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Found 15 papers, 3 papers with code
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
Learning from many trajectories
Specifically, we establish that the worst-case error rate of this problem is $\Theta(n / m T)$ whenever $m \gtrsim n$.
Efficient and Modular Implicit Differentiation
In this paper, we propose automatic implicit differentiation, an efficient and modular approach for implicit differentiation of optimization problems.
Decomposing reverse-mode automatic differentiation
We decompose reverse-mode automatic differentiation into (forward-mode) linearization followed by transposition.
The advantages of multiple classes for reducing overfitting from test set reuse
We show a new upper bound of $\tilde O(\max\{\sqrt{k\log(n)/(mn)}, k/n\})$ on the worst-case bias that any attack can achieve in a prediction problem with $m$ classes.
Measuring the Effects of Data Parallelism on Neural Network Training
Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes.
Random Features for Compositional Kernels
We describe and analyze a simple random feature scheme (RFS) from prescribed compositional kernels.
Estimation from Indirect Supervision with Linear Moments
In structured prediction problems where we have indirect supervision of the output, maximum marginal likelihood faces two computational obstacles: non-convexity of the objective and intractability of even a single gradient computation.
Principal Component Projection Without Principal Component Analysis
To achieve our results, we first observe that ridge regression can be used to obtain a "smooth projection" onto the top principal components.
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning.
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
We develop a family of accelerated stochastic algorithms that minimize sums of convex functions.
Competing with the Empirical Risk Minimizer in a Single Pass
In the absence of computational constraints, the minimizer of a sample average of observed data -- commonly referred to as either the empirical risk minimizer (ERM) or the $M$-estimator -- is widely regarded as the estimation strategy of choice due to its desirable statistical convergence properties.
Simple MAP Inference via Low-Rank Relaxations
We focus on the problem of maximum a posteriori (MAP) inference in Markov random fields with binary variables and pairwise interactions.
Relaxations for inference in restricted Boltzmann machines
We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field.
