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09/07/2023
Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck
This work investigates the nuanced algorithm design choices for deep lea...
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07/18/2023
Scaling Laws for Imitation Learning in NetHack
Imitation Learning (IL) is one of the most widely used methods in machin...
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06/14/2023
Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning
The success of SGD in deep learning has been ascribed by prior works to ...
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05/30/2023
AdANNS: A Framework for Adaptive Semantic Search
Web-scale search systems learn an encoder to embed a given query which i...
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05/18/2023
Modified Gauss-Newton Algorithms under Noise
Gauss-Newton methods and their stochastic version have been widely used ...
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02/21/2023
Provable Copyright Protection for Generative Models
There is a growing concern that learned conditional generative models ma...
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09/01/2022
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
Neural Networks (NNs) struggle to efficiently learn certain problems, su...
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07/18/2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
There is mounting empirical evidence of emergent phenomena in the capabi...
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05/26/2022
Matryoshka Representations for Adaptive Deployment
Learned representations are a central component in modern ML systems, se...
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03/08/2022
A Sharp Characterization of Linear Estimators for Offline Policy Evaluation
Offline policy evaluation is a fundamental statistical problem in reinfo...
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02/28/2022
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Contrastive learning is a popular form of self-supervised learning that ...
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01/04/2022
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Text adventure games present unique challenges to reinforcement learning...
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10/21/2021
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Intrinsic rewards play a central role in handling the exploration-exploi...
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10/19/2021
Inductive Biases and Variable Creation in Self-Attention Mechanisms
Self-attention, an architectural motif designed to model long-range inte...
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10/12/2021
Sparsity in Partially Controllable Linear Systems
A fundamental concept in control theory is that of controllability, wher...
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06/30/2021
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning
Most modern reinforcement learning algorithms optimize a cumulative sing...
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06/17/2021
Gone Fishing: Neural Active Learning with Fisher Embeddings
There is an increasing need for effective active learning algorithms tha...
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06/02/2021
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Learning binary representations of instances and classes is a classical ...
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02/18/2021
Robust and Differentially Private Mean Estimation
Differential privacy has emerged as a standard requirement in a variety ...
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10/12/2020
How Important is the Train-Validation Split in Meta-Learning?
Meta-learning aims to perform fast adaptation on a new task through lear...
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07/16/2020
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Direct policy gradient methods for reinforcement learning are a successf...
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06/22/2020
Information Theoretic Regret Bounds for Online Nonlinear Control
This work studies the problem of sequential control in an unknown, nonli...
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06/18/2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
In order to deal with the curse of dimensionality in reinforcement learn...
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06/17/2020
Robust Meta-learning for Mixed Linear Regression with Small Batches
A common challenge faced in practical supervised learning, such as medic...
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04/07/2020
PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
The global health threat from COVID-19 has been controlled in a number o...
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03/04/2020
Optimal Regularization Can Mitigate Double Descent
Recent empirical and theoretical studies have shown that many learning a...
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02/28/2020
The Implicit and Explicit Regularization Effects of Dropout
Dropout is a widely-used regularization technique, often required to obt...
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02/24/2020
Provable Representation Learning for Imitation Learning via Bi-level Optimization
A common strategy in modern learning systems is to learn a representatio...
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02/20/2020
Meta-learning for mixed linear regression
In modern supervised learning, there are a large number of tasks, but ma...
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02/08/2020
Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...
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09/10/2019
Meta-Learning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn...
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06/10/2019
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes
This work considers the sample complexity of obtaining an ϵ-optimal poli...
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02/22/2019
Online Meta-Learning
A central capability of intelligent systems is the ability to continuous...
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11/05/2018
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
We propose a plan online and learn offline (POLO) framework for the sett...
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09/23/2018
Provably Correct Automatic Subdifferentiation for Qualified Programs
The Cheap Gradient Principle (Griewank 2008) --- the computational cost ...
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04/20/2018
Stochastic subgradient method converges on tame functions
This work considers the question: what convergence guarantees does the s...
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03/20/2018
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Policy gradient methods have enjoyed great success in deep reinforcement...
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02/26/2018
Variance Reduction Methods for Sublinear Reinforcement Learning
This work considers the problem of provably optimal reinforcement learni...
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11/22/2017
Leverage Score Sampling for Faster Accelerated Regression and ERM
Given a matrix A∈R^n× d and a vector b ∈R^d, we show how to compute an ϵ...
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11/07/2017
Learning Overcomplete HMMs
We study the problem of learning overcomplete HMMs---those that have man...
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12/08/2016
Prediction with a Short Memory
We consider the problem of predicting the next observation given a seque...
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06/08/2015
Convergence Rates of Active Learning for Maximum Likelihood Estimation
An active learner is given a class of models, a large set of unlabeled e...
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02/13/2015
A Linear Dynamical System Model for Text
Low dimensional representations of words allow accurate NLP models to be...
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08/13/2013
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Overcomplete latent representations have been very popular for unsupervi...
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02/20/2012
(weak) Calibration is Computationally Hard
We show that the existence of a computationally efficient calibration al...
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10/19/2011
An Optimal Algorithm for Linear Bandits
We provide the first algorithm for online bandit linear optimization who...
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04/11/2011
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide ...
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02/27/2010