Research Papers

A repository of all the research papers I read

Sem 5

Track 1: Honours Exploration

Towards a formalization of Teamwork with Resource constraints (implementation)
Constructing Optimal Policies for Agents with Constrained Architectures
Coordinating occupant behavior for building energy and comfort management using multi-agent systems
Towards adjustable autonomy for the real world
An introduction to Reinforcement Learning (textbook)- Chapters 1-6
Dynamic Preferences in Multi Criteria Reinforcement Learning - (implementation)
The Malmo Platform for Artificial Intelligence Experimentation - tool
A Comprehensive Survey on Safe Reinforcement Learning - in brief
HogRider: Champion Agent of Microsoft Malmo Collaborative AI Challenge (implementation)

Track 2: Multi-Agent Systems

Heuristic for Multiagent Reinforcement Learning in Decentralized Decision Problems
Point-based Dynamic Programming for Dec-POMDPs
Taming Decentralized POMDPs: Towards Efficient Policy Computation for Multiagent Settings

Apart from these papers, I have also invested time in watching tutorials on RL and in general, developing a better understanding of RL, by looking at various other applications and papers. I haave also explored DQNs to a certain degree.

Sem 6 (To be updated)

Track 1: Negotiation

Optimal non-adaptive Concession strategies with Incomplete Information
An Adaptive Bilateral Negotiation Model based on Bayesian Learning

Track 2: Reinforcement Learning

[book] Reinforcement Learning: An Introduction

Track 3: Decentralized MDPs

Solving Transition Independent Decentralized Markov Decision Processes
Planning and Learning For Decentralized MDPs With Event Driven Rewards

Track 4: Multi Agent Systems

Sem 7

Track 1: Power TAC

The 2018 Power Trading Agent Competition
[to read] TacTex’13: A Champion Adaptive Power Trading Agent
[to read] Autonomous Electricity Trading using Time-Of-Use Tariffs in a Competitive Market
[to read] An MDP-Based Winning Approach to Autonomous Power Trading: Formalization and Empirical Analysis
[to read] AgentUDE17: Imbalance Management of a Retailer Agent to Exploit Balancing Market Incentives in a Smart Grid Ecosystem
[to read] Fixed-price Tariff Generation Using Reinforcement Learning

Track 2: Negotiation & LSTM

Deal or No Deal? End-to-End Learning for Negotiation Dialogues (Facebook AI Research + code)
[brief] DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
The Negotiation Dance: Time, Culture, and Behavioral Sequences in Negotiation
[brief] “I think it might help if we multiply, and not add” : Detecting Indirectness in Conversation
Long Short Term Memory for Driver Intent Prediction

Track 3: Privacy Preserving Machine Learning

[book] The Algorithmic Foundations of Differential Privacy
The Johnson-Lindenstrauss Transform itself preserves Differential Privacy
GELU-Net: A Globally Encrypted, Locally Unencrypted Deep Neural Network for Privacy-Preserved Learning
Secure Face Matching Using Fully Homomorphic Encryption
Deep Learning with Differential Privacy
Analyze Gauss: Optimal Bounds for Privacy-Preserving Principal Component Analysis
Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining
Interactive Privacy via the Median Mechanism

Track 4: Reinforcement Learning

Track 5: Blockchain

Bitcoin: A Peer-to-Peer Electronic Cash System
Speed-Security Tradeos in Blockchain Protocols (Reading Assignment)

Track 6: General

How to Read a paper
[finish] The PhD Grind (book)
Apart from these papers, I picked up a decent background in Time Series Forecasting.

Track 7: Digital Image Processing

LaTeX Generation from Printed Equations
Geometric Feature Points Based Optical Character Recognition
Visual Pattern Recognition by Moment Invariants

To read list:

Human-level control through deep reinforcement learning
Playing Atari with Deep Reinforcement Learning
Deep Reinforcement Learning with Double Q-Learning
[low] Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods (Partha Pratim)
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
Learning Purposeful Behaviour in the Absence of Rewards
AAAI 2018 Notes
[lecture] Adversarial Machine Learning: Ian Goodfellow
Solving the Rubik’s Cube Without Human Knowledge

Sem 8:

Game Theory:

Summer 2019:

Privacy track

RL track

PowerTAC

Fifth year:

Privacy preserving ML

Differentially private JL transform

Privacy in PCA

Analyze Gauss (Cynthia Dwork)

General

Statistical foundations of virtual democracy

Monte Carlo

Demand Response

Reinforcement Learning

Computer Vision