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07/20/2023
From Adaptive Query Release to Machine Unlearning
We formalize the problem of machine unlearning as design of efficient un...
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02/24/2023
Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards
We propose a novel offline reinforcement learning (RL) algorithm, namely...
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11/23/2022
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
Sample-efficient offline reinforcement learning (RL) with linear functio...
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08/19/2022
A Risk-Sensitive Approach to Policy Optimization
Standard deep reinforcement learning (DRL) aims to maximize expected rew...
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06/18/2022
Adversarial Robustness is at Odds with Lazy Training
Recent works show that random neural networks are vulnerable against adv...
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06/02/2022
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
We study the problem of approximating stationary points of Lipschitz and...
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05/06/2022
Differentially Private Generalized Linear Models Revisited
We study the problem of (ϵ,δ)-differentially private learning of linear ...
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02/25/2021
Machine Unlearning via Algorithmic Stability
We study the problem of machine unlearning and identify a notion of algo...
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10/23/2020
On Convergence and Generalization of Dropout Training
We study dropout in two-layer neural networks with rectified linear unit...
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10/22/2020
Adversarial Robustness of Supervised Sparse Coding
Several recent results provide theoretical insights into the phenomena o...
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07/15/2020
FetchSGD: Communication-Efficient Federated Learning with Sketching
Existing approaches to federated learning suffer from a communication bo...
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06/17/2020
Is Network the Bottleneck of Distributed Training?
Recently there has been a surge of research on improving the communicati...
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06/16/2020
Corralling Stochastic Bandit Algorithms
We study the problem of corralling stochastic bandit algorithms, that is...
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03/06/2020
Dropout: Explicit Forms and Capacity Control
We investigate the capacity control provided by dropout in various machi...
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02/22/2020
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
In this paper, we revisit the problem of private stochastic convex optim...
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12/30/2019
Multiview Representation Learning for a Union of Subspaces
Canonical correlation analysis (CCA) is a popular technique for learning...
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07/29/2019
Bandits with Feedback Graphs and Switching Costs
We study the adversarial multi-armed bandit problem where partial observ...
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05/28/2019
On Dropout and Nuclear Norm Regularization
We give a formal and complete characterization of the explicit regulariz...
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03/12/2019
Communication-efficient distributed SGD with Sketching
Large-scale distributed training of neural networks is often limited by ...
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11/21/2018
Learning from Multiview Correlations in Open-Domain Videos
An increasing number of datasets contain multiple views, such as video, ...
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11/09/2018
Policy Regret in Repeated Games
The notion of policy regret in online learning is a well defined? perfor...
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08/02/2018
Streaming Kernel PCA with Õ(√(n)) Random Features
We study the statistical and computational aspects of kernel principal c...
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06/26/2018
On the Implicit Bias of Dropout
Algorithmic approaches endow deep learning systems with implicit bias th...
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03/30/2018
Visual Robot Task Planning
Prospection, the act of predicting the consequences of many possible fut...
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11/08/2017
Learning to Imagine Manipulation Goals for Robot Task Planning
Prospection is an important part of how humans come up with new task pla...
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02/22/2017
Stochastic Approximation for Canonical Correlation Analysis
We study canonical correlation analysis (CCA) as a stochastic optimizati...
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02/08/2017
Deep Generalized Canonical Correlation Analysis
We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a ...
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12/29/2016
Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization
We propose a general theory for studying the geometry of nonconvex objec...
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11/04/2016
Understanding Deep Neural Networks with Rectified Linear Units
In this paper we investigate the family of functions representable by de...
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07/10/2016
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
The cyclic block coordinate descent-type (CBCD-type) methods, which perf...
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06/29/2016
Disease Trajectory Maps
Medical researchers are coming to appreciate that many diseases are in f...
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05/25/2016
A First Order Free Lunch for SQRT-Lasso
Many statistical machine learning techniques sacrifice convenient comput...
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05/09/2016
Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
We propose a stochastic variance reduced optimization algorithm for solv...
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04/02/2016
Embedding Lexical Features via Low-Rank Tensors
Modern NLP models rely heavily on engineered features, which often combi...
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07/05/2013
Stochastic Optimization of PCA with Capped MSG
We study PCA as a stochastic optimization problem and propose a novel st...
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06/27/2012
