227 citations · 246 across the 47 of their papers we have counts for
16 papers · 1 filter
Learning Multiple Initial Solutions to Optimization Problems
Elad Sharony, Heng Yang, Tong Che +3
Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications, such as robot control, autonomous driving, and portfolio man…
Improving Inverse Folding for Peptide Design with Diversity-regularized Direct Preference Optimization
Ryan Park, Darren J. Hsu, C. Brian Roland +5
Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures. Models like ProteinMPNN, a m…
On the Convergence of Single-Timescale Actor-Critic
Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi +2
We analyze the global convergence of the single-timescale actor-critic (AC) algorithm for the infinite-horizon discounted Markov Decision Processes (MDPs) with finite state spaces.…
Dual Pricing to Prioritize Renewable Energy and Consumer Preferences in Electricity Markets
Emilie Jong, Samuel Chevalier, Spyros Chatzivasileiadis +1
Electricity markets currently fail to incorporate preferences of buyers, treating polluting and renewable energy sources as having equal social benefit under a system of uniform cl…
Efficient Fairness-Performance Pareto Front Computation
Mark Kozdoba, Binyamin Perets, Shie Mannor
There is a well known intrinsic trade-off between the fairness of a representation and the performance of classifiers derived from the representation. Due to the complexity of opti…
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis
Guy Lutsker, Gal Sapir, Smadar Shilo +11
Recent advances in SSL enabled novel medical AI models, known as foundation models, offer great potential for better characterizing health from diverse biomedical data. CGM provide…
PlaMo: Plan and Move in Rich 3D Physical Environments
Assaf Hallak, Gal Dalal, Chen Tessler +3
Controlling humanoids in complex physically simulated worlds is a long-standing challenge with numerous applications in gaming, simulation, and visual content creation. In our setu…
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
Jeongyeol Kwon, Shie Mannor, Constantine Caramanis +1
In many real-world decision problems there is partially observed, hidden or latent information that remains fixed throughout an interaction. Such decision problems can be modeled a…
On Bits and Bandits: Quantifying the Regret-Information Trade-off
Itai Shufaro, Nadav Merlis, Nir Weinberger +1
In many sequential decision problems, an agent performs a repeated task. He then suffers regret and obtains information that he may use in the following rounds. However, sometimes…
Tree Search-Based Policy Optimization under Stochastic Execution Delay
David Valensi, Esther Derman, Shie Mannor +1
The standard formulation of Markov decision processes (MDPs) assumes that the agent's decisions are executed immediately. However, in numerous realistic applications such as roboti…
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes
Navdeep Kumar, Yashaswini Murthy, Itai Shufaro +3
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs). Specifically, we…
Conservative DDPG -- Pessimistic RL without Ensemble
Nitsan Soffair, Shie Mannor
DDPG is hindered by the overestimation bias problem, wherein its -estimates tend to overstate the actual -values. Traditional solutions to this bias involve ensemble-based me…
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
Yihan Du, Anna Winnicki, Gal Dalal +2
Reinforcement Learning from Human Feedback (RLHF) has achieved impressive empirical successes while relying on a small amount of human feedback. However, there is limited theoretic…
MinMaxMin -learning
Nitsan Soffair, Shie Mannor
MinMaxMin -learning is a novel optimistic Actor-Critic algorithm that addresses the problem of overestimation bias (-estimations are overestimating the real -values) inher…
SQT -- std -target
Nitsan Soffair, Dotan Di-Castro, Orly Avner +1
Std -target is a conservative, actor-critic, ensemble, -learning-based algorithm, which is based on a single key -formula: -networks standard deviation, which is an "un…
Improving Token-Based World Models with Parallel Observation Prediction
Lior Cohen, Kaixin Wang, Bingyi Kang +1
Motivated by the success of Transformers when applied to sequences of discrete symbols, token-based world models (TBWMs) were recently proposed as sample-efficient methods. In TBWM…