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07/17/2023
Zero-th Order Algorithm for Softmax Attention Optimization
Large language models (LLMs) have brought about significant transformati...
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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. ...
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03/29/2023
An Over-parameterized Exponential Regression
Over the past few years, there has been a significant amount of research...
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12/18/2022
A Layered Architecture for Universal Causality
We propose a layered hierarchical architecture called UCLA (Universal Ca...
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11/03/2022
Privacy Aware Experiments without Cookies
Consider two brands that want to jointly test alternate web experiences ...
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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 ...
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08/23/2022
Categoroids: Universal Conditional Independence
Conditional independence has been widely used in AI, causal inference, m...
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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...
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04/23/2022
Smoothed Online Combinatorial Optimization Using Imperfect Predictions
Smoothed online combinatorial optimization considers a learner who repea...
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10/28/2021
Universal Decision Models
Humans are universal decision makers: we reason causally to understand t...
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09/20/2021
Causal Inference in Network Economics
Network economics is the study of a rich class of equilibrium problems t...
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09/20/2021
Asymptotic Causal Inference
We investigate causal inference in the asymptotic regime as the number o...
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09/19/2021
Multiscale Manifold Warping
Many real-world applications require aligning two temporal sequences, in...
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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...
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06/06/2020
Regularized Off-Policy TD-Learning
We present a novel l_1 regularized off-policy convergent TD-learning met...
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05/17/2020
Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption th...
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08/04/2018
Global Convergence to the Equilibrium of GANs using Variational Inequalities
In optimization, the negative gradient of a function denotes the directi...
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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...
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10/19/2017
Online Monotone Games
Algorithmic game theory (AGT) focuses on the design and analysis of algo...
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11/05/2016
Generative Multi-Adversarial Networks
Generative adversarial networks (GANs) are a framework for producing a g...
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08/21/2016
Inverting Variational Autoencoders for Improved Generative Accuracy
Recent advances in semi-supervised learning with deep generative models ...
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06/15/2016
Deep Reinforcement Learning With Macro-Actions
Deep reinforcement learning has been shown to be a powerful framework fo...
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07/28/2015
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds
Recent work has explored methods for learning continuous vector space wo...
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01/10/2013
Decision-Theoretic Planning with Concurrent Temporally Extended Actions
We investigate a model for planning under uncertainty with temporallyext...
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10/16/2012
Sparse Q-learning with Mirror Descent
This paper explores a new framework for reinforcement learning based on ...
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07/04/2012