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08/30/2023
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation
Data valuation, a critical aspect of data-centric ML research, aims to q...
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08/17/2023
Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games
The problem of two-player zero-sum Markov games has recently attracted i...
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07/04/2023
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Convolutional residual neural networks (ConvResNets), though overparamet...
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06/30/2023
Provable Robust Watermarking for AI-Generated Text
As AI-generated text increasingly resembles human-written content, the a...
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06/24/2023
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Developing theoretical guarantees on the sample complexity of offline RL...
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06/12/2023
"Private Prediction Strikes Back!” Private Kernelized Nearest Neighbors with Individual Renyi Filter
Most existing approaches of differentially private (DP) machine learning...
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06/02/2023
Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats
Invisible watermarks safeguard images' copyrights by embedding hidden me...
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06/01/2023
Non-stationary Reinforcement Learning under General Function Approximation
General function approximation is a powerful tool to handle large state ...
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05/31/2023
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
This paper focuses on supervised and unsupervised online label shift, wh...
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03/06/2023
Improved Differentially Private Regression via Gradient Boosting
We revisit the problem of differentially private squared error linear re...
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02/26/2023
No-Regret Linear Bandits beyond Realizability
We study linear bandits when the underlying reward function is not linea...
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02/24/2023
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs
In many real-life reinforcement learning (RL) problems, deploying new po...
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02/06/2023
Protecting Language Generation Models via Invisible Watermarking
Language generation models have been an increasingly powerful enabler fo...
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12/31/2022
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy
The ”Propose-Test-Release” (PTR) framework is a classic recipe for desig...
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12/09/2022
Near-Optimal Differentially Private Reinforcement Learning
Motivated by personalized healthcare and other applications involving se...
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11/16/2022
Global Optimization with Parametric Function Approximation
We consider the problem of global optimization with noisy zeroth order o...
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10/07/2022
Distillation-Resistant Watermarking for Model Protection in NLP
How can we protect the intellectual property of trained NLP models? Mode...
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10/03/2022
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
Offline reinforcement learning, which aims at optimizing sequential deci...
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10/03/2022
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
We study the problem of deployment efficient reinforcement learning (RL)...
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09/30/2022
Differentially Private Optimization on Large Model at Small Cost
Differentially private (DP) optimization is the standard paradigm to lea...
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09/30/2022
Differentially Private Bias-Term only Fine-tuning of Foundation Models
We study the problem of differentially private (DP) fine-tuning of large...
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09/23/2022
Doubly Fair Dynamic Pricing
We study the problem of online dynamic pricing with two types of fairnes...
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06/18/2022
Optimal Dynamic Regret in LQR Control
We consider the problem of nonstochastic control with a sequence of quad...
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06/14/2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Per-example gradient clipping is a key algorithmic step that enables pra...
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06/13/2022
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks
Quantized neural networks have drawn a lot of attention as they reduce t...
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06/10/2022
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality
Goal-oriented Reinforcement Learning, where the agent needs to reach the...
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06/02/2022
Offline Reinforcement Learning with Differential Privacy
The offline reinforcement learning (RL) problem is often motivated by th...
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05/19/2022
Differentially Private Linear Sketches: Efficient Implementations and Applications
Linear sketches have been widely adopted to process fast data streams, a...
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05/04/2022
Second Order Path Variationals in Non-Stationary Online Learning
We consider the problem of universal dynamic regret minimization under e...
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05/04/2022
Provably Confidential Language Modelling
Large language models are shown to memorize privacy information such as ...
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04/20/2022
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?
We study the theory of neural network (NN) from the lens of classical no...
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03/30/2022
Towards Differential Relational Privacy and its use in Question Answering
Memorization of the relation between entities in a dataset can lead to p...
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03/30/2022
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
We provide an end-to-end Renyi DP based-framework for differentially pri...
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03/22/2022
Mixed Differential Privacy in Computer Vision
We introduce AdaMix, an adaptive differentially private algorithm for tr...
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03/11/2022
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Offline reinforcement learning, which seeks to utilize offline/historica...
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02/13/2022
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
We study the problem of reinforcement learning (RL) with low (policy) sw...
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01/27/2022
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise
In feature-based dynamic pricing, a seller sets appropriate prices for a...
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01/21/2022
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
We study the framework of universal dynamic regret minimization with str...
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12/29/2021
Multivariate Trend Filtering for Lattice Data
We study a multivariate version of trend filtering, called Kronecker tre...
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11/03/2021
Privately Publishable Per-instance Privacy
We consider how to privately share the personalized privacy losses incur...
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10/17/2021
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism
We study the offline reinforcement learning (offline RL) problem, where ...
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06/16/2021
Optimal Accounting of Differential Privacy via Characteristic Function
Characterizing the privacy degradation over compositions, i.e., privacy ...
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05/13/2021
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings
This work studies the statistical limits of uniform convergence for offl...
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04/23/2021
Optimal Dynamic Regret in Exp-Concave Online Learning
We consider the problem of the Zinkevich (2003)-style dynamic regret min...
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02/20/2021
Logarithmic Regret in Feature-based Dynamic Pricing
Feature-based dynamic pricing is an increasingly popular model of settin...
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02/07/2021
Non-stationary Online Learning with Memory and Non-stochastic Control
We study the problem of Online Convex Optimization (OCO) with memory, wh...
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02/02/2021
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
We consider the problem of offline reinforcement learning (RL) – a well-...
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01/23/2021
An Optimal Reduction of TV-Denoising to Adaptive Online Learning
We consider the problem of estimating a function from n noisy samples wh...
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11/06/2020
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
The Private Aggregation of Teacher Ensembles (PATE) framework is one of ...
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10/25/2020
