16 citations · 73 across the 29 of their papers we have counts for
34 papers
Conditional Flow Matching for Transport Between Markov Processes
Syamantak Kumar, Dheeraj Nagaraj, Saptarshi Roy +1
Motivated by sequence-to-sequence transport in the context time-series domain adaptation, we study the problem of transportation between trajectories of Markov processes. Given a l…
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
Poisson Midpoint Method for Log Concave Sampling: Beyond the Strong Error Lower Bounds
Rishikesh Srinivasan, Dheeraj Nagaraj
We study the problem of sampling from strongly log-concave distributions over using the Poisson midpoint discretization (a variant of the randomized midpoint method)…
Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
Chandan Tankala, Dheeraj M. Nagaraj, Anant Raj
Gradient flow in the 2-Wasserstein space is widely used to optimize functionals over probability distributions and is typically implemented using an interacting particle system wit…
Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
Gautham Govind Anil, Sachin Yadav, Dheeraj Nagaraj +2
We introduce Interleaved Gibbs Diffusion (IGD), a novel generative modeling framework for discrete-continuous data, focusing on problems with important, implicit and unspecified co…
Dimension-free Score Matching and Time Bootstrapping for Diffusion Models
Syamantak Kumar, Dheeraj Nagaraj, Purnamrita Sarkar
Diffusion models generate samples by estimating the score function of the target distribution at various noise levels. The model is trained using samples drawn from the target dist…
Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD
Aniket Das, Dheeraj Nagaraj, Soumyabrata Pal +2
We consider the problem of high-dimensional heavy-tailed statistical estimation in the streaming setting, which is much harder than the traditional batch setting due to memory cons…
The Bandit Whisperer: Communication Learning for Restless Bandits
Yunfan Zhao, Tonghan Wang, Dheeraj Nagaraj +2
Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynam…
The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models
Saravanan Kandasamy, Dheeraj Nagaraj
Langevin Dynamics is a Stochastic Differential Equation (SDE) central to sampling and generative modeling and is implemented via time discretization. Langevin Monte Carlo (LMC), ba…
Glauber Generative Model: Discrete Diffusion Models via Binary Classification
Harshit Varma, Dheeraj Nagaraj, Karthikeyan Shanmugam
We introduce the Glauber Generative Model (GGM), a new class of discrete diffusion models, to obtain new samples from a distribution given samples from a discrete space. GGM deploy…
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Nikhil Behari, Edwin Zhang, Yunfan Zhao +3
Restless multi-armed bandits (RMAB) have demonstrated success in optimizing resource allocation for large beneficiary populations in public health settings. Unfortunately, RMAB mod…
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Yunfan Zhao, Nikhil Behari, Edward Hughes +5
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently…
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Dheeraj Baby, Aniket Das, Dheeraj Nagaraj +1
We consider the problem of heteroscedastic linear regression, where, given samples from $y_i = \langle \mathbf{w}^{*}, \mathbf{x}_i \rangle + ε_i \cdot \l…
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
Ramnath Kumar, Kushal Majmundar, Dheeraj Nagaraj +1
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample re-weighting. Leveraging…
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Aniket Das, Dheeraj Nagaraj
Stein Variational Gradient Descent (SVGD) is a popular variational inference algorithm which simulates an interacting particle system to approximately sample from a target distribu…
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits
Abheek Ghosh, Dheeraj Nagaraj, Manish Jain +1
We study the problem of planning restless multi-armed bandits (RMABs) with multiple actions. This is a popular model for multi-agent systems with applications like multi-channel co…
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
Gandharv Patil, Prashanth L. A., Dheeraj Nagaraj +1
We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm when combined with tail-averaging. We derive finite time bounds on the parameter error…
Multi-User Reinforcement Learning with Low Rank Rewards
Naman Agarwal, Prateek Jain, Suhas Kowshik +2
In this work, we consider the problem of collaborative multi-user reinforcement learning. In this setting there are multiple users with the same state-action space and transition p…
Metastable Mixing of Markov Chains: Efficiently Sampling Low Temperature Exponential Random Graphs
Guy Bresler, Dheeraj Nagaraj, Eshaan Nichani
In this paper we consider the problem of sampling from the low-temperature exponential random graph model (ERGM). The usual approach is via Markov chain Monte Carlo, but Bhamidi et…
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
Aniket Das, Dheeraj Nagaraj, Anant Raj
We consider stochastic approximations of sampling algorithms, such as Stochastic Gradient Langevin Dynamics (SGLD) and the Random Batch Method (RBM) for Interacting Particle Dynamc…
Introspective Experience Replay: Look Back When Surprised
Ramnath Kumar, Dheeraj Nagaraj
In reinforcement learning (RL), experience replay-based sampling techniques play a crucial role in promoting convergence by eliminating spurious correlations. However, widely used…
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Naman Agarwal, Syomantak Chaudhuri, Prateek Jain +2
Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical re…
The staircase property: How hierarchical structure can guide deep learning
Emmanuel Abbe, Enric Boix-Adsera, Matthew Brennan +2
This paper identifies a structural property of data distributions that enables deep neural networks to learn hierarchically. We define the "staircase" property for functions over t…
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
Prateek Jain, Suhas S Kowshik, Dheeraj Nagaraj +1
We consider the setting of vector valued non-linear dynamical systems , where is unbiased noise and is a known link…