parshakova / GAMS-for-Data-Efficient-Learning

Global Autoregressive Models (GAMs) for Data-Efficient Sequence Learning

Global Autoregressive Models for Data-Efficient Sequence Learning.

Tetiana Parshakova, Jean-Marc Andreoli and Marc Dymetman. CONLL, Hong Kong. Nov. 2019

Distributional Reinforcement Learning for Energy-Based Sequential Models.

Tetiana Parshakova, Jean-Marc Andreoli and Marc Dymetman. Optimization Foundations for Reinforcement Learning Workshop at NeurIPS, Vancouver. Dec. 2019

CONLL

1. Make true data:
   • Call wfsa_n_z.py to create D,V,T datasets with a particular motif and process
   • motif PFSA: python wfsa_n_z.py -prob_0 0.5 -length 30 -motif 1011100111001 -data_target ./data/pfsa_30_1011100111001 -valid 2000 -test 5000 -train 20000
   • motif-anti-motif PFSA: python wfsa_m.py -prob_0 0.5 -length 30 -motif 10001011111000 -second_select_prob 0.1 -second_length 30 -second_prob_0 0.5 -second_anti_motif 10001011111000 -second_selector_bit_remove -data_target ./data/pfsa_30_10001011111000.10001011111000 -valid 2000 -test 5000 -train 20000
2. Train r -> P_\lambda -> distillation (cyclic or no) -> \pi_\theta
   • call cycle_r_plambda_pitheta.py with needed flags
   • e.g., python -u cycle_r_plambda_pitheta.py --n 30 --ds_size 5000 --motif 4 --feat '1001111' --train 'snis_r' --mtype 'm' --restore yes --distill_size 20000 --cyclic
   • or using slurm: call pa_slurm_cycle_r_lambda_pi, e.g. sbatch --array=0 pa_slurm_cycle_r_lambda_pi
3. Analyze the performace with Jupyter Notebook:
   • jupyter notebook or remotely jupyter notebook --ip='0.0.0.0' --no-browser --port 8889
   • plot_conll_f12.ipynb.ipynb

OptRL at NeurIPS

1. Make true data:
   • Call wfsa_n_z.py to create D,V,T datasets with a particular motif and process
   • motif: python wfsa_n_z.py -prob_0 0.5 -length 30 -motif 1011100111001 -data_target ./data/pfsa_30_1011100111001 -valid 2000 -test 5000 -train 20000
2. Train r -> P_\lambda -> Distillation/D-PG/PG/AC D-PG -> \pi_\theta
   • connect to wandb optionally wandb login ...
   • call tuned_slurm_pg_r_plambda_pi e.g. sbatch --array=0 tuned_slurm_pg_r_plambda_pi
   • it executes r_plambda_pitheta_full.py with particular flags
3. Analyze the performace with Jupyter Notebook:
   • jupyter notebook --ip='0.0.0.0' --no-browser --port 8889
   • dpg_distill_plot_bounds.ipynb