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Found 15 papers, 8 papers with code
The Virtues of Pessimism in Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations.
Accelerating Inverse Reinforcement Learning with Expert Bootstrapping
Existing inverse reinforcement learning methods (e. g. MaxEntIRL, $f$-IRL) search over candidate reward functions and solve a reinforcement learning problem in the inner loop.
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.
CryptOpt: Automatic Optimization of Straightline Code
Manual engineering of high-performance implementations typically consumes many resources and requires in-depth knowledge of the hardware.
Robust Risk-Aware Option Hedging
The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies.
Improving Chess Commentaries by Combining Language Models with Symbolic Reasoning Engines
Despite many recent advancements in language modeling, state-of-the-art language models lack grounding in the real world and struggle with tasks involving complex reasoning.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
CryptOpt: Verified Compilation with Randomized Program Search for Cryptographic Primitives (full version)
Most software domains rely on compilers to translate high-level code to multiple different machine languages, with performance not too much worse than what developers would have the patience to write directly in assembly language.
Self-Explaining Deviations for Coordination
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world.
$AIR^2$ for Interaction Prediction
The 2021 Waymo Interaction Prediction Challenge introduced a problem of predicting the future trajectories and confidences of two interacting agents jointly.
QK Iteration: A Self-Supervised Representation Learning Algorithm for Image Similarity
Previous work in contrastive self-supervised learning has identified the importance of being able to optimize representations while ``pushing'' against a large number of negative examples.
No-Press Diplomacy from Scratch
Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data.
Likelihood-based estimation and prediction for a measles outbreak in Samoa
Stochastic models can help with misspecification but are even more expensive to simulate and perform inference with.
Off-Belief Learning
Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming
We present a convex cone program to infer the latent probability matrix of a random dot product graph (RDPG).
