Sridhar Mahadevan | DeepAI

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∙ 07/17/2023

Zero-th Order Algorithm for Softmax Attention Optimization

Large language models (LLMs) have brought about significant transformati...

0 Yichuan Deng, et al. ∙

∙ 04/10/2023

Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension

Large language models (LLMs) have shown their power in different areas. ...

0 Yichuan Deng, et al. ∙

∙ 03/29/2023

An Over-parameterized Exponential Regression

Over the past few years, there has been a significant amount of research...

0 Yeqi Gao, et al. ∙

∙ 12/18/2022

A Layered Architecture for Universal Causality

We propose a layered hierarchical architecture called UCLA (Universal Ca...

0 Sridhar Mahadevan, et al. ∙

∙ 11/03/2022

Privacy Aware Experiments without Cookies

Consider two brands that want to jointly test alternate web experiences ...

0 Shiv Shankar, et al. ∙

∙ 09/13/2022

Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory

We present a unified formalism for structure discovery of causal models ...

0 Sridhar Mahadevan, et al. ∙

∙ 08/23/2022

Categoroids: Universal Conditional Independence

Conditional independence has been widely used in AI, causal inference, m...

0 Sridhar Mahadevan, et al. ∙

∙ 07/06/2022

On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property

We propose Universal Causality, an overarching framework based on catego...

0 Sridhar Mahadevan, et al. ∙

∙ 04/23/2022

Smoothed Online Combinatorial Optimization Using Imperfect Predictions

Smoothed online combinatorial optimization considers a learner who repea...

0 Kai Wang, et al. ∙

∙ 10/28/2021

Universal Decision Models

Humans are universal decision makers: we reason causally to understand t...

0 Sridhar Mahadevan, et al. ∙

∙ 09/20/2021

Causal Inference in Network Economics

Network economics is the study of a rich class of equilibrium problems t...

0 Sridhar Mahadevan, et al. ∙

∙ 09/20/2021

Asymptotic Causal Inference

We investigate causal inference in the asymptotic regime as the number o...

0 Sridhar Mahadevan, et al. ∙

∙ 09/19/2021

Multiscale Manifold Warping

Many real-world applications require aligning two temporal sequences, in...

0 Sridhar Mahadevan, et al. ∙

∙ 06/06/2020

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity

In this paper, we introduce proximal gradient temporal difference learni...

0 Bo Liu, et al. ∙

∙ 06/06/2020

Regularized Off-Policy TD-Learning

We present a novel l_1 regularized off-policy convergent TD-learning met...

0 Bo Liu, et al. ∙

∙ 05/17/2020

Optimizing for the Future in Non-Stationary MDPs

Most reinforcement learning methods are based upon the key assumption th...

2 Yash Chandak, et al. ∙

∙ 08/04/2018

Global Convergence to the Equilibrium of GANs using Variational Inequalities

In optimization, the negative gradient of a function denotes the directi...

0 Ian Gemp, et al. ∙

∙ 04/28/2018

A Unified Framework for Domain Adaptation using Metric Learning on Manifolds

We present a novel framework for domain adaptation, whereby both geometr...

0 Sridhar Mahadevan, et al. ∙

∙ 10/19/2017

Online Monotone Games

Algorithmic game theory (AGT) focuses on the design and analysis of algo...

0 Ian Gemp, et al. ∙

∙ 11/05/2016

Generative Multi-Adversarial Networks

Generative adversarial networks (GANs) are a framework for producing a g...

0 Ishan Durugkar, et al. ∙

∙ 08/21/2016

Inverting Variational Autoencoders for Improved Generative Accuracy

Recent advances in semi-supervised learning with deep generative models ...

0 Ian Gemp, et al. ∙

∙ 06/15/2016

Deep Reinforcement Learning With Macro-Actions

Deep reinforcement learning has been shown to be a powerful framework fo...

0 Ishan P. Durugkar, et al. ∙

∙ 07/28/2015

Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds

Recent work has explored methods for learning continuous vector space wo...

0 Sridhar Mahadevan, et al. ∙

∙ 01/10/2013

Decision-Theoretic Planning with Concurrent Temporally Extended Actions

We investigate a model for planning under uncertainty with temporallyext...

0 Khashayar Rohanimanesh, et al. ∙

∙ 10/16/2012

Sparse Q-learning with Mirror Descent

This paper explores a new framework for reinforcement learning based on ...

0 Sridhar Mahadevan, et al. ∙

∙ 07/04/2012

Representation Policy Iteration

This paper addresses a fundamental issue central to approximation method...

0 Sridhar Mahadevan, et al. ∙

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