research
∙
09/18/2023
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
Safe Reinforcement Learning (RL) aims to find a policy that achieves hig...
share
research
∙
09/07/2023
Noisy Computing of the 𝖮𝖱 and 𝖬𝖠𝖷 Functions
We consider the problem of computing a function of n variables using noi...
research
∙
06/21/2023
On the Optimal Bounds for Noisy Computing
We revisit the problem of computing with noisy information considered in...
research
∙
06/04/2023
Fine-Tuning Language Models with Advantage-Induced Policy Alignment
Reinforcement learning from human feedback (RLHF) has emerged as a relia...
research
∙
06/03/2023
On Optimal Caching and Model Multiplexing for Large Model Inference
Large Language Models (LLMs) and other large foundation models have achi...
research
∙
06/01/2023
Doubly Robust Self-Training
Self-training is an important technique for solving semi-supervised lear...
research
∙
05/19/2023
Online Learning in a Creator Economy
The creator economy has revolutionized the way individuals can profit th...
research
∙
02/12/2023
Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits
We consider the sequential decision-making problem where the mean outcom...
research
∙
01/30/2023
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a...
research
∙
01/27/2023
Online Learning in Stackelberg Games with an Omniscient Follower
We study the problem of online learning in a two-player decentralized co...
research
∙
01/26/2023
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
We provide a theoretical framework for Reinforcement Learning with Human...
research
∙
11/01/2022
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Offline reinforcement learning (RL), which refers to decision-making fro...
research
∙
11/01/2022
Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits
In the infinite-armed bandit problem, each arm's average reward is sampl...
research
∙
05/30/2022
Minimax Optimal Online Imitation Learning via Replay Estimation
Online imitation learning is the problem of how best to mimic expert dem...
research
∙
05/24/2022
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
We propose Byzantine-robust federated learning protocols with nearly opt...
research
∙
04/05/2022
Jump-Start Reinforcement Learning
Reinforcement learning (RL) provides a theoretical framework for continu...
research
∙
02/02/2022
Robust Estimation for Nonparametric Families via Generative Adversarial Networks
We provide a general framework for designing Generative Adversarial Netw...
research
∙
12/21/2021
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Policy optimization methods are one of the most widely used classes of R...
research
∙
07/08/2021
Computational Benefits of Intermediate Rewards for Hierarchical Planning
Many hierarchical reinforcement learning (RL) applications have empirica...
research
∙
06/18/2021
MADE: Exploration via Maximizing Deviation from Explored Regions
In online reinforcement learning (RL), efficient exploration remains par...
research
∙
06/07/2021
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Secure aggregation is a critical component in federated learning, which ...
research
∙
03/22/2021
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Offline (or batch) reinforcement learning (RL) algorithms seek to learn ...
research
∙
02/25/2021
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally
We study the statistical limits of Imitation Learning (IL) in episodic M...
research
∙
01/19/2021
Minimax Off-Policy Evaluation for Multi-Armed Bandits
We study the problem of off-policy evaluation in the multi-armed bandit ...
research
∙
01/12/2021
Linear Representation Meta-Reinforcement Learning for Instant Adaptation
This paper introduces Fast Linearized Adaptive Policy (FLAP), a new meta...
research
∙
10/12/2020
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
We present an efficient and practical (polynomial time) algorithm for on...
research
∙
09/13/2020
Toward the Fundamental Limits of Imitation Learning
Imitation learning (IL) aims to mimic the behavior of an expert policy i...
research
∙
05/28/2020
Robust estimation via generalized quasi-gradients
We explore why many recently proposed robust estimation problems are eff...
research
∙
01/21/2020
When does the Tukey median work?
We analyze the performance of the Tukey median estimator under total var...
research
∙
09/19/2019
Generalized Resilience and Robust Statistics
Robust statistics traditionally focuses on outliers, or perturbations in...
research
∙
09/18/2019
Barracuda: The Power of ℓ-polling in Proof-of-Stake Blockchains
A blockchain is a database of sequential events that is maintained by a ...
research
∙
01/27/2019
Deconstructing Generative Adversarial Networks
We deconstruct the performance of GANs into three components: 1. Formu...
research
∙
01/24/2019
Theoretically Principled Trade-off between Robustness and Accuracy
We identify a trade-off between robustness and accuracy that serves as a...
research
∙
11/19/2018
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
We study the problem of alleviating the instability issue in the GAN tra...
research
∙
09/18/2018
Concentration Inequalities for the Empirical Distribution
We study concentration inequalities for the Kullback--Leibler (KL) diver...
research
∙
05/02/2018
Minimax redundancy for Markov chains with large state space
For any Markov source, there exist universal codes whose normalized code...
research
∙
05/02/2018
Lower bounds on the minimax redundancy for Markov chains with large state space
For any Markov source, there exist universal codes whose normalized code...
research
∙
02/23/2018
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance
We present Local Moment Matching (LMM), a unified methodology for symmet...
research
∙
02/22/2018
Entropy Rate Estimation for Markov Chains with Large State Space
Estimating the entropy based on data is one of the prototypical problems...
research
∙
12/19/2017
Approximate Profile Maximum Likelihood
We propose an efficient algorithm for approximate computation of the pro...
research
∙
11/23/2017
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
We analyze the Kozachenko--Leonenko (KL) nearest neighbor estimator for ...
research
∙
11/06/2017
Optimal rates of entropy estimation over Lipschitz balls
We consider the problem of minimax estimation of the entropy of a densit...
research
∙
07/05/2017
Estimating the Fundamental Limits is Easier than Achieving the Fundamental Limits
We show through case studies that it is easier to estimate the fundament...
research
∙
11/03/2016
Demystifying ResNet
The Residual Network (ResNet), proposed in He et al. (2015), utilized sh...
research
∙
09/26/2014
Beyond Maximum Likelihood: from Theory to Practice
Maximum likelihood is the most widely used statistical estimation techni...
research
∙
07/29/2011
