3 citations · 7 across the 10 of their papers we have counts for
10 papers
No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models
Changlong Wu, Ananth Grama, Wojciech Szpankowski
Generative models have shown impressive capabilities in synthesizing high-quality outputs across various domains. However, a persistent challenge is the occurrence of "hallucinatio…
Online Distribution Learning with Local Private Constraints
Jin Sima, Changlong Wu, Olgica Milenkovic +1
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy. Let be a distribution-valued fun…
Oracle-Efficient Hybrid Online Learning with Unknown Distribution
Changlong Wu, Jin Sima, Wojciech Szpankowski
We study the problem of oracle-efficient hybrid online learning when the features are generated by an unknown i.i.d. process and the labels are generated adversarially. Assuming ac…
Robust Online Classification: From Estimation to Denoising
Changlong Wu, Ananth Grama, Wojciech Szpankowski
We study online classification of features into labels with general hypothesis classes. In our setting, true labels are determined by some function within the hypothesis class but…
Online Learning in Dynamically Changing Environments
Changlong Wu, Ananth Grama, Wojciech Szpankowski
We study the problem of online learning and online regret minimization when samples are drawn from a general unknown non-stationary process. We introduce the concept of a dynamic c…
Expected Worst Case Regret via Stochastic Sequential Covering
Changlong Wu, Mohsen Heidari, Ananth Grama +1
We study the problem of sequential prediction and online minimax regret with stochastically generated features under a general loss function. We introduce a notion of expected wors…
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
Changlong Wu, Mohsen Heidari, Ananth Grama +1
We study the sequential general online regression, known also as the sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We f…
Prediction with eventual almost sure guarantees
Changlong Wu, Narayana Santhanam
We study the problem of sequentially predicting properties of a probabilistic model and its next outcome over an infinite horizon, with the goal of ensuring that the predictions in…
Almost Uniform Sampling From Neural Networks
Changlong Wu, Narayana Prasad Santhanam
Given a length sample from and a neural network with a fixed architecture with weights, neurons, linear threshold activation functions, and binary output…
Redundancy of unbounded memory Markov classes with continuity conditions
Changlong Wu, Maryam Hosseini, Narayana Santhanam
We study the redundancy of universally compressing strings generated by a binary Markov source without any bound on the memory. To better understand the connec…