Weiss spin Hamiltonians reduces to an unbalanced norm computation, and…
Sampling and Loss Weights in Multi-Domain Training
Mahdi Salmani, Pratik Worah, Meisam Razaviyayn +1
In the training of large deep neural networks, there is a need for vast amounts of training data. To meet this need, data is collected from multiple domains, such as Wikipedia and…
A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials
Pratik Worah
We design an efficient algorithm that outputs tests for identifying predominantly homogeneous subcohorts of patients from large in-homogeneous datasets. Our theoretical contributio…
Enhancing selectivity using Wasserstein distance based reweighing
Pratik Worah
Given two labeled data-sets and , we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of t…
Description Complexity of Regular Distributions
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
Myerson's regularity condition of a distribution is a standard assumption in economics. In this paper, we study the complexity of describing a regular distribution within a small s…
Learning Rate Schedules in the Presence of Distribution Shift
Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni +1
We design learning rate schedules that minimize regret for SGD-based online learning in the presence of a changing data distribution. We fully characterize the optimal learning rat…
Pricing Query Complexity of Revenue Maximization
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most wit…
Learning to Price Against a Moving Target
Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng +1
In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood whe…
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologicall…
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Minimax optimization has become a central tool in machine learning with applications in robust optimization, reinforcement learning, GANs, etc. These applications are often nonconv…
A Characterization of Approximation Resistance
Subhash Khot, Madhur Tulsiani, Pratik Worah
A predicate f:{-1,1}^k -> {0,1} with ρ(f) = \frac{|f^{-1}(1)|}{2^k} is called {\it approximation resistant} if given a near-satisfiable instance of CSP(f), it is computationally ha…