337 citations · 369 across the 21 of their papers we have counts for
16 papers · 1 filter
Learning Straight Flows by Learning Curved Interpolants
Shiv Shankar, Tomas Geffner
Flow matching models typically use linear interpolants to define the forward/noise addition process. This, together with the independent coupling between noise and target distribut…
A/B testing under Interference with Partial Network Information
Shiv Shankar, Ritwik Sinha, Yash Chandak +2
A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control t…
Adaptive Instrument Design for Indirect Experiments
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis +1
Indirect experiments provide a valuable framework for estimating treatment effects in situations where conducting randomized control trials (RCTs) is impractical or unethical. Unli…
Optimization using Parallel Gradient Evaluations on Multiple Parameters
Yash Chandak, Shiv Shankar, Venkata Gandikota +2
We propose a first-order method for convex optimization, where instead of being restricted to the gradient from a single parameter, gradients from multiple parameters can be used d…
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian +3
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary. This limits the application of such method…
Implicit Training of Energy Model for Structure Prediction
Shiv Shankar, Vihari Piratla
Most deep learning research has focused on developing new model and training procedures. On the other hand the training objective has usually been restricted to combinations of sta…
Progressive Fusion for Multimodal Integration
Shiv Shankar, Laure Thompson, Madalina Fiterau
Integration of multimodal information from various sources has been shown to boost the performance of machine learning models and thus has received increased attention in recent ye…
Adversarial Stein Training for Graph Energy Models
Shiv Shankar
Learning distributions over graph-structured data is a challenging task with many applications in biology and chemistry. In this work we use an energy-based model (EBM) based on mu…
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Yash Chandak, Shiv Shankar, Philip S. Thomas
Many sequential decision-making systems leverage data collected using prior policies to propose a new policy. For critical applications, it is important that high-confidence guaran…
Bosonic Random Walk Networks for Graph Learning
Shiv Shankar, Don Towsley
The development of Graph Neural Networks (GNNs) has led to great progress in machine learning on graph-structured data. These networks operate via diffusing information across the…
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint
Vihari Piratla, Shiv Shankar
Data augmentation is a popular pre-processing trick to improve generalization accuracy. It is believed that by processing augmented inputs in tandem with the original ones, the mod…
Optimizing for the Future in Non-Stationary MDPs
Yash Chandak, Georgios Theocharous, Shiv Shankar +3
Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process i…
Differential Equation Units: Learning Functional Forms of Activation Functions from Data
MohamadAli Torkamani, Shiv Shankar, Amirmohammad Rooshenas +1
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential e…
Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
MohamadAli Torkamani, Phillip Wallis, Shiv Shankar +1
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential e…
Generalizing Across Domains via Cross-Gradient Training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti +3
We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or u…
Labeled Memory Networks for Online Model Adaptation
Shiv Shankar, Sunita Sarawagi
Augmenting a neural network with memory that can grow without growing the number of trained parameters is a recent powerful concept with many exciting applications. We propose a de…