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Found 23 papers, 7 papers with code
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Moreover, we extend our analysis to the approximate optimization setting and derive exponentially decaying convergence rates for both RLHF and DPO.
Informativeness of Reward Functions in Reinforcement Learning
Reward functions are central in specifying the task we want a reinforcement learning agent to perform.
Corruption Robust Offline Reinforcement Learning with Human Feedback
We aim to design algorithms that identify a near-optimal policy from the corrupted data, with provable guarantees.
Proximal Curriculum for Reinforcement Learning Agents
We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings.
Learning Personalized Decision Support Policies
In this work, we propose learning a decision support policy that, for a given input, chooses which form of support, if any, to provide.
Robust Learning from Observation with Model Misspecification
Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult.
Explicable Reward Design for Reinforcement Learning Agents
By being explicable, we seek to capture two properties: (a) informativeness so that the rewards speed up the agent's convergence, and (b) sparseness as a proxy for ease of interpretability of the rewards.
Provable Defense Against Clustering Attacks on 3D Point Clouds
Lately, the literature on adversarial robustness spans from images to other domains such as point clouds.
Curriculum Design for Teaching via Demonstrations: Theory and Applications
In particular, we study how to design a personalized curriculum over demonstrations to speed up the learner's convergence.
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner.
Interaction-limited Inverse Reinforcement Learning
This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training.
Environment Shaping in Reinforcement Learning using State Abstraction
However, the applicability of potential-based reward shaping is limited in settings where (i) the state space is very large, and it is challenging to compute an appropriate potential function, (ii) the feedback signals are noisy, and even with shaped rewards the agent could be trapped in local optima, and (iii) changing the rewards alone is not sufficient, and effective shaping requires changing the dynamics.
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents.
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and cooperate.
Distributed Optimization
Multi-agent Reinforcement Learning
+2
Interactive Teaching Algorithms for Inverse Reinforcement Learning
We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher.
Iterative Classroom Teaching
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students.
Not All Attributes are Created Equal: $d_{\mathcal{X}}$-Private Mechanisms for Linear Queries
However, it provides the same level of protection for all elements (individuals and attributes) in the data.
Transitions, Losses, and Re-parameterizations: Elements of Prediction Games
This thesis presents some geometric insights into three different types of two player prediction games -- namely general learning task, prediction with expert advice, and online convex optimization.
Exp-Concavity of Proper Composite Losses
In special cases like the Aggregating Algorithm (\cite{vovk1995game}) with mixable losses and the Weighted Average Algorithm (\cite{kivinen1999averaging}) with exp-concave losses, it is possible to achieve $O(1)$ regret bounds.
Minimax Lower Bounds for Cost Sensitive Classification
The cost-sensitive classification problem plays a crucial role in mission-critical machine learning applications, and differs with traditional classification by taking the misclassification costs into consideration.
Consistent Robust Regression
We present the first efficient and provably consistent estimator for the robust regression problem.
Improved Optimistic Mirror Descent for Sparsity and Curvature
Online Convex Optimization plays a key role in large scale machine learning.
Efficient and Consistent Robust Time Series Analysis
We illustrate our methods on synthetic datasets and show that our methods indeed are able to consistently recover the optimal parameters despite a large fraction of points being corrupted.
