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06/19/2023
Guiding Language Models of Code with Global Context using Monitors
Language models of code (LMs) work well when the surrounding code in the...
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06/08/2023
In-Context Learning through the Bayesian Prism
In-context learning is one of the surprising and useful features of larg...
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03/14/2023
A Theory of Emergent In-Context Learning as Implicit Structure Induction
Scaling large language models (LLMs) leads to an emergent capacity to le...
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11/14/2022
Towards a Mathematics Formalisation Assistant using Large Language Models
Mathematics formalisation is the task of writing mathematics (i.e., defi...
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10/23/2022
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks
Humans can reason compositionally whilst grounding language utterances t...
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03/14/2022
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
Compositional generalization is a fundamental trait in humans, allowing ...
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06/19/2021
Learning and Generalization in Overparameterized Normalizing Flows
In supervised learning, it is known that overparameterized neural networ...
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05/31/2021
Learning and Generalization in RNNs
Simple recurrent neural networks (RNNs) and their more advanced cousins ...
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04/29/2021
Analyzing the Nuances of Transformers' Polynomial Simplification Abilities
Symbolic Mathematical tasks such as integration often require multiple w...
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03/12/2021
Are NLP Models really able to Solve Simple Math Word Problems?
The problem of designing NLP solvers for math word problems (MWP) has se...
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11/08/2020
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
While recurrent models have been effective in NLP tasks, their performan...
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09/23/2020
On the Ability of Self-Attention Networks to Recognize Counter Languages
Transformers have supplanted recurrent models in a large number of NLP t...
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07/14/2020
Robust Identifiability in Linear Structural Equation Models of Causal Inference
In this work, we consider the problem of robust parameter estimation fro...
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06/16/2020
On the Computational Power of Transformers and Its Implications in Sequence Modeling
Transformers are being used extensively across several sequence modeling...
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10/21/2019
Non-Gaussianity of Stochastic Gradient Noise
What enables Stochastic Gradient Descent (SGD) to achieve better general...
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08/16/2019
Effect of Activation Functions on the Training of Overparametrized Neural Nets
It is well-known that overparametrized neural networks trained using gra...
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07/24/2019
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature
The Euclidean space notion of convex sets (and functions) generalizes to...
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05/16/2019
Stability of Linear Structural Equation Models of Causal Inference
We consider the numerical stability of the parameter recovery problem in...
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07/13/2018
Non-Gaussian Component Analysis using Entropy Methods
Non-Gaussian component analysis (NGCA) is a problem in multidimensional ...
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02/22/2017
Heavy-Tailed Analogues of the Covariance Matrix for ICA
Independent Component Analysis (ICA) is the problem of learning a square...
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09/02/2015
Heavy-tailed Independent Component Analysis
Independent component analysis (ICA) is the problem of efficiently recov...
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11/12/2013
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
In this paper we show that very large mixtures of Gaussians are efficien...
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06/25/2013
Fourier PCA and Robust Tensor Decomposition
Fourier PCA is Principal Component Analysis of a matrix obtained from hi...
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11/09/2012
Efficient learning of simplices
We show an efficient algorithm for the following problem: Given uniforml...
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09/15/2012
Further Optimal Regret Bounds for Thompson Sampling
Thompson Sampling is one of the oldest heuristics for multi-armed bandit...
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09/15/2012
Thompson Sampling for Contextual Bandits with Linear Payoffs
Thompson Sampling is one of the oldest heuristics for multi-armed bandit...
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06/07/2005
